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METHODS AND SYS IBMS FOR THE INDUSTRIAL INTERNET OF THINGS
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application claims the benefit of U.S. Provisional Pat. App. No.
62/584,099 (STRF-0020-P01), filed 9 November
2017, entitled "Methods and Systems for the Industrial Internet of Things".
[0002] This application also is a continuation-in-part of U.S. Non-Provisional
Pat. App. No. 15/859,238 (STRF-0022-U01), filed
29 December 2017, published on 5 July 2018 as US 2008/0188714, and entitled
"Methods and Systems for the Industrial Internet
of Things". US Non-Provisional Pat. App. No. 15/859,238 is a bypass
continuation of International Pat. App. No. PCT/US17/31721
(STRF-0001-W0), filed on 9 May 2017, published on 16 November 2017 as WO
2017/196821, and entitled "Methods and Systems
for the Industrial Internet of Things". International Pat. App. No.
PCT/US17/31721 claims the benefit of of U.S. Provisional Pat.
App. No. 62/333,589 (STRF-0001-P01), filed 9 May 2016, entitled "Strong Force
Industrial IoT Matrix"; U.S. Provisional Pat.
App. No. 62/350,672 (STRF-0001-P02), filed 15 June 2016, entitled "Strategy
for High Sampling Rate Digital Recording of
Measurement Waveform Data as Part of an Automated Sequential List that Streams
Long-Duration and Gap-Free Waveform Data
to Storage for more flexible Post-Processing"; U.S. Provisional Pat. App. No.
62/412,843 (STRF-0001-P03), filed 26 October 2016,
entitled "Methods and Systems for the Industrial Internet of Things"; and U.S.
Provisional Pat. App. No. 62/427,141 (STRF-0001-
PO4), filed 28 November 2016, entitled "Methods and Systems for the Industrial
Internet of Things".
[0003] All of the above applications are hereby incorporated by reference in
their entirety.
BACKGROUND
1. Field
[0004] The present disclosure relates to methods and systems for data
collection in industrial environments, as well as methods
and systems for leveraging collected data for monitoring, remote control,
autonomous action, and other activities in industrial
environments.
2. Description of the Related Art
[0005] Heavy industrial environments, such as environments for large scale
manufacturing (such as of aircraft, ships, trucks,
automobiles, and large industrial machines), energy production environments
(such as oil and gas plants, renewable energy
environments, and others), energy extraction environments (such as mining,
drilling, and the like), construction environments (such
as for construction of large buildings), and others, involve highly complex
machines, devices and systems and highly complex
workflows, in which operators must account for a host of parameters, metrics,
and the like in order to optimize design, development,
deployment, and operation of different technologies in order to improve
overall results. Historically, data has been collected in
heavy industrial environments by human beings using dedicated data collectors,
often recording batches of specific sensor data on
media, such as tape or a hard drive, for later analysis. Batches of data have
historically been returned to a central office for analysis,
such as by undertaking signal processing or other analysis on the data
collected by various sensors, after which analysis can be used
as a basis for diagnosing problems in an environment and/or suggesting ways to
improve operations. This work has historically
taken place on a time scale of weeks or months, and has been directed to
limited data sets.
[0006] The emergence of the Internet of Things (IoT) has made it possible to
connect continuously to and among a much wider
range of devices. Most such devices are consumer devices, such as lights,
thermostats, and the like. More complex industrial
environments remain more difficult, as the range of available data is often
limited, and the complexity of dealing with data from
multiple sensors makes it much more difficult to produce "smart" solutions
that are effective for the industrial sector. A need exists
for improved methods and systems for data collection in industrial
environments, as well as for improved methods and systems for
using collected data to provide improved monitoring, control, and intelligent
diagnosis of problems and intelligent optimization of
operations in various heavy industrial environments.
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SUMMARY
[0007] Methods and systems are provided herein for data collection in
industrial environments, as well as for improved methods
and systems for using collected data to provide improved monitoring, control,
and intelligent diagnosis of problems and intelligent
optimization of operations in various heavy industrial environments. These
methods and systems include methods, systems,
components, devices, workflows, services, processes, and the like that are
deployed in various configurations and locations, such
as: (a) at the "edge" of the Internet of Things, such as in the local
environment of a heavy industrial machine; (b) in data transport
networks that move data between local environments of heavy industrial
machines and other environments, such as of other
machines or of remote controllers, such as enterprises that own or operate the
machines or the facilities in which the machines are
operated; and (c) in locations where facilities are deployed to control
machines or their environments, such as cloud-computing
environments and on-premises computing environments of enterprises that own or
control heavy industrial environments or the
machines, devices or systems deployed in them. These methods and systems
include a range of ways for providing improved data
include a range of methods and systems for providing improved data collection,
as well as methods and systems for deploying
increased intelligence at the edge, in the network, and in the cloud or
premises of the controller of an industrial environment.
[0008] Methods and systems are disclosed herein for continuous ultrasonic
monitoring, including providing continuous ultrasonic
monitoring of rotating elements and bearings of an energy production facility.
[0009] Methods and systems are disclosed herein for cloud-based, machine
pattern recognition based on fusion of remote, analog
industrial sensors.
[0010] Methods and systems are disclosed herein for cloud-based, machine
pattern analysis of state information from multiple
analog industrial sensors to provide anticipated state information for an
industrial system.
[0011] Methods and systems are disclosed herein for on-device sensor fusion
and data storage for industrial IoT devices, including
on-device sensor fusion and data storage for an Industrial IoT device, where
data from multiple sensors is multiplexed at the device
for storage of a fused data stream.
[0012] Methods and systems are disclosed herein for a self-organizing data
marketplace for industrial IoT data, including a self-
organizing data marketplace for industrial IoT data, where available data
elements are organized in the marketplace for consumption
by consumers based on training a self-organizing facility with a training set
and feedback from measures of marketplace success.
[0013] Methods and systems are disclosed herein for self-organizing data
pools, including self-organization of data pools based
on utilization and/or yield metrics, including utilization and/or yield
metrics that are tracked for a plurality of data pools.
[0014] Methods and systems are disclosed herein for training artificial
intelligence ("Al") models based on industry-specific
feedback, including training an Al model based on industry-specific feedback
that reflects a measure of utilization, yield, or impact,
where the Al model operates on sensor data from an industrial environment.
[0015] Methods and systems are disclosed herein for a self-organized swarm of
industrial data collectors, including a self-
organizing swarm of industrial data collectors that organize among themselves
to optimize data collection based on the capabilities
and conditions of the members of the swarm.
[0016] Methods and systems are disclosed herein for an industrial IoT
distributed ledger, including a distributed ledger supporting
the tracking of transactions executed in an automated data marketplace for
industrial IoT data.
[0017] Methods and systems are disclosed herein for a self-organizing
collector, including a self-organizing, multi-sensor data
collector that can optimize data collection, power and/or yield based on
conditions in its environment.
[0018] Methods and systems are disclosed herein for a network-sensitive
collector, including a network condition-sensitive, self-
organizing, multi-sensor data collector that can optimize based on bandwidth,
quality of service, pricing and/or other network
conditions.
[0019] Methods and systems are disclosed herein for a remotely organized
universal data collector that can power up and down
sensor interfaces based on need and/or conditions identified in an industrial
data collection environment.
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[0020] Methods and systems are disclosed herein for a self-organizing storage
for a multi-sensor data collector, including self-
organizing storage for a multi-sensor data collector for industrial sensor
data.
[0021] Methods and systems are disclosed herein for a self-organizing network
coding for a multi-sensor data network, including
self-organizing network coding for a data network that transports data from
multiple sensors in an industrial data collection
environment.
[0022] Methods and systems are disclosed herein for a haptic or multi-sensory
user interface, including a wearable haptic or multi-
sensory user interface for an industrial sensor data collector, with
vibration, heat, electrical and/or sound outputs.
[0023] Methods and systems are disclosed herein for a presentation layer for
augmented reality and virtual reality (AR/VR)
industrial glasses, where heat map elements are presented based on patterns
and/or parameters in collected data.
[0024] Methods and systems are disclosed herein for condition-sensitive, self-
organized tuning of AR/VR interfaces based on
feedback metrics and/or training in industrial environments.
[0025] In embodiments, a system for data collection, processing, and
utilization of signals from at least a first element in a first
machine in an industrial environment includes a platform including a computing
environment connected to a local data collection
system having at least a first sensor signal and a second sensor signal
obtained from at least the first machine in the industrial
environment. The system includes a first sensor in the local data collection
system configured to be connected to the first machine
and a second sensor in the local data collection system. The system further
includes a crosspoint switch in the local data collection
system having multiple inputs and multiple outputs including a first input
connected to the first sensor and a second input connected
to the second sensor. The multiple outputs include a first output and second
output configured to be switchable between a condition
in which the first output is configured to switch between delivery of the
first sensor signal and the second sensor signal and a
condition in which there is simultaneous delivery of the first sensor signal
from the first output and the second sensor signal from
the second output. Each of multiple inputs is configured to be individually
assigned to any of the multiple outputs. Unassigned
outputs are configured to be switched off producing a high-impedance state.
[0026] In embodiments, the first sensor signal and the second sensor signal
are continuous vibration data about the industrial
environment. In embodiments, the second sensor in the local data collection
system is configured to be connected to the first
machine. In embodiments, the second sensor in the local data collection system
is configured to be connected to a second machine
in the industrial environment. In embodiments, the computing environment of
the platform is configured to compare relative phases
of the first and second sensor signals. In embodiments, the first sensor is a
single-axis sensor and the second sensor is a three-axis
sensor. In embodiments, at least one of the multiple inputs of the crosspoint
switch includes internet protocol, front-end signal
conditioning, for improved signal-to-noise ratio. In embodiments, the
crosspoint switch includes a third input that is configured with
a continuously monitored alarm having a pre-determined trigger condition when
the third input is unassigned to any of the multiple
outputs.
[0027] In embodiments, the local data collection system includes multiple
multiplexing units and multiple data acquisition units
receiving multiple data streams from multiple machines in the industrial
environment. In embodiments, the local data collection
system includes distributed complex programmable hardware device ("CPLD")
chips each dedicated to a data bus for logic control
of the multiple multiplexing units and the multiple data acquisition units
that receive the multiple data streams from the multiple
machines in the industrial environment. In embodiments, the local data
collection system is configured to provide high-amperage
input capability using solid state relays. In embodiments, the local data
collection system is configured to power-down at least one
of an analog sensor channel and a component board.
[0028] In embodiments, the local data collection system includes an external
voltage reference for an A/D zero reference that is
independent of the voltage of the first sensor and the second sensor. In
embodiments, the local data collection system includes a
phase-lock loop band-pass tracking filter configured to obtain slow-speed
revolutions per minute ("RPMs") and phase information.
In embodiments, the local data collection system is configured to digitally
derive phase using on-board timers relative to at least
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one trigger channel and at least one of the multiple inputs. In embodiments,
the local data collection system includes a peak-detector
configured to auto scale using a separate analog-to-digital converter for peak
detection. In embodiments, the local data collection
system is configured to route at least one trigger channel that is one of raw
and buffered into at least one of the multiple inputs. In
embodiments, the local data collection system includes at least one delta-
sigma analog-to-digital converter that is configured to
increase input oversampling rates to reduce sampling rate outputs and to
minimize anti-aliasing filter requirements. In embodiments,
the distributed CPLD chips each dedicated to the data bus for logic control of
the multiple multiplexing units and the multiple data
acquisition units includes as high-frequency crystal clock reference
configured to be divided by at least one of the distributed CPLD
chips for at least one delta-sigma analog-to-digital converter to achieve
lower sampling rates without digital resampling.
[0029] In embodiments, the local data collection system is configured to
obtain long blocks of data at a single relatively high-
sampling rate as opposed to multiple sets of data taken at different sampling
rates. In embodiments, the single relatively high-
sampling rate corresponds to a maximum frequency of about forty kilohertz. In
embodiments, the long blocks of data are for a
duration that is in excess of one minute. In embodiments, the local data
collection system includes multiple data acquisition units
each having an onboard card set configured to store calibration information
and maintenance history of a data acquisition unit in
which the onboard card set is located. In embodiments, the local data
collection system is configured to plan data acquisition routes
based on hierarchical templates.
[0030] In embodiments, the local data collection system is configured to
manage data collection bands. In embodiments, the data
collection bands define a specific frequency band and at least one of a group
of spectral peaks, a true-peak level, a crest factor
derived from a time waveform, and an overall waveform derived from a vibration
envelope. In embodiments, the local data
collection system includes a neural net expert system using intelligent
management of the data collection bands. In embodiments,
the local data collection system is configured to create data acquisition
routes based on hierarchical templates that each include the
data collection bands related to machines associated with the data acquisition
routes. In embodiments, at least one of the hierarchical
templates is associated with multiple interconnected elements of the first
machine. In embodiments, at least one of the hierarchical
templates is associated with similar elements associated with at least the
first machine and a second machine. In embodiments, at
least one of the hierarchical templates is associated with at least the first
machine being proximate in location to a second machine.
[0031] In embodiments, the local data collection system includes a graphical
user interface ("GUI") system configured to manage
the data collection bands. In embodiments, the GUI system includes an expert
system diagnostic tool. In embodiments, the platform
includes cloud-based, machine pattern analysis of state information from
multiple sensors to provide anticipated state information
for the industrial environment. In embodiments, the platform is configured to
provide self-organization of data pools based on at
least one of the utilization metrics and yield metrics. In embodiments, the
platform includes a self-organized swarm of industrial
data collectors. In embodiments, the local data collection system includes a
wearable haptic user interface for an industrial sensor
data collector with at least one of vibration, heat, electrical, and sound
outputs.
[0032] In embodiments, multiple inputs of the crosspoint switch include a
third input connected to the second sensor and a fourth
input connected to the second sensor. The first sensor signal is from a single-
axis sensor at an unchanging location associated with
the first machine. In embodiments, the second sensor is a three-axis sensor.
In embodiments, the local data collection system is
configured to record gap-free digital waveform data simultaneously from at
least the first input, the second input, the third input,
and the fourth input. In embodiments, the platform is configured to determine
a change in relative phase based on the simultaneously
recorded gap-free digital waveform data. In embodiments, the second sensor is
configured to be movable to a plurality of positions
associated with the first machine while obtaining the simultaneously recorded
gap-free digital waveform data. In embodiments,
multiple outputs of the crosspoint switch include a third output and fourth
output. The second, third, and fourth outputs are assigned
together to a sequence of tri-axial sensors each located at different
positions associated with the machine. In embodiments, the
platform is configured to determine an operating deflection shape based on the
change in relative phase and the simultaneously
recorded gap-free digital waveform data.
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[0033] In embodiments, the unchanging location is a position associated with
the rotating shaft of the first machine. In
embodiments, tri-axial sensors in the sequence of the tri-axial sensors are
each located at different positions on the first machine but
are each associated with different bearings in the machine. In embodiments,
tri-axial sensors in the sequence of the tri-axial sensors
are each located at similar positions associated with similar bearings but are
each associated with different machines. In
embodiments, the local data collection system is configured to obtain the
simultaneously recorded gap-free digital waveform data
from the first machine while the first machine and a second machine are both
in operation. In embodiments, the local data collection
system is configured to characterize a contribution from the first machine and
the second machine in the simultaneously recorded
gap-free digital waveform data from the first machine. In embodiments, the
simultaneously recorded gap-free digital waveform data
has a duration that is in excess of one minute.
[0034] In embodiments, a method of monitoring a machine having at least one
shaft supported by a set of bearings includes
monitoring a first data channel assigned to a single-axis sensor at an
unchanging location associated with the machine. The method
includes monitoring second, third, and fourth data channels each assigned to
an axis of a three-axis sensor. The method includes
recording gap-free digital waveform data simultaneously from all of the data
channels while the machine is in operation and
determining a change in relative phase based on the digital waveform data.
[0035] In embodiments, the tri-axial sensor is located at a plurality of
positions associated with the machine while obtaining the
digital waveform. In embodiments, the second, third, and fourth channels are
assigned together to a sequence of tri-axial sensors
each located at different positions associated with the machine. In
embodiments, the data is received from all of the sensors
simultaneously. In embodiments, the method includes determining an operating
deflection shape based on the change in relative
phase information and the waveform data. In embodiments, the unchanging
location is a position associated with the shaft of the
machine. In embodiments, the tri-axial sensors in the sequence of the tri-
axial sensors are each located at different positions and are
each associated with different bearings in the machine. In embodiments, the
unchanging location is a position associated with the
shaft of the machine. The tri-axial sensors in the sequence of the tri-axial
sensors are each located at different positions and are each
associated with different bearings that support the shaft in the machine.
[0036] In embodiments, the method includes monitoring the first data channel
assigned to the single-axis sensor at an unchanging
location located on a second machine. The method includes monitoring the
second, the third, and the fourth data channels, each
assigned to the axis of a three-axis sensor that is located at the position
associated with the second machine. The method also
includes recording gap-free digital waveform data simultaneously from all of
the data channels from the second machine while both
of the machines are in operation. In embodiments, the method includes
characterizing the contribution from each of the machines
in the gap-free digital waveform data simultaneously from the second machine.
[0037] In embodiments, the method includes planning data acquisition routes
based on hierarchical templates associated with at
least the first element in the first machine in the industrial environment. In
embodiments, the local data collection system manages
data collection bands that define a specific frequency band and at least one
of a group of spectral peaks, a true-peak level, a crest
factor derived from a time waveform, and an overall waveform derived from a
vibration envelope. In embodiments, the local data
collection system includes a neural net expert system using intelligent
management of the data collection bands. In embodiments,
the local data collection system creates data acquisition routes based on
hierarchical templates that each include the data collection
bands related to machines associated with the data acquisition routes. In
embodiments, at least one of the hierarchical templates is
associated with multiple interconnected elements of the first machine. In
embodiments, at least one of the hierarchical templates is
associated with similar elements associated with at least the first machine
and a second machine. In embodiments, at least one of
the hierarchical templates is associated with at least the first machine being
proximate in location to a second machine.
[0038] Methods and systems described herein for industrial machine sensor data
streaming, collection, processing, and storage
may be configured to operate and integrate with existing data collection,
processing and storage systems and may include a method
for capturing a plurality of streams of sensed data from sensors deployed to
monitor aspects of an industrial machine associated
with at least one moving part of the machine; at least one nf the Qtre1n1Q
entitnitlitla i nlurality of frequencies of data. The method
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may include identifying a subset of data in at least one of the plurality of
streams that corresponds to data representing at least one
predefined frequency. The at least one predefined frequency is represented by
a set of data collected from alternate sensors deployed
to monitor aspects of the industrial machine associated with the at least one
moving part of the machine. The method may further
include processing the identified data with a data processing facility that
processes the identified data with an algorithm configured
to be applied to the set of data collected from alternate sensors. Lastly, the
method may include storing the at least one of the streams
of data, the identified subset of data, and a result of processing the
identified data in an electronic data set.
[0039] Methods and systems described herein for industrial machine sensor data
streaming, collection, processing, and storage
may be configured to operate and integrate with existing data collection,
processing, and storage systems and may include a method
for applying data captured from sensors deployed to monitor aspects of an
industrial machine associated with at least one moving
part of the machine. The data is captured with predefined lines of resolution
covering a predefined frequency range and is sent to a
frequency matching facility that identifies a subset of data streamed from
other sensors deployed to monitor aspects of the industrial
machine associated with at least one moving part of the machine. The streamed
data includes a plurality of lines of resolution and
frequency ranges. The subset of data identified corresponds to the lines of
resolution and predefined frequency range. This method
may include storing the subset of data in an electronic data record in a
format that corresponds to a format of the data captured with
predefined lines of resolution; and signaling to a data processing facility
the presence of the stored subset of data. This method may,
optionally, include processing the subset of data with at least one set of
algorithms, models and pattern recognizers that corresponds
to algorithms, models and pattern recognizers associated with processing the
data captured with predefined lines of resolution
covering a predefined frequency range.
[0040] Methods and systems described herein for industrial machine sensor data
streaming, collection, processing, and storage
may be configured to operate and integrate with existing data collection,
processing and storage systems and may include a method
for identifying a subset of streamed sensor data, the sensor data captured
from sensors deployed to monitor aspects of an industrial
machine associated with at least one moving part of the machine, the subset of
streamed sensor data at predefined lines of resolution
for a predefined frequency range, and establishing a first logical route for
communicating electronically between a first computing
facility performing the identifying and a second computing facility, wherein
identified subset of the streamed sensor data is
communicated exclusively over the established first logical route when
communicating the subset of streamed sensor data from the
first facility to the second facility. This method may further include
establishing a second logical route for communicating
electronically between the first computing facility and the second computing
facility for at least one portion of the streamed sensor
data that is not the identified subset. Additionally, this method may further
include establishing a third logical route for
communicating electronically between the first computing facility and the
second computing facility for at least one portion of the
streamed sensor data that includes the identified subset and at least one
other portion of the data not represented by the identified
subset.
[0041] Methods and systems described herein for industrial machine sensor data
streaming, collection, processing, and storage
may be configured to operate and integrate with existing data collection,
processing and storage systems and may include a first
data sensing and processing system that captures first data from a first set
of sensors deployed to monitor aspects of an industrial
machine associated with at least one moving part of the machine, the first
data covering a set of lines of resolution and a frequency
range. This system may include a second data sensing and processing system
that captures and streams a second set of data from a
second set of sensors deployed to monitor aspects of the industrial machine
associated with at least one moving part of the machine,
the second data covering a plurality of lines of resolution that includes the
set of lines of resolution and a plurality of frequencies
that includes the frequency range. The system may enable selecting a portion
of the second data that corresponds to the set of lines
of resolution and the frequency range of the first data, and processing the
selected portion of the second data with the first data
sensing and processing system.
[0042] Methods and systems described herein for industrial machine sensor data
streaming, collection, processing, and storage
may be configured to operate and integrate with existing data collection,
processing and storage systems and may include a method
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for automatically processing a portion of a stream of sensed data. The sensed
data is received from a first set of sensors deployed to
monitor aspects of an industrial machine associated with at least one moving
part of the machine. The sensed data is in response to
an electronic data structure that facilitates extracting a subset of the
stream of sensed data that corresponds to a set of sensed data
received from a second set of sensors deployed to monitor the aspects of the
industrial machine associated with the at least one
moving part of the machine. The set of sensed data is constrained to a
frequency range. The stream of sensed data includes a range
of frequencies that exceeds the frequency range of the set of sensed data, the
processing comprising executing an algorithm on a
portion of the stream of sensed data that is constrained to the frequency
range of the set of sensed data, the algorithm configured to
process the set of sensed data.
[0043] Methods and systems described herein for industrial machine sensor data
streaming, collection, processing, and storage
may be configured to operate and integrate with existing data collection,
processing and storage systems and may include a method
for receiving first data from sensors deployed to monitor aspects of an
industrial machine associated with at least one moving part
of the machine. This method may further include detecting at least one of a
frequency range and lines of resolution represented by
the first data; receiving a stream of data from sensors deployed to monitor
the aspects of the industrial machine associated with the
at least one moving part of the machine. The stream of data includes: (1) a
plurality of frequency ranges and a plurality of lines of
resolution that exceeds the frequency range and the lines of resolution
represented by the first data; (2) a set of data extracted from
the stream of data that corresponds to at least one of the frequency range and
the lines of resolution represented by the first data;
and (3) the extracted set of data which is processed with a data processing
algorithm that is configured to process data within the
frequency range and within the lines of resolution of the first data.
[0044] An example monitoring system for data collection in an industrial
environment includes a data acquisition circuit that
interprets a number of detection values, each of the detection values
corresponding to an input received from at least one of a
number of input sensors; a multiplexor (MUX) having a number of inputs
corresponding to a subset of the detection values; a
MUX control circuit that interprets the subset of the detection values and
provides, as a result, a logical control of the MUX and a
correspondence of MUX input and detection values. The logical control of the
MUX includes an adaptive scheduling of one or
more select lines (e.g., MUX input to output relationships, MUX input to
sensor relationships, and/or MUX output to downstream
data collector relationships). The example system further includes a data
analysis circuit that receives an output from the MUX
and data corresponding to the logical control of the MUX resulting in a
component health status, and an analysis response circuit
adapted to perform at least one operation in response to the component health
status. The input sensors include at least two
sensors selected from: a temperature sensor, a load sensor, a vibration
sensor, an acoustic wave sensor, a heat flux sensor, an
infrared sensor, an accelerometer, a ti-axial vibration sensor, and/or and a
tachometer.
[0045] Certain further aspects of an example system are described following,
any one or more of which may be present in
certain embodiments. An example system includes where one or more of the
detection values correspond to a fusion of two or
more input sensors representing a virtual sensor; a data storage circuit
adapted to store at least one of a number of component
specifications and/or an anticipated component state information, and to
buffer a subset of the detection values for a
predetermined length of time; a data storage circuit adapted to store at least
one of component specifications and/or an anticipated
component state information, and to buffer an output of the MUX and data
corresponding to the logical control of the MUX for a
predetermined length of time. An example system includes the data analysis
circuit further including a peak detection circuit, a
phase detection circuit, a bandpass filter circuit, a frequency transformation
circuit, a frequency analysis circuit, a phase lock loop
circuit, a torsional analysis circuit, and/or a bearing analysis circuit. An
example system includes the operation as storing
additional data in the data storage circuit, enabling or disabling one or more
portions of the MUX, and/or causing the MUX
control circuit to alter the logical control of the MUX and the correspondence
of MUX input and detection values.
[0046] An example system for data collection in an industrial environment
includes a data acquisition circuit that interprets a
number of detection values, each of the number of detection values
corresponding to input received from at least one of a number
of input sensors; at least two multiplexors (MUXs), each having inputs
correspondinu to a subset of the detection values and each
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providing a data stream as output; a MUX control circuit that interprets a
subset of the number of detection values and provides
logical control of the MUXs, and control of a correspondence of MUX input and
detected values as a result, where the logic
control of the MUX comprise an adaptive scheduling of one or more select lines
(e.g., MUX input to output relationships, MUX
input to sensor relationships, and/or MUX output to downstream data collector
relationships, and/or relationships between the
MUXs). The example system further includes a data analysis circuit that
receives the data stream from at least one of the MUXs
and data corresponding to the logic control of the MUXs resulting in a
component health status, and an analysis response circuit
that performs at least one operation in response to the component health
status. The input sensors include at least two sensors
selected from: a temperature sensor, a load sensor, a vibration sensor, an
acoustic wave sensor, a heat flux sensor, an infrared
sensor, an accelerometer, a tri-axial vibration sensor, and/or and a
tachometer.
[0047] Certain further aspects of an example system are described following,
any one or more of which may be present in
certain embodiments. An example system includes where at least one of the
number of detection values corresponds to a fusion of
two or more input sensors representing a virtual sensor; a data storage
circuit adapted to store at least one of a number of
component specifications and an anticipated component state information, and
to buffer a subset of the number of detection values
for a predetermined length of time; a data storage circuit adapted to store at
least one of component specifications and an
anticipated component state information and buffer an output of the
multiplexor and data corresponding to the logical control of
the MUX for a predetermined length of time; and/or where the data analysis
circuit includes at least one of a peak detection
circuit, a phase detection circuit, a bandpass filter circuit, a frequency
transformation circuit, a frequency analysis circuit, a phase
lock loop circuit, a torsional analysis circuit, and/or a bearing analysis
circuit. An example system includes where the operation
includes storing additional data in the data storage circuit; enabling or
disabling one or more portions of at least one of the MUXs,
and/or where the operation includes causing the MUX control circuit to alter
the logical control of the MUXs and the
correspondence of MUX input and detection values.
[0048] An example system for data collection in an industrial environment
having a self-sufficient data acquisition box for
capturing and analyzing data in an industrial process includes: a data circuit
for analyzing a number of sensor inputs from one or
more sensors; a network control circuit for sending and receiving information
related to the sensor inputs to an external system,
where the system provides sensor data to one or more similarly configured
systems; and where the data circuit dynamically
reconfigures a route by which data is sent based, at least in part, on a
number of other devices requesting the information.
[0049] Certain further aspects of an example system are described following,
any one or more of which may be present in
certain embodiments. An example system includes a number of network
communication interfaces; where the network control
circuit bridges another similarly configured system from a first network to a
second network via by utilizing the number of
network communication interfaces; where the other similarly configured system
has one or more operational characteristics that
differ from one or more operational characteristics of the system; where the
one or more operational characteristics of the
similarly configured system are selected from the list consisting of a power,
a storage, a network connectivity, a proximity, a
reliability and a duty cycle; where the network control circuit is adapted to
implement a network of similarly configured systems
using an intercommunication protocol selected from the list consisting of a
multi-hop, a mesh, a serial, a parallel, a ring, a real-
time and a hub-and-spoke; where the system is adapted to continuously provide
a single copy of its information to another
similarly configured system and direct one or more entities requesting the
information to the other similarly configured system;
where the system is adapted to store a summary of the information; and/or
where the system is adapted to store the summary after
a configurable time period.
[0050] An example procedure for data collection in an industrial production
environment includes: an operation to analyze, with
a processor, a number of sensor inputs, where the sensor inputs are configured
to sense a health status of a component of at least
one target system; an operation to sample, with the processor, data received
from at least one of the number of sensor inputs; and
an operation to self-organize, with the processor, at least one of: (i) a
storage operation of the data; (ii) a collection operation of
one or more sensors adapted to provide the number of sensor inputs, and (iii)
a selection operation of the number of sensor inputs.
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In certain further embodiments, the example procedure includes where the
number of sensor inputs are further configured to sense
at least one of: an operational mode of the target system, a fault mode of the
target system, or a health status of the target system.
[0051] An example system for data collection in an industrial production
environment includes: one or more sensors adapted to
provide a number of sensor inputs, where the one or more sensors are
configured to sense a health status of a component of at
least one target system; and a data collector including a processor, and
adapted to analyze the number of sensor inputs, sample
data received from at least one of the number of sensor inputs, and to self-
organize at least one of: (i) a storage operation of the
data; (ii) a collection operation of one or more sensors adapted to provide
the number of sensor inputs, and (iii) a selection
operation of the number of sensor inputs. In certain further embodiments, the
example system includes where at least one of the
one or more sensors forms a part of the data collector; where at least one of
the one or more sensors is external to the data
collector; and/or where the one or more sensor inputs are configured to sense
at least one of: an operational mode of the target
system, a fault mode of the target system, or a health status of the target
system.
[0052] An example procedure includes an operation to analyze, with a
processor, a number of sensor inputs; an operation to
sample, with the processor, data received from at least one of the number of
sensor inputs at a first frequency, and an operation to
self-organize, with the processor, a selection operation of the number of
sensor inputs. An example selection operation includes:
receiving a signal relating to at least one condition of an industrial
environment; and based, at least in part, on the signal, changing
at least one of the sensor inputs analyzed and sampling the data received from
at least one of the number of sensor inputs at a
second frequency.
[0053] Certain further aspects of an example procedure are described
following, any one or more of which may be present in
certain embodiments. An example procedure includes where the at least one
condition of the industrial environment is a signal-to-
noise ratio of the sampled data; where the selection operation further
includes identifying one or more non-target signals in a same
frequency band as the target signal to be sensed, and based, at least in part,
on the identified one or more non-target signals,
changing at least one of the sensor inputs analyzed and a frequency of the
sampling; where the selection operation further includes
identifying other data collectors sensing in a same signal band as the target
signal to be sensed; and based, at least in part, on the
identified other data collectors, changing at least one of the sensor inputs
analyzed and a frequency of the sampling; where the
selection operation further includes identifying a level of activity of a
target associated with the target signal to be sensed, and
based, at least in part, on the identified level of activity, changing the at
least one of the sensor inputs analyzed and a frequency of
the sampling; where the selection operation further includes receiving data
indicative of one or more environmental conditions
near a target associated with the target signal, comparing the received one or
more environmental conditions of the target with
past environmental conditions near the target or another target similar to the
target, and based, ate least in part, on the comparison,
changing at least one of the sensor inputs analyzed and frequency of the
sampling; and/or where the selection operation further
includes transmitting at least a portion of the received sampling data to
another data collector according to a predetermined
hierarchy of data collection.
[0054] An example procedure for data collection in an industrial environment
having self-organization functionality includes an
operation to analyzed, at a data collector, a number of sensor inputs from one
or more sensors, where at least one of the number of
sensor inputs corresponds to a vibration sensor; an operation to provide
frequency data corresponding to a component of the
industrial environment; an operation to sample data received from the number
of sensor inputs; and an operation to self-organize
at least one of: (i) a storage operation of the data; (ii) a collection
operation of sensors that provide the number of sensor inputs,
and (iii) a selection operation of the number of sensor inputs. In certain
embodiments, the selection operation further includes an
operation to receive a signal relating to at least one condition of the
component of the industrial environment, and based, at least in
part, on the signal, an operation to change a frequency of the sampling of the
one of the number of sensor inputs corresponding to
the vibration sensor.
[0055] Certain further aspects of an example procedure are described
following, any one or more of which may be present in
certain embodiments. An example procedure further includes an oneration to
receive data indicative of at least one condition of
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the industrial environment in proximity to the component of the industrial
environment, an operation to transmit at least a portion
of the received sampled data to another collector according to a predetermined
hierarchy of data collection; an operation to receive
feedback via a network connection relating to a quality or sufficiency of the
transmitted data; and operation to analyze the
received feedback, based, at least in part, on the analysis of the received
feedback, an operation to change at least one of: the
sensor inputs analyzed, the frequency of the sampling, the data stored, and/or
the data transmitted. An example procedure
includes where the at least one condition of the industrial environment is a
signal-to-noise ratio of the sampled data; where at least
one of the one or more sensors forms a part of the data collector; where at
least one of the one or more sensors is external to the
data collector; and/or where the vibration sensor is configured to sense at
least one of: an operational mode, a fault mode, or a
health status of the component of the industrial environment.
[0056] An example procedure for data collection in an industrial environment
having self-organization functionality includes an
operation to analyze, at a data collector, a number of sensor inputs from one
or more sensors; an operation to sample data received
from the sensor inputs; and an operation to perform self-organizing including
at least one of: (i) a storage operation of the data; (ii)
a collection operation of sensors that provide the number of sensor inputs,
and (iii) a selection operation of the number of sensor
inputs. The example procedure includes the selection operation further
including: an operation to identify a target signal to be
sensed; an operation to receive a signal relating to at least one condition of
the industrial environment, and based, at least in part,
on the signal, an operation to change at least one of the sensor inputs
analyzed and a frequency of the sampling; an operation to
receive data indicative of environmental conditions near a target associated
with the target signal; an operation to transmit at least
a portion of the received sampling data to another data collector according to
a predetermined hierarchy of data collection; an
operation to receive feedback via a network connection relating to one or more
yield metrics of the transmitted data; an operation
to analyze the received feedback; and based on the analysis of the received
feedback, an operation to change at least one of: the
sensor inputs analyzed, the frequency of sampling, the data stored, and the
data transmitted. In certain embodiments, an example
procedure includes where the at least one condition of the industrial
environment is a signal-to-noise ratio of the sampled data;
where at least one of the one or more sensors forms a part of the data
collector; where at least one of the one or more sensors is
external to the data collector; and/or where the number of sensor inputs are
configured to sense at least one of an operational
mode, a fault mode and a health status of at least one target system.
[0057] An example procedure for data collection in an industrial environment
having self-organization functionality, comprising
includes an operation to analyze, at a data collector, a number of sensor
inputs from one or more sensors; an operation to sample
data received from the sensor inputs; and an operation to self-organize at
least one of: : (i) a storage operation of the data; (ii) a
collection operation of sensors that provide the number of sensor inputs, and
(iii) a selection operation of the number of sensor
inputs. An example procedure further includes the selection operation
including: an operation to identify a target signal to be
sensed; an operation to receive a signal relating to at least one condition of
the industrial environment; an operation based, at least
in part, on the signal, to change at least one of the sensor inputs analyzed
and a frequency of the sampling; an operation to receive
data indicative of environmental conditions near a target associated with the
target signal; an operation to transmit at least a
portion of the received sampling data to another data collector according to a
predetermined hierarchy of data collection; an
operation to receive feedback via a network connection relating to a quality
or sufficiency of the transmitted data; and an
operation based, at least in part, on the analysis of the received feedback,
to execute a dimensionality reduction algorithm on the
sensed data.
[0058] Certain further aspects of an example procedure are described
following, any one or more of which may be present in
certain embodiments. An example procedure includes the dimensionality
reduction algorithm including one or more of: a
Decision Tree, a Random Forest, a Principal Component Analysis, a Factor
Analysis, a Linear Discriminant Analysis,
Identification based on correlation matrix, a Missing Values Ratio, a Low
Variance Filter, a Random Projection, a Nonnegative
Matrix Factorization, a Stacked Auto-encoder, a Chi-square or Information
Gain, a Multidimensional Scaling, a Correspondence
Analysis, a Factor Analysis, a Clustering, and/or a Bayesian Model. An example
procedure includes: where the dimensionality
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reduction algorithm is performed at the data collector; where executing the
dimensionality reduction algorithm comprises sending
the sensed data to a remote computing device; where the at least one condition
of the industrial environment is a signal-to-noise
ratio of the sampled data; where at least one of the one or more sensors forms
a part of the data collector; where at least one of the
one or more sensors is external to the data collector; and/or where the number
of sensor inputs are configured to sense at least one
of an operational mode, a fault mode and a health status of at least one
target system.
[0059] An example system for self-organizing collection and storage of data
collection in a power generation environment
includes a data collector for handling a number of sensor inputs from one or
more sensors in the power generation environment,
where the number of sensor inputs is configured to sense at least one of an
operational mode, a fault mode, and a health status of
at least one target system of the power generation environment; and a self-
organizing system for self-organizing at least one of (i)
a storage operation of the data; (ii) a data collection operation of the
sensors that provide the number of sensor inputs, and (iii) a
selection operation of the number of sensor inputs.
[0060] Certain further aspects of an example system are described following,
any one or more of which may be present in
certain embodiments. An example system includes where the self-organizing
system organizes a swarm of mobile data collectors
to collect data from a number of target systems; where each of the number of
target systems further comprises at least one system
such as a fuel handling system, a power source, a turbine, a generator, a gear
system, an electrical transmission system, and/or a
transformer; where the system further includes an intermittently available
network, and where the self-organizing system is
configured to perform the self-organizing based on an impeded network
connectivity of the intermittently available network;
and/or where the self-organizing system generates a storage specification for
organizing storage of the data, the storage
specification specifying data for local storage in the power generation
environment and specifying data for streaming via a
network connection from the power generation environment.
[0061] An example system for self-organizing collection and storage of data
collection in an energy source extraction
environment includes a data collector for handling a number of sensor inputs
from sensors in the energy extraction environment,
where the number of sensor inputs is configured to sense at least one of an
operational mode, a fault mode, and a health status of
at least one target system of the energy extraction environment; and a self-
organizing system for self-organizing at least one of (i)
a storage operation of the data; (ii) a data collection operation of the
sensors that provide the number of sensor inputs, and (iii) a
selection operation of the number of sensor inputs.
[0062] Certain further aspects of an example system are described following,
any one or more of which may be present in
certain embodiments. An example system includes where the self-organizing
system organizes a swarm of mobile data collectors
to collect data from a number of target systems; where each of the number of
target systems further include a system such as a
hauling system, a lifting system, a drilling system, a mining system, a
digging system, a boring system, a material handling
system, a conveyor system, a pipeline system, a wastewater treatment system,
and/or a fluid pumping system; where the system
further comprises an intermittently available network, and where the self-
organizing system is configured to perform the self-
organizing based on an impeded network connectivity of the intermittently
available network; where the energy source extraction
environment is a metal mining environment; where the energy source extraction
environment is a coal mining environment; where
the energy source extraction environment is a mineral mining environment;
where the energy source extraction environment is an
oil drilling environment; and/or where the self-organizing system generates a
storage specification for organizing storage of the
data, the storage specification specifying data for local storage in the
energy extraction environment and specifying data for
streaming via a network connection from the energy extraction environment.
[0063] An example system for self-organizing collection and storage of data
collection in refining environment includes a data
collector for handling a number of sensor inputs from sensors in the refining
environment, where the number of sensor inputs is
configured to sense at least one of an operational mode, a fault mode, and a
health status of at least one target system of the
refining environment; and a self-organizing system for self-organizing at
least one of (i) a storage operation of the data; (ii) a data
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collection operation of the sensors that provide the number of sensor inputs,
and (iii) a selection operation of the number of sensor
inputs.
[0064] Certain further aspects of an example system are described following,
any one or more of which may be present in
certain embodiments. An example system includes where the self-organizing
system organizes a swarm of mobile data collectors
to collect data from a number of target systems; where the self-organizing
system generates a storage specification for organizing
the storage of the data, the storage specification specifying data for local
storage in the refining environment and specifying data
for streaming via a network connection from the refining environment; where
each of the number of target systems further include
a system such as a power system, a pumping system, a mixing system, a reaction
system, a distillation system, a fluid handling
system, a heating system, a cooling system, an evaporation system, a catalytic
system, a moving system, and a container system;
where the system further comprises an intermittently available network, and
where the self-organizing system is configured to
perform the self-organizing based on an impeded network connectivity of the
intermittently available network; where the refining
environment is a chemical refining environment; where the refining environment
is a pharmaceutical environment; where the
refining environment is a biological refining environment; and/or where the
refining environment is a hydrocarbon refining
environment.
BRIEF DESCRIPTION OF THE FIGURES
[0065] Figures 1 through Figure 5 are diagrammatic views that each depicts
portions of an overall view of an industrial Internet
of Things (IoT) data collection, monitoring and control system in accordance
with the present disclosure.
[0066] Figure 6 is a diagrammatic view of a platform including a local data
collection system disposed in an industrial environment
for collecting data from or about the elements of the environment, such as
machines, components, systems, sub-systems, ambient
conditions, states, workflows, processes, and other elements in accordance
with the present disclosure.
[0067] Figure 7 is a diagrammatic view that depicts elements of an industrial
data collection system for collecting analog sensor
data in an industrial environment in accordance with the present disclosure.
[0068] Figure 8 is a diagrammatic view of a rotating or oscillating machine
having a data acquisition module that is configured to
collect waveform data in accordance with the present disclosure.
[0069] Figure 9 is a diagrammatic view of an exemplary tri-axial sensor
mounted to a motor bearing of an exemplary rotating
machine in accordance with the present disclosure.
[0070] Figure 10 and Figure 11 are diagrammatic views of an exemplary tri-
axial sensor and a single-axis sensor mounted to an
exemplary rotating machine in accordance with the present disclosure.
[0071] Figure 12 is a diagrammatic view of a multiple machines under survey
with ensembles of sensors in accordance with the
present disclosure.
[0072] Figure 13 is a diagrammatic view of hybrid relational metadata and a
binary storage approach in accordance with the
present disclosure.
[0073] Figure 14 is a diagrammatic view of components and interactions of a
data collection architecture involving application of
cognitive and machine learning systems to data collection and processing in
accordance with the present disclosure.
[0074] Figure 15 is a diagrammatic view of components and interactions of a
data collection architecture involving application of
a platform having a cognitive data marketplace in accordance with the present
disclosure.
[0075] Figure 16 is a diagrammatic view of components and interactions of a
data collection architecture involving application of
a self-organizing swarm of data collectors in accordance with the present
disclosure.
[0076] Figure 17 is a diagrammatic view of components and interactions of a
data collection architecture involving application of
a haptic user interface in accordance with the present disclosure.
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[0077] Figure 18 is a diagrammatic view of a multi-format streaming data
collection system in accordance with the present
disclosure.
[0078] Figure 19 is a diagrammatic view of combining legacy and streaming data
collection and storage in accordance with the
present disclosure.
[0079] Figure 20 is a diagrammatic view of industrial machine sensing using
both legacy and updated streamed sensor data
processing in accordance with the present disclosure.
[0080] Figure 21 is a diagrammatic view of an industrial machine sensed data
processing system that facilitates portal algorithm
use and alignment of legacy and streamed sensor data in accordance with the
present disclosure.
[0081] Figure 22 is a diagrammatic view of components and interactions of a
data collection architecture involving a streaming
data acquisition instrument receiving analog sensor signals from an industrial
environment connected to a cloud network facility in
accordance with the present disclosure.
[0082] Figure 23 is a diagrammatic view of components and interactions of a
data collection architecture involving a streaming
data acquisition instrument having an alarms module, expert analysis module,
and a driver API to facilitate communication with a
cloud network facility in accordance with the present disclosure.
[0083] Figure 24 is a diagrammatic view of components and interactions of a
data collection architecture involving a streaming
data acquisition instrument and first in, first out memory architecture to
provide a real time operating system in accordance with the
present disclosure.
[0084] Figure 25 through Figure 30 are diagrammatic views of screens showing
four analog sensor signals, transfer functions
between the signals, analysis of each signal, and operating controls to move
and edit throughout the streaming signals obtained from
the sensors in accordance with the present disclosure.
[0085] Figure 31 is a diagrammatic view of components and interactions of a
data collection architecture involving a multiple
streaming data acquisition instrument receiving analog sensor signals and
digitizing those signals to be obtained by a streaming hub
server in accordance with the present disclosure.
[0086] Figure 32 is a diagrammatic view of components and interactions of a
data collection architecture involving a master raw
data server that processes new streaming data and data already extracted and
processed in accordance with the present disclosure.
[0087] Figure 33, Figure 34, and Figure 35 are diagrammatic views of
components and interactions of a data collection architecture
involving a processing, analysis, report, and archiving server that processes
new streaming data and data already extracted and
processed in accordance with the present disclosure.
[0088] Figure 36 is a diagrammatic view of components and interactions of a
data collection architecture involving a relation
database server and data archives and their connectivity with a cloud network
facility in accordance with the present disclosure.
[0089] Figure 37 through Figure 42 are diagrammatic views of components and
interactions of a data collection architecture
involving a virtual streaming data acquisition instrument receiving analog
sensor signals from an industrial environment connected
to a cloud network facility in accordance with the present disclosure.
[0090] Figure 43 is a diagrammatic view that depicts embodiments of a data
monitoring device in accordance with the present
disclosure.
[0091] Figure 44 and Figure 45 are diagrammatic views that depict embodiments
of a data monitoring device in accordance with
the present disclosure.
[0092] Figure 46 is a diagrammatic view that depicts embodiments of a data
monitoring device in accordance with the present
disclosure.
[0093] Figures 47 and 48 are diagrammatic views that depict an embodiment of a
system for data collection in accordance with
the present disclosure.
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[0094] Figures 49 and 50 are diagrammatic views that depict an embodiment of a
system for data collection comprising a plurality
of data monitoring devices in accordance with the present disclosure.
[0095] Figure 51 depicts an embodiment of a data monitoring device
incorporating sensors in accordance with the present
disclosure.
[0096] Figures 52 and 53 are diagrammatic views that depict embodiments of a
data monitoring device in communication with
external sensors in accordance with the present disclosure.
[0097] Figure 54 is a diagrammatic view that depicts embodiments of a data
monitoring device with additional detail in the signal
evaluation circuit in accordance with the present disclosure.
[0098] Figure 55 is a diagrammatic view that depicts embodiments of a data
monitoring device with additional detail in the signal
evaluation circuit in accordance with the present disclosure.
[0099] Figure 56 is a diagrammatic view that depicts embodiments of a data
monitoring device with additional detail in the signal
evaluation circuit in accordance with the present disclosure.
[00100]Figure 57 is a diagrammatic view that depicts embodiments of a system
for data collection in accordance with the present
disclosure.
00101] Figure 58 is a diagrammatic view that depicts embodiments of a system
for data collection comprising a plurality of data
monitoring devices in accordance with the present disclosure.
[00102] Figure 59 is a diagrammatic view that depicts embodiments of a data
monitoring device in accordance with the present
disclosure.
[00103] Figures 60 and 61 are diagrammatic views that depict embodiments of a
data monitoring device in accordance with the
present disclosure.
[00104] Figures 62-63 are diagrammatic views that depict embodiments of a data
monitoring device in accordance with the present
disclosure.
[00105] Figures 64 and 65 are diagrammatic views that depict embodiments of a
data monitoring device in accordance with the
present disclosure.
[00106] Figures 66 and 67 is a diagrammatic view that depicts embodiments of a
system for data collection comprising a plurality
of data monitoring devices in accordance with the present disclosure.
[00107] Figure 68 is a diagrammatic view that depicts embodiments of a data
monitoring device in accordance with the present
disclosure.
[00108] Figures 69 and 70 are diagrammatic views that depict embodiments of a
data monitoring device in accordance with the
present disclosure.
[00109] Figure 71 is a diagrammatic view that depicts embodiments of a data
monitoring device in accordance with the present
disclosure.
[00110] Figure 72 is a diagrammatic view that depicts embodiments of a data
monitoring device in accordance with the present
disclosure.
00111] Figures 73 and 74 are diagrammatic views that depict embodiments of a
system for data collection in accordance with the
present disclosure.
[00112] Figures 75 and 76 are diagrammatic views that depict embodiments of a
system for data collection comprising a plurality
of data monitoring devices in accordance with the present disclosure.
[00113] Figure 77 is a diagrammatic view that depicts embodiments of a data
monitoring device in accordance with the present
disclosure.
[00114] Figures 78 and 79 are diagrammatic views that depict embodiments of a
data monitoring device in accordance with the
present disclosure.
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[00115] Figure 80 is a diagrammatic view that depicts embodiments of a data
monitoring device in accordance with the present
disclosure.
[00116] Figures 81 and 82 are diagrammatic views that depict embodiments of a
system for data collection in accordance with the
present disclosure.
[00117] Figures 83 and 84 are diagrammatic views that depict embodiments of a
system for data collection comprising a plurality
of data monitoring devices in accordance with the present disclosure.
[00118] Figure 85 is a diagrammatic view that depicts embodiments of a data
monitoring device in accordance with the present
disclosure.
[00119] Figures 86 and 87 are diagrammatic views that depict embodiments of a
data monitoring device in accordance with the
present disclosure.
100120]Figure 88 is a diagrammatic view that depicts embodiments of a data
monitoring device in accordance with the present
disclosure.
00121] Figures 89 and 90 are diagrammatic views that depict embodiments of a
system for data collection in accordance with the
present disclosure.
00122] Figures 91 and 92 are diagrammatic views that depict embodiments of a
system for data collection comprising a plurality
of data monitoring devices in accordance with the present disclosure.
[00123]Figure 93 is a diagrammatic view that depicts embodiments of a data
monitoring device in accordance with the present
disclosure.
[00124]Figures 94 and 95 are diagrammatic views that depict embodiments of a
data monitoring device in accordance with the
present disclosure.
[00125]Figure 96 is a diagrammatic view that depicts embodiments of a data
monitoring device in accordance with the present
disclosure.
00126] Figures 97 and 98 are diagrammatic views that depict embodiments of a
system for data collection in accordance with the
present disclosure.
[00127]Figures 99 and 100 are diagrammatic views that depict embodiments of a
system for data collection comprising a plurality
of data monitoring devices in accordance with the present disclosure.
[00128]Figure 101 is a diagrammatic view of components and interactions of a
data collection architecture involving swarming
data collectors and sensor mech protocol in an industrial environment in
accordance with the present disclosure.
[00129]Figure 102 through Figure 105 are diagrammatic views mobile sensors
platforms in an industrial environment in
accordance with the present disclosure.
[00130]Figure 106 is a diagrammatic view of components and interactions of a
data collection architecture involving two mobile
sensor platforms inspecting a vehicle during assembly in an industrial
environment in accordance with the present disclosure.
[00131]Figure 107 and Figure 108 are diagrammatic views one of the mobile
sensor platforms in an industrial environment in
accordance with the present disclosure.
[0132] Figure 109 is a diagrammatic view of components and interactions of a
data collection architecture involving two mobile
sensor platforms inspecting a turbine engine during assembly in an industrial
environment in accordance with the present disclosure.
[0133] Figure 110 is a diagrammatic view that depicts data collection system
according to some aspects of the present disclosure.
[0134] Figures 111-119 are diagrammatic views that depicts data collection
systems according to some aspects of the present
disclosure.
[0135] Figure 120 is a diagrammatic view that depicts a smart heating system
as an element in a network for in an industrial
Internet of Things ecosystem in accordance with the present disclosure.
DETAILED DESCRIPTION
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[0136] Detailed embodiments of the present disclosure are disclosed herein;
however, it is to be understood that the disclosed
embodiments are merely exemplary of the disclosure, which may be embodied in
various forms. Therefore, specific structural and
functional details disclosed herein are not to be interpreted as limiting, but
merely as a basis for the claims and as a representative
basis for teaching one skilled in the art to variously employ the present
disclosure in virtually any appropriately detailed structure.
[0137] The terms "a" or "an," as used herein, are defined as one or more than
one. The term "another," as used herein, is defined
as at least a second or more. The terms "including" and/or "having," as used
herein, are defined as comprising (i.e., open transition).
[0138] While only a few embodiments of the present disclosure have been shown
and described, it will be obvious to those skilled
in the art that many changes and modifications may be made thereunto without
departing from the spirit and scope of the present
disclosure as described in the following claims. All patent applications and
patents, both foreign and domestic, and all other
publications referenced herein are incorporated herein in their entireties to
the full extent permitted by law.
[0139] Figures 1 through 5 depict portions of an overall view of an industrial
Internet of Things (IoT) data collection, monitoring
and control system 10. Figure 2 shows an upper left portion of a schematic
view of an industrial IoT system 10 of Figures 1-5.
Figure 2 includes a mobile ad hoc network ("MANET") 20, which may form a
secure, temporal network connection 22 (sometimes
connected and sometimes isolated), with a cloud 30 or other remote networking
system, so that network functions may occur over
the MANET 20 within the environment, without the need for external networks,
but at other times information can be sent to and
from a central location. This allows the industrial environment to use the
benefits of networking and control technologies, while
also providing security, such as preventing cyber-attacks. The MANET 20 may
use cognitive radio technologies 40, including ones
that form up an equivalent to the IP protocol, such as router 42, MAC 44, and
physical layer technologies 46. Also, depicted is
network-sensitive or network-aware transport of data over the network to and
from a data collection device or a heavy industrial
machine.
[0140] Figure 3 shows the upper right portion of a schematic view of an
industrial IoT system 10 of Figures 1 through 5. This
includes intelligent data collection systems 102 deployed locally, at the edge
of an IoT deployment, where heavy industrial machines
are located. This includes various sensors 52, swarms of data collectors 4202,
IoT devices 54, data storage capabilities (including
intelligent, self-organizing storage), sensor fusion (including self-
organizing sensor fusion), and the like. Figure 3 shows interfaces
for data collection, including multi-sensory interfaces, tablets, smartphones
58, and the like. Figure 3 also shows data pools 60 that
may collect data published by machines or sensors that detect conditions of
machines, such as for later consumption by local or
remote intelligence. A distributed ledger system 62 may distribute storage
across the local storage of various elements of the
environment, or more broadly throughout the system.
[0141] Figure 1 shows a center portion of a schematic view of an industrial
IoT system of Figures 1 through 5. This includes use
of network coding (including self-organizing network coding) that configures a
network coding model based on feedback measures,
network conditions, or the like, for highly efficient transport of large
amounts of data across the network to and from data collection
systems and the cloud. In the cloud or on an enterprise owner' s or operator'
s premises may be deployed a wide range of capabilities
for intelligence, analytics, remote control, remote operation, remote
optimization, and the like, including a wide range of capabilities
depicted in Figure 1. This includes various storage configurations, which may
include distributed ledger storage, such as for
supporting transactional data or other elements of the system.
[0142] Figures 1, 4, and 5 show the lower right corner of a schematic view of
an industrial IoT system of Figures 1 through 5.
This includes a programmatic data marketplace 70, which may be a self-
organizing marketplace, such as for making available data
that is collected in industrial environments, such as from data collectors,
data pools, distributed ledgers, and other elements disclosed
herein and depicted in Figures 1 through 5. Figures 1, 4, and 5 also show on-
device sensor fusion 80, such as for storing on a device
data from multiple analog sensors 82, which may be analyzed locally or in the
cloud, such as by machine learning 84, including by
training a machine based on initial models created by humans that are
augmented by providing feedback (such as based on measures
of success) when operating the methods and systems disclosed herein.
Additional detail on the various components and sub-
components of Figures 1 through 5 is provided througb¨tthe
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[0143] In embodiments, methods and systems are provided for a system for data
collection, processing, and utilization in an
industrial environment, referred to herein as the platform 100. With reference
to Figure 6, the platform 100 may include a local data
collection system 102, which may be disposed in an environment 104, such as an
industrial environment, for collecting data from
or about the elements of the environment, such as machines, components,
systems, sub-systems, ambient conditions, states,
workflows, processes, and other elements. The platform 100 may connect to or
include portions of the industrial IoT data collection,
monitoring and control system 10 depicted in Figures 1-5.The platform 100 may
include a network data transport system 108, such
as for transporting data to and from the local data collection system 102 over
a network 110, such as to a host processing system
112, such as one that is disposed in a cloud computing environment or on the
premises of an enterprise, or that consists of distributed
components that interact with each other to process data collected by the
local data collection system 102. The host processing
system 112, referred to for convenience in some cases as the host processing
system 112, may include various systems, components,
methods, processes, facilities, and the like for enabling automated, or
automation-assisted processing of the data, such as for
monitoring one or more environments 104 or networks 110 or for remotely
controlling one or more elements in a local environment
104 or in a network 110. The platform 100 may include one or more local
autonomous systems 114, such as for enabling autonomous
behavior, such as reflecting artificial, or machine-based intelligence or such
as enabling automated action based on the applications
of a set of rules or models upon input data from the local data collection
system 102 or from one or more input sources 116, which
may comprise information feeds and inputs from a wide array of sources,
including ones in the local environment 104, in a network
110, in the host processing system 112, or in one or more external systems,
databases, or the like. The platform 100 may include
one or more intelligent systems 118, which may be disposed in, integrated
with, or acting as inputs to one or more components of
the platform 100. Details of these and other components of the platform 100
are provided throughout this disclosure.
[0144] Intelligent systems may include cognitive systems 120, such as enabling
a degree of cognitive behavior as a result of the
coordination of processing elements, such as mesh, peer-to-peer, ring, serial
and other architectures, where one or more node
elements is coordinated with other node elements to provide collective,
coordinated behavior to assist in processing, communication,
data collection, or the like. The MANET 20 depicted in Figure 2 may also use
cognitive radio technologies, including ones that
form up an equivalent to the IP protocol, such as router 42, MAC 44, and
physical layer technologies 46. In one example, the
cognitive system technology stack can include examples disclosed in U.S.
Patent Number 8,060,017 to Schlicht et al., issued 15
November 2011 and hereby incorporated by reference as if fully set forth
herein. Intelligent systems may include machine learning
systems 122, such as for learning on one or more data sets. The one or may
data sets may include information collections using local
data collection systems 102 or other information from input sources 116, such
as to recognize states, objects, events, patterns,
conditions, or the like that may in turn be used for processing by the host
processing system 112 as inputs to components of the
platform 100 and portions of the industrial IoT data collection, monitoring
and control system 10, or the like. Learning may be
human-supervised or fully-automated, such as using one or more input sources
116 to provide a data set, along with information
about the item to be learned. Machine learning may use one or more models,
rules, semantic understandings, workflows, or other
structured or semi-structured understanding of the world, such as for
automated optimization of control of a system or process based
on feedback or feed forward to an operating model for the system or process.
One such machine learning technique for semantic
and contextual understandings, workflows, or other structured or semi-
structured understandings is disclosed in U.S. Patent Number
8,200,775 to Moore, issued 12 June 2012 and hereby incorporated by reference
as if fully set forth herein. Machine learning may
be used to improve the foregoing, such as by adjusting one or more weights,
structures, rules, or the like (such as changing a function
within a model) based on feedback (such as regarding the success of a model in
a given situation) or based on iteration (such as in
a recursive process). Where sufficient understanding of the underlying
structure or behavior of a system is not known, insufficient
data is not available, or in other cases where preferred for various reasons,
machine learning may also be undertaken in the absence
of an underlying model; that is, input sources may be weighted, structured, or
the like within a machine learning facility without
regard to any a priori understanding of structure, and outcomes (such as based
on measures of success at accomplishing various
desired objectives) can be serially fed to the machine learning system to
allow it to learn how to achieve the targeted objectives. For
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example, the system may learn to recognize faults, to recognize patterns, to
develop models or functions, to develop rules, to
optimize performance, to minimize failure rates, to optimize profits, to
optimize resource utilization, to optimize flow (such as of
traffic), or to optimize many other parameters that may be relevant to
successful outcomes (such as in a wide range of environments).
Machine learning may use genetic programming techniques, such as promoting or
demoting one or more input sources, structures,
data types, objects, weights, nodes, links, or other factors based on feedback
(such that successful elements emerge over a series of
generations). For example, alternative available sensor inputs for a data
collection system 102 may be arranged in alternative
configurations and permutations, such that the system may, using genetic
programming techniques over a series of data collection
events, determine what permutations provide successful outcomes based on
various conditions (such as conditions of components
of the platform 100, conditions of the network 110, conditions of a data
collection system 102, conditions of an environment 104),
or the like. In embodiments, local machine learning may turn on or off one or
more sensors in a multi-sensor data collection system
102 in permutations over time, while tracking success outcomes (such as
contributing to success in predicting a failure, contributing
to a performance indicator (such as efficiency, effectiveness, return on
investment, yield, or the like), contributing to optimization
of one or more parameters, identification of a pattern (such as relating to a
threat, a failure mode, a success mode, or the like) or the
like. For example, a system may learn what sets of sensors should be turned on
or off under given conditions to achieve the highest
value utilization of a data collector 102. In embodiments, similar techniques
may be used to handle optimization of transport of data
in the platform 100 (such as in the network 110) by using genetic programming
or other machine learning techniques to learn to
configure network elements (such as configuring network transport paths,
configuring network coding types and architectures,
configuring network security elements), and the like.
[0145] In embodiments, the local data collection system 102 may include a high-
performance, multi-sensor data collector having
a number of novel features for collection and processing of analog and other
sensor data. In embodiments, a local data collection
system 102 may be deployed to the industrial facilities depicted in Figure 3.
A local data collection system 102 may also be deployed
monitor other machines such as the machine 2300 in Figure 9 and Figure 10, the
machines 2400, 2600, 2800, 2950, 3000 depicted
in Figure 12, and the machines 3202, 3204 depicted in Figure 13. The data
collection system 102 may have on board intelligent
systems (such as for learning to optimize the configuration and operation of
the data collector, such as configuring permutations
and combinations of sensors based on contexts and conditions). In one example,
the data collection system 102 includes a crosspoint
switch 130. Automated, intelligent configuration of the local data collection
system 102 may be based on a variety of types of
information, such as from various input sources, such as based on available
power, power requirements of sensors, the value of the
data collected (such as based on feedback information from other elements of
the platform 100), the relative value of information
(such as based on the availability of other sources of the same or similar
information), power availability (such as for powering
sensors), network conditions, ambient conditions, operating states, operating
contexts, operating events, and many others.
[0146] Figure 7 shows elements and sub-components of a data collection and
analysis system 1100 for sensor data (such as analog
sensor data) collected in industrial environments. As depicted in Figure 7,
embodiments of the methods and systems disclosed herein
may include hardware that has several different modules starting with the
multiplexer ("Mux") 1104. In embodiments, the Mux
1104 is made up of a main board 1103 and an option board 1108. The main board
is where the sensors connect to the system. These
connections are on top to enable ease of installation. Then there are numerous
settings on the underside of this board as well as on
the Mux option board, which attaches to the main board via two headers one at
either end of the board. In embodiments, the Mux
option board has the male headers, which mesh together with the female header
on the main Mux board. This enables them to be
stacked on top of each other taking up less real estate.
[0147] In embodiments, the main Mux then connects to the mother (e.g., with 4
simultaneous channels) and daughter (e.g., with
4 additional channels for 8 total channels) analog boards 1110 via cables
where some of the signal conditioning (such as hardware
integration) occurs. The signals then move from the analog boards 1110 to the
anti-aliasing board where some of the potential
aliasing is removed. The rest of the aliasing is done on the delta sigma board
1112, which it connects to through cables. The delta
sigma board 1112 provides more aliasing protection along with other
conditioning and digitizing of the signal. Next, the data moves
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to the JennicTM board 1114 for more digitizing as well as communication to a
computer via USB or Ethernet. In embodiments, the
JennicTM board 1114 may be replaced with a pic board 1118 for more advanced
and efficient data collection as well as
communication. Both the JennicTm board 1114 and the pic board 1118 may feed to
a self sufficient DAQ 1122. Once the data moves
to the computer software 1102, the computer software analysis modules 1128 can
manipulate the data to show trending, spectra,
waveform, statistics, and analytics which may be see and manipulated in the
system GUI 1124. In some cases there may be dedicated
modules for continuous ultrasonic monitoring 1120 or REID monitoring of an
inclinometer in sensor 1130.
[0148] In embodiments, the system is meant to take in all types of data from
volts to 4-20 mA signals. In embodiments, open
formats of data storage and communication may be used. In some instances,
certain portions of the system may be proprietary
especially some of research and data associated with the analytics and
reporting. In embodiments, smart band analysis is a way to
break data down into easily analyzed parts that can be combined with other
smart bands to make new more simplified yet
sophisticated analytics. In embodiments, this unique information is taken and
graphics are used to depict the conditions because
picture depictions are more helpful to the user. In embodiments, complicated
programs and user interfaces are simplified so that any
user can manipulate the data like an expert.
[0149] In embodiments, the system in essence works in a big loop. It starts in
software with a general user interface. Most, if not
all, online systems require the OEM to create or develop the system GUI 1124.
In embodiments, rapid route creation takes advantage
of hierarchical templates. In embodiments, a GUI is created so any general
user can populate the information itself with simple
templates. Once the templates are created the user can copy and paste whatever
the user needs. In addition, users can develop their
own templates for future ease of use and institutionalizing the knowledge.
When the user has entered all of the user's information
and connected all of the user's sensors, the user can then start the system
acquiring data. In some applications, rotating machinery
can build up an electric charge which can harm electrical equipment. In
embodiments, in order to diminish this charge' s effect on
the equipment, a unique electrostatic protection for trigger and vibration
inputs is placed upfront on the Mux and DAQ hardware in
order to dissipate this electric charge as the signal passed from the sensor
to the hardware. In embodiments, the Mux and analog
board also can offer upfront circuitry and wider traces in high-amperage input
capability using solid state relays and design topology
that enables the system to handle high amperage inputs if necessary.
[0150] In embodiments, an important part at the front of the Mux is up front
signal conditioning on Mux for improved signal-to-
noise ratio which provides upfront signal conditioning. Most multiplexers are
after thoughts and the original equipment
manufacturers usually do not worry or even think about the quality of the
signal coming from it. As a result, the signals quality can
drop as much as 30 dB or more. Every system is only as strong as its weakest
link, so no matter if you have a 24 bit DAQ that has
a Sibl ratio of 110 dB, your signal quality has already been lost through the
Mux. If the signal to noise ratio has dropped to 80 dB
in the Mux, it may not be much better than a 16-bit system from 20 years ago.
[0151] In embodiments, in addition to providing a better signal, the
multiplexer also can play a key role in enhancing a system.
Truly continuous systems monitor every sensor all the time but these systems
are very expensive. Multiplexer systems can usually
only monitor a set number of channels at one time and switches from bank to
bank from a larger set of sensors. As a result, the
sensors not being collected on are not being monitored so if a level increases
the user may never know. In embodiments, a
multiplexer continuous monitor alarming feature provides a continuous
monitoring alarming multiplexer by placing circuitry on the
multiplexer that can measure levels against known alarms even when the data
acquisition ("DAQ") is not monitoring the channel.
This in essence makes the system continuous without the ability to instantly
capture data on the problem like a true continuous
system. In embodiments, coupling this capability to alarm with adaptive
scheduling techniques for continuous monitoring and the
continuous monitoring system's software adapting and adjusting the data
collection sequence based on statistics, analytics, data
alarms and dynamic analysis the system will be able to quickly collect dynamic
spectral data on the alarming sensor very soon after
the alarm sounds.
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[0152] In embodiments, the system provides all the same capabilities as onsite
will allow phase-lock-loop band pass tracking filter
method for obtaining slow-speed revolutions per minute ("RPM") and phase for
balancing purposes to remotely balance slow speed
machinery such as in paper mills as well as offer additional analysis from its
data.
[0153] In embodiments, once the signals leave the multiplexer and hierarchical
Mux they move to the analog board where there
are other enhancements. In embodiments, power-down of analog channels when not
in use as well other power-saving measures
including powering down of component boards allow the system to power down
channels on the mother and the daughter analog
boards in order to save power. In embodiments, this can offer the same power
saving benefits to a protect system especially if it is
battery operated or solar powered. In embodiments, in order to maximize the
signal to noise ratio and provide the best data, a peak-
detector for auto-scaling routed into a separate A/D will provide the system
the highest peak in each set of data so it can rapidly
scale the data to that peak. In embodiments, improved integration using both
analog and digital methods create an innovative hybrid
integration which also improves or maintains the highest possible signal to
noise ratio.
[0154] In embodiments, a section of the analog board allows routing of a
trigger channel, either raw or buffered, into other analog
channels. This allows users to route the trigger to any of the channels for
analysis and trouble shooting. In embodiments, once the
signals leave the analog board, the signals move into the delta-sigma board
where precise voltage reference for A/D zero reference
offers more accurate direct current sensor data. The delta sigma's high speeds
also provide for using higher input oversampling for
delta-sigma A/D for lower sampling rate outputs to minimize antialiasing
filter requirements to oversample the data at a higher input
which minimizes anti-aliasing requirements. In embodiments, a CPLD may be used
as a clock-divider for a delta-sigma A/D to
achieve lower sampling rates without the need for digital resampling so the
delta-sigma A/D can achieve lower sampling rates
without digitally resampling the data.
[0155] In embodiments, the data then moves from the delta-sigma board to the
JennicTM board where digital derivation of phase
relative to input and trigger channels using on-board timers digitally derives
the phase from the input signal and the trigger using
on board timers. In embodiments, the JennicTM board also has the ability to
store calibration data and system maintenance repair
history data in an on-board card set. In embodiments, the JennicTM board will
enable acquiring long blocks of data at high-sampling
rate as opposed to multiple sets of data taken at different sampling rates so
it can stream data and acquire long blocks of data for
advanced analysis in the future.
[0156] In embodiments, after the signal moves through the JennicTM board it is
then transmitted to the computer. Once on the
computer, the software has a number of enhancements that improve the systems
analytic capabilities. In embodiments, rapid route
creation takes advantage of hierarchical templates and provides rapid route
creation of all the equipment using simple templates
which also speeds up the software deployment. In embodiments, the software
will be used to add intelligence to the system. It will
start with an expert system GUIs graphical approach to defining smart bands
and diagnoses for the expert system, which will offer
a graphical expert system with simplified user interface so anyone can develop
complex analytics. In embodiments, this user
interface will revolve around smart bands, which are a simplified approach to
complex yet flexible analytics for the general user. In
embodiments, the smart bands will pair with a self-learning neural network for
an even more advanced analytical approach. In
embodiments, this system will also use the machine's hierarchy for additional
analytical insight. One critical part of predictive
maintenance is the ability to learn from known information during repairs or
inspections. In embodiments, graphical approaches for
back calculations may improve the smart bands and correlations based on a
known fault or problem.
[0157] In embodiments, besides detailed analysis via smart bands, a bearing
analysis method is provided. In recent years, there
has been a strong drive in industry to save power which has resulted in an
influx of variable frequency drives. In embodiments,
torsional vibration detection and analysis utilizing transitory signal
analysis provides an advanced torsional vibration analysis for a
more comprehensive way to diagnose machinery where torsional forces are
relevant (such as machinery with rotating components).
In embodiments, the system can deploy a number of intelligent capabilities on
its own for better data and more comprehensive
analysis. In embodiments, this intelligence will start with a smart route
where the software's smart route can adapt the sensors it
collects simultaneously in order to gain additional correintive intelli genre
In emilndiments, smart operational data store ("ODS")
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allows the system to elect to gather operational deflection shape analysis in
order to further examine the machinery condition. In
embodiments, besides changing the route, adaptive scheduling techniques for
continuous monitoring allow the system to change the
scheduled data collected for full spectral analysis across a number (e.g.,
eight), of correlative channels. The systems intelligence
will provide data to enable extended statistics capabilities for continuous
monitoring as well as ambient local vibration for analysis
that combines ambient temperature and local temperature and vibration levels
changes for identifying machinery issues.
[0158] Embodiments of the methods and systems disclosed herein may include a
self-sufficient DAQ box 1122. In embodiments,
a data acquisition device may be controlled by a personal computer (PC) to
implement the desired data acquisition commands. In
embodiments, the system has the ability to be self-sufficient and can acquire,
process, analyze and monitor independent of external
PC control. Embodiments of the methods and systems disclosed herein may
include secure digital (SD) card storage. In
embodiments, significant additional storage capability is provided utilizing
an SD card such as cameras, smart phones, and so on.
This can prove critical for monitoring applications where critical data can be
stored permanently. Also, if a power failure should
occur, the most recent data may be stored despite the fact that it was not off-
loaded to another system. Embodiments of the methods
and systems disclosed herein may include a DAQ system. A current trend has
been to make DAQ systems as communicative as
possible with the outside world usually in the form of networks including
wireless. Whereas in the past it was common to use a
dedicated bus to control a DAQ system with either a microprocessor or
microcontroller/microprocessor paired with a PC, today the
demands for networking are much greater and so it is out of this environment
that arises this new design prototype. In embodiments,
multiple microprocessor/microcontrollers or dedicated processors may be
utilized to carry out various aspects of this increase in
DAQ functionality with one or more processor units focused primarily on the
communication aspects with the outside world. This
negates the need for constantly interrupting the main processes which include
the control of the signal conditioning circuits,
triggering, raw data acquisition using the A/D, directing the A/D output to
the appropriate on-board memory and processing that
data. In embodiments, a specialized microcontroller/microprocessor is
designated for all communications with the outside. These
include USB, Ethernet and wireless with the ability to provide an IP address
or addresses in order to host a webpage. All
communications with the outside world are then accomplished using a simple
text based menu. The usual array of commands (in
practice more than a hundred) such as InitializeCard, AcquireData,
StopAcquisition, RetrieveCalibration Info, and so on, would be
provided. In addition, in embodiments, other intense signal processing
activities including resampling, weighting, filtering, and
spectrum processing can be performed by dedicated processors such as field-
programmable gate array ("FPGAs"), digital signal
processor ("DSP"), microprocessors, micro-controllers, or a combination
thereof. In embodiments, this subsystem will communicate
via a specialized hardware bus with the communication processing section. It
will be facilitated with dual-port memory, semaphore
logic, and so on. This embodiment will not only provide a marked improvement
in efficiency but can significantly improve the
processing capability, including the streaming of the data as well other high-
end analytical techniques.
[0159] Embodiments of the methods and systems disclosed herein may include
sensor overload identification. A need exists for
monitoring systems to identify when the sensor is overloading. A monitoring
system may identify when their system is overloading,
but in embodiments, the system may look at the voltage of the sensor to
determine if the overload is from the sensor, which is useful
to the user to get another sensor better suited to the situation, or the user
can try to gather the data again. There are often situations
involving high frequency inputs that will saturate a standard 100 mv/g sensor
(which is most commonly used in the industry) and
having the ability to sense the overload improves data quality for better
analysis.
[0160] Embodiments of the methods and systems disclosed herein may include up
front signal conditioning on Mux for improved
signal-to-noise ratio. Embodiments may perform signal conditioning (such as
range/gain control, integration, filtering, etc.) on
vibration as well as other signal inputs up front before Mux switching to
achieve the highest signal-to-noise ratio.
[0161] Embodiments of the methods and systems disclosed herein may include a
Mux continuous monitor alarming feature. In
embodiments, continuous monitoring Mux bypass offers a mechanism whereby
channels not being currently sampled by the Mux
system may be continuously monitored for significant alarm conditions via a
number of trigger conditions using filtered peak-hold
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circuits or functionally similar that are in turn passed on to the monitoring
system in an expedient manner using hardware interrupts
or other means.
[0162] Embodiments of the methods and systems disclosed herein may include use
of distributed CPLD chips with dedicated bus
for logic control of multiple Mux and data acquisition sections. Interfacing
to multiple types of predictive maintenance and vibration
transducers requires a great deal of switching. This includes AC/DC coupling,
4-20 interfacing, integrated electronic piezoelectric
transducer, channel power-down (for conserving op amp power), single-ended or
differential grounding options, and so on. Also
required is the control of digital pots for range and gain control, switches
for hardware integration, AA filtering and triggering. This
logic can be performed by a series of CPLD chips strategically located for the
tasks they control. A single giant CPLD requires long
circuit routes with a great deal of density at the single giant CPLD. In
embodiments, distributed CPLDs not only address these
concerns but offer a great deal of flexibility. A bus is created where each
CPLD that has a fixed assignment has its own unique
device address. For multiple boards (e.g., for multiple Mux boards), jumpers
are provided for setting multiple addresses. In another
example, three bits permit up to 8 boards that are jumper configurable. In
embodiments, a bus protocol is defined such that each
CPLD on the bus can either be addressed individually or as a group.
[0163] Embodiments of the methods and systems disclosed herein may include
high-amperage input capability using solid state
relays and design topology. Typically, vibration data collectors are not
designed to handle large input voltages due to the expense
and the fact that, more often than not, it is not needed. A need exists for
these data collectors to acquire many varied types of PM
data as technology improves and monitoring costs plummet. In embodiments, a
method is using the already established OptoMOSTm
technology which permits the switching up front of high voltage signals rather
than using more conventional reed-relay approaches.
Many historic concerns regarding non-linear zero crossing or other non-linear
solid-state behaviors have been eliminated with regard
to the passing through of weakly buffered analog signals. In addition, in
embodiments, printed circuit board routing topologies place
all of the individual channel input circuitry as close to the input connector
as possible.
[0164] Embodiments of the methods and systems disclosed herein may include
unique electrostatic protection for trigger and
vibration inputs. In many critical industrial environments where large
electrostatic forces may build up, for example low-speed
balancing using large belts, proper transducer and trigger input protection is
required. In embodiments, a low-cost but efficient
method is described for such protection without the need for external
supplemental devices.
[0165] Embodiments of the methods and systems disclosed herein may include
precise voltage reference for A/D zero reference.
Some A/D chips provide their own internal zero voltage reference to be used as
a mid-scale value for external signal conditioning
circuitry to ensure that both the A/D and external op amps use the same
reference. Although this sounds reasonable in principle,
there are practical complications. In many cases these references are
inherently based on a supply voltage using a resistor-divider.
For many current systems, especially those whose power is derived from a PC
via USB or similar bus, this provides for an unreliable
reference, as the supply voltage will often vary quite significantly with
load. This is especially true for delta-sigma A/D chips which
necessitate increased signal processing. Although the offsets may drift
together with load, a problem arises if one wants to calibrate
the readings digitally. It is typical to modify the voltage offset expressed
as counts coming from the A/D digitally to compensate
for the DC drift. However, for this case, if the proper calibration offset is
determined for one set of loading conditions, they will not
apply for other conditions. An absolute DC offset expressed in counts will no
longer be applicable. As a result, it becomes necessary
to calibrate for all loading conditions which becomes complex, unreliable, and
ultimately unmanageable. In embodiments, an
external voltage reference is used which is simply independent of the supply
voltage to use as the zero offset.
[0166] Embodiments of the methods and systems disclosed herein may include
phase-lock-loop band pass tracking filter method
for obtaining slow-speed RPMs and phase for balancing purposes. For balancing
purposes, it is sometimes necessary to balance at
very slow speeds. A typical tracking filter may be constructed based on a
phase-lock loop or PLL design. However, stability and
speed range are overriding concerns. In embodiments, a number of digitally
controlled switches are used for selecting the appropriate
RC and damping constants. The switching can be done all automatically after
measuring the frequency of the incoming tach signal.
Embodiments of the methods and systems disclosed herein may include di gital
derivation of phase relative to input and trigger
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channels using on-board timers. In embodiments, digital phase derivation uses
digital timers to ascertain an exact delay from a
trigger event to the precise start of data acquisition. This delay, or offset,
then, is further refined using interpolation methods to
obtain an even more precise offset which is then applied to the analytically
determined phase of the acquired data such that the
phase is "in essence" an absolute phase with precise mechanical meaning useful
for among other things, one-shot balancing,
alignment analysis, and so on.
[0167] Embodiments of the methods and systems disclosed herein may include
peak-detector for auto-scaling routed into separate
A/D. Many microprocessors in use today feature built-in A/D converters. For
vibration analysis purposes, they are more often than
not inadequate with regards to number of bits, number of channels or sampling
frequency versus not slowing the microprocessor
down significantly. Despite these limitations, it is useful to use them for
purposes of auto-scaling. In embodiments, a separate A/D
may be used that has reduced functionality and is cheaper. For each channel of
input, after the signal is buffered (usually with the
appropriate coupling: AC or DC) but before it is signal conditioned, the
signal is fed directly into the microprocessor or low-cost
A/D. Unlike the conditioned signal for which range, gain and filter switches
are thrown, no switches are varied. This permits the
simultaneous sampling of the auto-scaling data while the input data is signal
conditioned, fed into a more robust external A/D, and
directed into on-board memory using direct memory access (DMA) methods where
memory is accessed without requiring a CPU.
This significantly simplifies the auto-scaling process by not having to throw
switches and then allow for settling time, which greatly
slows down the auto-scaling process. Furthermore, the data can be collected
simultaneously, which assures the best signal-to-noise
ratio. The reduced number of bits and other features is usually more than
adequate for auto-scaling purposes.
[00168] Embodiments of the methods and systems disclosed herein may include
using higher input oversampling for delta-sigma
A/D for lower sampling rate outputs to minimize AA filter requirements. In
embodiments, higher input oversampling rates for delta-
sigma A/D are used for lower sampling rate output data to minimize the AA
filtering requirements. Lower oversampling rates can
be used for higher sampling rates. For example, a 3' order AA filter set for
the lowest sampling requirement for 256 Hz (Fmax of
100 Hz) is then adequate for Fmax ranges of 200 and 500 Hz. Another higher-
cutoff AA filter can then be used for Fmax ranges
from 1 kHz and higher (with a secondary filter kicking in at 2.56x the highest
sampling rate of 128 kHz). Embodiments of the
methods and systems disclosed herein may include use of a CPLD as a clock-
divider for a delta-sigma A/D to achieve lower
sampling rates without the need for digital resampling. In embodiments, a high-
frequency crystal reference can be divided down to
lower frequencies by employing a CPLD as a programmable clock divider. The
accuracy of the divided down lower frequencies is
even more accurate than the original source relative to their longer time
periods. This also minimizes or removes the need for
resampling processing by the delta-sigma A/D.
[00169] Embodiments of the methods and systems disclosed herein may include
storage of calibration data and maintenance history
on-board card sets. Many data acquisition devices which rely on interfacing to
a PC to function store their calibration coefficients
on the PC. This is especially true for complex data acquisition devices whose
signal paths are many and therefore whose calibration
tables can be quite large. In embodiments, calibration coefficients are stored
in flash memory which will remember this data or any
other significant information for that matter, for all practical purposes,
permanently. This information may include nameplate
information such as serial numbers of individual components, firmware or
software version numbers, maintenance history, and the
calibration tables. In embodiments, no matter which computer the box is
ultimately connected to, the DAQ box remains calibrated
and continues to hold all of this critical information. The PC or external
device may poll for this information at any time for
implantation or information exchange purposes.
[0170] Embodiments of the methods and systems disclosed herein may include a
graphical approach for back-calculation
definition. In embodiments, the expert system also provides the opportunity
for the system to learn. If one already knows that a
unique set of stimuli or smart bands corresponds to a specific fault or
diagnosis, then it is possible to back-calculate a set of
coefficients that when applied to a future set of similar stimuli would arrive
at the same diagnosis. In embodiments, if there are
multiple sets of data a best-fit approach may be used. Unlike the smart band
GUI, this embodiment will self-generate a wiring
diagram. In embodiments, the user may tailor the back-propagation approach
settings and use a database browser to match specific
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sets of data with the desired diagnoses. In embodiments, the desired diagnoses
may be created or custom tailored with a smart band
GUI. In embodiments, after that, a user may press the GENERATE button and a
dynamic wiring of the symptom-to-diagnosis may
appear on the screen as it works through the algorithms to achieve the best
fit. In embodiments, when complete, a variety of statistics
are presented which detail how well the mapping process proceeded. In some
cases, no mapping may be achieved if, for example,
the input data was all zero or the wrong data (mistakenly assigned) and so on.
Embodiments of the methods and systems disclosed
herein may include bearing analysis methods. In embodiments, bearing analysis
methods may be used in conjunction with a
computer aided design ("CAD"), predictive deconvolution, minimum variance
distortionless response ("MVDR") and spectrum
sum-of-harmonics.
[0171] Embodiments of the methods and systems disclosed herein may include
torsional vibration detection and analysis utilizing
transitory signal analysis. There has been a marked trend in recent times
regarding the prevalence of variable speed machinery. Due
primarily to the decrease in cost of motor speed control systems, as well as
the increased cost and consciousness of energy-usage,
it has become more economically justifiable to take advantage of the
potentially vast energy savings of load control. Unfortunately,
one frequently overlooked design aspect of this issue is that of vibration.
When a machine is designed to run at only one speed, it is
far easier to design the physical structure accordingly so as to avoid
mechanical resonances both structural and torsional, each of
which can dramatically shorten the mechanical health of a machine. This would
include such structural characteristics as the types
of materials to use, their weight, stiffening member requirements and
placement, bearing types, bearing location, base support
constraints, etc. Even with machines running at one speed, designing a
structure so as to minimize vibration can prove a daunting
task, potentially requiring computer modeling, finite-element analysis, and
field testing. By throwing variable speeds into the mix,
in many cases, it becomes impossible to design for all desirable speeds. The
problem then becomes one of minimization, e.g., by
speed avoidance. This is why many modern motor controllers are typically
programmed to skip or quickly pass through specific
speed ranges or bands. Embodiments may include identifying speed ranges in a
vibration monitoring system. Non-torsional,
structural resonances are typically fairly easy to detect using conventional
vibration analysis techniques. However, this is not the
case for torsion. One special area of current interest is the increased
incidence of torsional resonance problems, apparently due to
the increased torsional stresses of speed change as well as the operation of
equipment at torsional resonance speeds. Unlike non-
torsional structural resonances which generally manifest their effect with
dramatically increased casing or external vibration,
torsional resonances generally show no such effect. In the case of a shaft
torsional resonance, the twisting motion induced by the
resonance may only be discernible by looking for speed and/or phase changes.
The current standard methodology for analyzing
torsional vibration involves the use of specialized instrumentation. Methods
and systems disclosed herein allow analysis of torsional
vibration without such specialized instrumentation. This may consist of
shutting the machine down and employing the use of strain
gauges and/or other special fixturing such as speed encoder plates and/or
gears. Friction wheels are another alternative but they
typically require manual implementation and a specialized analyst. In general,
these techniques can be prohibitively expensive
and/or inconvenient. An increasing prevalence of continuous vibration
monitoring systems due to decreasing costs and increasing
convenience (e.g., remote access) exists. In embodiments, there is an ability
to discern torsional speed and/or phase variations with
just the vibration signal. In embodiments, transient analysis techniques may
be utilized to distinguish torsionally induced vibrations
from mere speed changes due to process control. In embodiments, factors for
discernment might focus on one or more of the
following aspects: the rate of speed change due to variable speed motor
control would be relatively slow, sustained and deliberate;
torsional speed changes would tend to be short, impulsive and not sustained;
torsional speed changes would tend to be oscillatory,
most likely decaying exponentially, process speed changes would not; and
smaller speed changes associated with torsion relative to
the shaft's rotational speed which suggest that monitoring phase behavior
would show the quick or transient speed bursts in contrast
to the slow phase changes historically associated with ramping a machine's
speed up or down (as typified with Bode or Nyquist
plots).
[0172] With reference to Figure 8, the present disclosure generally includes
digitally collecting or streaming waveform data 2010
from a machine 2020 whose operational speed can vary from relatively slow
rotational or oscillational speeds to much higher speeds
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in different situations. The waveform data 2010, at least on one machine, may
include data from a single axis sensor 2030 mounted
at an unchanging reference location 2040 and from a three-axis sensor 2050
mounted at changing locations (or located at multiple
locations), including location 2052. In embodiments, the waveform data 2010
can be vibration data obtained simultaneously from
each sensor 2030, 2050 in a gap-free format for a duration of multiple minutes
with maximum resolvable frequencies sufficiently
large to capture periodic and transient impact events. By way of this example,
the waveform data 2010 can include vibration data
that can be used to create an operational deflecting shape. It can also be
used, as needed, to diagnose vibrations from which a
machine repair solution can be prescribed.
[0173] In embodiments, the machine 2020 can further include a housing 2100
that can contain a drive motor 2110 that can drive
a drive shaft 2120. The drive shaft 2120 can be supported for rotation or
oscillation by a set of bearings 2130, such as including a
first bearing 2140 and a second bearing 2150. A data collection module 2160
can connect to (or be resident on) the machine 2020.
In one example, the data collection module 2160 can be located and accessible
through a cloud network facility 2170, can collect
the waveform data 2010 from the machine 2020, and deliver the waveform data
2010 to a remote location. A working end 2180 of
the drive shaft 2120 of the machine 2020 can drive a windmill, a fan, a pump,
a drill, a gear system, a drive system, or other working
element, as the techniques described herein can apply to a wide range of
machines, equipment, tools, or the like that include rotating
or oscillating elements. In other instances, a generator can be substituted
for the drive motor 2110, and the working end of the drive
shaft 2120 can direct rotational energy to the generator to generate power,
rather than consume it.
[0174] In embodiments, the waveform data 2010 can be obtained using a
predetermined route format based on the layout of the
machine 2020. The waveform data 2010 may include data from the single axis
sensor 2030 and the three-axis sensor 2050. The
single-axis sensor 2030 can serve as a reference probe with its one channel of
data and can be fixed at the unchanging location 2040
on the machine under survey. The three-axis sensor 2050 can serve as a tri-
axial probe (e.g., three orthogonal axes) with its three
channels of data and can be moved along a predetermined diagnostic route
format from one test point to the next test point. In one
example, both sensors 2030, 2050 can be mounted manually to the machine 2020
and can connect to a separate portable computer
in certain service examples. The reference probe can remain at one location
while the user can move the tri-axial vibration probe
along the predetermined route, such as from bearing-to-bearing on a machine.
In this example, the user is instructed to locate the
sensors at the predetermined locations to complete the survey (or portion
thereof) of the machine.
[0175] With reference to Figure 9, a portion of an exemplary machine 2200 is
shown having a tri-axial sensor 2210 mounted to a
location 2220 associated with a motor bearing of the machine 2200 with an
output shaft 2230 and output member 2240 in accordance
with the present disclosure. With reference to Figure 9 and Figure 10, an
exemplary machine 2300 is shown having a tri-axial sensor
2310 and a single-axis vibration sensor 2320 serving as the reference sensor
that is attached on the machine 2300 at an unchanging
location for the duration of the vibration survey in accordance with the
present disclosure. The tri-axial sensor 2310 and the single-
axis vibration sensor 2320 can be connected to a data collection system 2330
[0176] In further examples, the sensors and data acquisition modules and
equipment can be integral to, or resident on, the rotating
machine. By way of these examples, the machine can contain many single axis
sensors and many tri-axial sensors at predetermined
locations. The sensors can be originally installed equipment and provided by
the original equipment manufacturer or installed at a
different time in a retrofit application. The data collection module 2160, or
the like, can select and use one single axis sensor and
obtain data from it exclusively during the collection of waveform data 2010
while moving to each of the tri-axial sensors. The data
collection module 2160 can be resident on the machine 2020 and/or connect via
the cloud network facility 2170
[0177] With reference to Figure 8, the various embodiments include collecting
the waveform data 2010 by digitally recording
locally, or streaming over, the cloud network facility 2170. The waveform data
2010 can be collected so as to be gap-free with no
interruptions and, in some respects, can be similar to an analog recording of
waveform data. The waveform data 2010 from all of
the channels can be collected for one to two minutes depending on the rotating
or oscillating speed of the machine being monitored.
In embodiments, the data sampling rate can be at a relatively high sampling
rate relative to the operating frequency of the machine
2020.
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[0178] In embodiments, a second reference sensor can be used, and a fifth
channel of data can be collected. As such, the single-
axis sensor can be the first channel and tri-axial vibration can occupy the
second, the third, and the fourth data channels. This second
reference sensor, like the first, can be a single axis sensor, such as an
accelerometer. In embodiments, the second reference sensor,
like the first reference sensor, can remain in the same location on the
machine for the entire vibration survey on that machine. The
location of the first reference sensor (i.e., the single axis sensor) may be
different than the location of the second reference sensors
(i.e., another single axis sensor). In certain examples, the second reference
sensor can be used when the machine has two shafts with
different operating speeds, with the two reference sensors being located on
the two different shafts. In accordance with this example,
further single-axis reference sensors can be employed at additional but
different unchanging locations associated with the rotating
machine.
[0179] In embodiments, the waveform data can be transmitted electronically in
a gap-free free format at a significantly high rate
of sampling for a relatively longer period of time. In one example, the period
of time is 60 seconds to 120 seconds. In another
example, the rate of sampling is 100 kHz with a maximum resolvable frequency
(Fmax) of 40 kHz. It will be appreciated in light of
this disclosure that the waveform data can be shown to approximate more
closely some of the wealth of data available from previous
instances of analog recording of waveform data.
[0180] In embodiments, sampling, band selection, and filtering techniques can
permit one or more portions of a long stream of
data (i.e., one to two minutes in duration) to be under sampled or over
sampled to realize varying effective sampling rates. To this
end, interpolation and decimation can be used to further realize varying
effective sampling rates. For example, oversampling may
be applied to frequency bands that are proximal to rotational or oscillational
operating speeds of the sampled machine, or to
harmonics thereof, as vibration effects may tend to be more pronounced at
those frequencies across the operating range of the
machine. In embodiments, the digitally-sampled data set can be decimated to
produce a lower sampling rate. It will be appreciated
in light of the disclosure that decimate in this context can be the opposite
of interpolate. In embodiments, decimating the data set
can include first applying a low-pass filter to the digitally-sampled data set
and then undersampling the data set.
[0181] In one example, a sample waveform at 100 Hz can be undersampled at
every tenth point of the digital waveform to produce
an effective sampling rate of 10 Hz, but the remaining nine points of that
portion of the waveform are effectively discarded and not
included in the modeling of the sample waveform. Moreover, this type of bare
undersampling can create ghost frequencies due to
the undersampling rate (i.e., 10 Hz) relative to the 100 Hz sample waveform.
[0182] Most hardware for analog to digital conversions use a sample-and-hold
circuit that can charge up a capacitor for a given
amount of time such that an average value of the waveform is determined over a
specific change in time. It will be appreciated in
light of the disclosure that the value of the waveform over the specific
change in time in not linear but more similar to a cardinal
sinusoidal ("sinc") function; and, therefore, it can be shown that more
emphasis can be placed on the waveform data at the center
of the sampling interval with exponential decay of the cardinal sinusoidal
signal occurring from its center.
[0183] By way of the above example, the sample waveform at 100 Hz can be
hardware-sampled at 10 Hz and therefore each
sampling point is averaged over 100 milliseconds (e.g., a signal sampled at
100 Hz can have each point averaged over 10
milliseconds). In contrast to the effective discarding of nine out of the ten
data points of the sampled waveform as discussed above,
the present disclosure can include weighing adjacent data. The adjacent data
can include refers to the sample points that were
previously discarded and the one remaining point that was retained. In one
example, a low pass filter can average the adjacent
sample data linearly, i.e., determining the sum of every ten points and then
dividing that sum by ten. In a further example, the
adjacent data can be weighted with a sinc function. The process of weighting
the original waveform with the sinc function can be
referred to as an impulse function, or can be referred to in the time domain
as a convolution.
[0184] The present disclosure can be applicable to not only digitizing a
waveform signal based on a detected voltage, but can also
be applicable to digitizing waveform signals based on current waveforms,
vibration waveforms, and image processing signals
including video signal rasterization. In one example, the resizing of a window
on a computer screen can be decimated, albeit in at
least two directions. In these further examples, it will be appreciated that
undersampling by itself can be shown to be insufficient.
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To that end, oversampling or upsampling by itself can similarly be shown to be
insufficient, such that interpolation can be used like
decimation but in lieu of only undersampling by itself.
[0185] It will be appreciated in light of the disclosure that interpolation in
this context can refer to first applying a low pass filter
to the digitally-sampled waveform data and then upsampling the waveform data.
It will be appreciated in light of the disclosure that
real-world examples can often require the use of use non-integer factors for
decimation or interpolation, or both. To that end, the
present disclosure includes interpolating and decimating sequentially in order
to realize a non-integer factor rate for interpolating
and decimating. In one example, interpolating and decimating sequentially can
define applying a low-pass filter to the sample
waveform, then interpolating the waveform after the low-pass filter, and then
decimating the waveform after the interpolation. In
embodiments, the vibration data can be looped to purposely emulate
conventional tape recorder loops, with digital filtering
techniques used with the effective splice to facilitate longer analyses. It
will be appreciated in light of the disclosure that the above
techniques do not preclude waveform, spectrum, and other types of analyses to
be processed and displayed with a GUI of the user
at the time of collection. It will be appreciated in light of the disclosure
that newer systems can permit this functionality to be
performed in parallel to the high-performance collection of the raw waveform
data.
[0186] With respect to time of collection issues, it will be appreciated that
older systems using the compromised approach of
improving data resolution, by collecting at different sampling rates and data
lengths, do not in fact save as much time as expected.
To that end, every time the data acquisition hardware is stopped and started,
latency issues can be created, especially when there is
hardware auto-scaling performed. The same can be true with respect to data
retrieval of the route information (i.e., test locations)
that is often in a database format and can be exceedingly slow. The storage of
the raw data in bursts to disk (whether solid state or
otherwise) can also be undesirably slow.
[0187] In contrast, the many embodiments include digitally streaming the
waveform data 2010, as disclosed herein, and also
enjoying the benefit of needing to load the route parameter information while
setting the data acquisition hardware only once.
Because the waveform data 2010 is streamed to only one file, there is no need
to open and close files, or switch between loading
and writing operations with the storage medium. It can be shown that the
collection and storage of the waveform data 2010, as
described herein, can be shown to produce relatively more meaningful data in
significantly less time than the traditional batch data
acquisition approach. An example of this includes an electric motor about
which waveform data can be collected with a data length
of 4K points (i.e., 4,096) for sufficiently high resolution in order to, among
other things, distinguish electrical sideband frequencies.
For fans or blowers, a reduced resolution of 1K (i.e., 1,024) can be used. In
certain instances, 1K can be the minimum waveform
data length requirement. The sampling rate can be 1,280 Hz and that equates to
an Fmax of 500 Hz. It will be appreciated in light
of the disclosure that oversampling by an industry standard factor of 2.56 can
satisfy the necessary two-times (2x) oversampling for
the Nyquist Criterion with some additional leeway that can accommodate anti-
aliasing filter-rolloff. The time to acquire this
waveform data would be 1,024 points at 1,280 hertz, which are 800
milliseconds.
[0188] To improve accuracy, the waveform data can be averaged. Eight averages
can be used with, for example, fifty percent
overlap. This would extend the time from 800 milliseconds to 3.6 seconds,
which is equal to 800 msec x 8 averages x 0.5 (overlap
ratio) + 0.5 x 800 msec (non-overlapped head and tail ends). After collection
at Fmax = 500 Hz waveform data, a higher sampling
rate can be used. In one example, ten times (10x) the previous sampling rate
can be used and Fmax = 10 kHz. By way of this
example, eight averages can be used with fifty percent (50%) overlap to
collect waveform data at this higher rate that can amount
to a collection time of 360 msec or 0.36 seconds. It will be appreciated in
light of the disclosure that it can be necessary to read the
hardware collection parameters for the higher sampling rate from the route
list, as well as permit hardware auto-scaling, or the
resetting of other necessary hardware collection parameters, or both. To that
end, a few seconds of latency can be added to
accommodate the changes in sampling rate. In other instances, introducing
latency can accommodate hardware autoscaling and
changes to hardware collection parameters that can be required when using the
lower sampling rate disclosed herein. In addition to
accommodating the change in sampling rate, additional time is needed for
reading the route point information from the database
(i.e., where to monitor and where to monitor next), displaying the route
information, and processing the waveform data. Moreover,
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display of the waveform data and/or associated spectra can also consume
significant time. In light of the above, 15 seconds to 20
seconds can elapse while obtaining waveform data at each measurement point.
[0189] The present disclosure includes the use of at least one of the single-
axis reference probe on one of the channels to allow
for acquisition of relative phase comparisons between channels. The reference
probe can be an accelerometer or other type of
transducer that is not moved and, therefore, fixed at an unchanging location
during the vibration survey of one machine. Multiple
reference probes can each be deployed as at suitable locations fixed in place
(i.e., at unchanging locations) throughout the acquisition
of vibration data during the vibration survey. In certain examples, up to
seven reference probes can be deployed depending on the
capacity of the data collection module 2160 or the like. Using transfer
functions or similar techniques, the relative phases of all
channels may be compared with one another at all selected frequencies. By
keeping the one or more reference probes fixed at their
unchanging locations while moving or monitoring the other tri-axial vibration
sensors, it can be shown that the entire machine can
be mapped with regard to amplitude and relative phase. This can be shown to be
true even when there are more measurement points
than channels of data collection. With this information, an operating
deflection shape can be created that can show dynamic
movements of the machine in 3 D, which can provide an invaluable diagnostic
tool. In embodiments, the one or more reference
probes can provide relative phase, rather than absolute phase. It will be
appreciated in light of the disclosure that relative phase may
not be as valuable absolute phase for some purposes, but the relative phase
the information can still be shown to be very useful.
[0190] In embodiments, the sampling rates used during the vibration survey can
be digitally synchronized to predetermined
operational frequencies that can relate to pertinent parameters of the machine
such as rotating or oscillating speed. Doing this,
permits extracting even more information using synchronized averaging
techniques. It will be appreciated in light of the disclosure
that this can be done without the use of a key phasor or a reference pulse
from a rotating shaft, which is usually not available for
route collected data. As such, non-synchronous signals can be removed from a
complex signal without the need to deploy
synchronous averaging using the key phasor. This can be shown to be very
powerful when analyzing a particular pinon in a gearbox
or generally applied to any component within a complicated mechanical
mechanism. In many instances, the key phasor or the
reference pulse is rarely available with route collected data, but the
techniques disclosed herein can overcome this absence. In
embodiments, there can be multiple shafts running at different speeds within
the machine being analyzed. In certain instances, there
can be a single-axis reference probe for each shaft. In other instances, it is
possible to relate the phase of one shaft to another shaft
using only one single axis reference probe on one shaft at its unchanging
location. In embodiments, variable speed equipment can
be more readily analyzed with relatively longer duration of data relative to
single speed equipment. The vibration survey can be
conducted at several machine speeds within the same contiguous set of
vibration data using the same techniques disclosed herein.
These techniques can also permit the study of the change of the relationship
between vibration and the change of the rate of speed
that was not available before.
[0191] In embodiments, there are numerous analytical techniques that can
emerge from because raw waveform data can be
captured in a gap-free digital format as disclosed herein. The gap-free
digital format can facilitate many paths to analyze the
waveform data in many ways after the fact to identify specific problems. The
vibration data collected in accordance with the
techniques disclosed herein can provide the analysis of transient, semi-
periodic and very low frequency phenomena. The waveform
data acquired in accordance with the present disclosure can contain relatively
longer streams of raw gap-free waveform data that
can be conveniently played back as needed, and on which many and varied
sophisticated analytical techniques can be performed. A
large number of such techniques can provide for various forms of filtering to
extract low amplitude modulations from transient
impact data that can be included in the relatively longer stream of raw gap-
free waveform data. It will be appreciated in light of the
disclosure that in past data collection practices, these types of phenomena
were typically lost by the averaging process of the spectral
processing algorithms because the goal of the previous data acquisition module
was purely periodic signals; or these phenomena
were lost to file size reduction methodologies due to the fact that much of
the content from an original raw signal was typically
discarded knowing it would not be used.
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[0192] In embodiments, there is a method of monitoring vibration of a machine
having at least one shaft supported by a set of
bearings. The method includes monitoring a first data channel assigned to a
single-axis sensor at an unchanging location associated
with the machine. The method also includes monitoring a second, third, and
fourth data channel assigned to a three-axis sensor. The
method further includes recording gap-free digital waveform data
simultaneously from all of the data channels while the machine
is in operation; and determining a change in relative phase based on the
digital waveform data. The method also includes the tri-
axial sensor being located at a plurality of positions associated with the
machine while obtaining the digital waveform. In
embodiments, the second, third, and fourth channels are assigned together to a
sequence of tri-axial sensors each located at different
positions associated with the machine. In embodiments, the data is received
from all of the sensors on all of their channels
simultaneously.
[0193] The method also includes determining an operating deflection shape
based on the change in relative phase information and
the waveform data. In embodiments, the unchanging location of the reference
sensor is a position associated with a shaft of the
machine. In embodiments, the tri-axial sensors in the sequence of the tri-
axial sensors are each located at different positions and are
each associated with different bearings in the machine. In embodiments, the
unchanging location is a position associated with a
shaft of the machine and, wherein, the tri-axial sensors in the sequence of
the tri-axial sensors are each located at different positions
and are each associated with different bearings that support the shaft in the
machine. The various embodiments include methods of
sequentially monitoring vibration or similar process parameters and signals of
a rotating or oscillating machine or analogous process
machinery from a number of channels simultaneously, which can be known as an
ensemble. In various examples, the ensemble can
include one to eight channels. In further examples, an ensemble can represent
a logical measurement grouping on the equipment
being monitored whether those measurement locations are temporary for
measurement, supplied by the original equipment
manufacturer, retrofit at a later date, or one or more combinations thereof.
[0194] In one example, an ensemble can monitor bearing vibration in a single
direction. In a further example, an ensemble can
monitor three different directions (e.g., orthogonal directions) using a tri-
axial sensor. In yet further examples, an ensemble can
monitor four or more channels where the first channel can monitor a single
axis vibration sensor, and the second, the third, and the
fourth channels can monitor each of the three directions of the tri-axial
sensor. In other examples, the ensemble can be fixed to a
group of adjacent bearings on the same piece of equipment or an associated
shaft. The various embodiments provide methods that
include strategies for collecting waveform data from various ensembles
deployed in vibration studies or the like in a relatively more
efficient manner. The methods also include simultaneously monitoring of a
reference channel assigned to an unchanging reference
location associated with the ensemble monitoring the machine. The cooperation
with the reference channel can be shown to support
a more complete correlation of the collected waveforms from the ensembles. The
reference sensor on the reference channel can be
a single axis vibration sensor, or a phase reference sensor that can be
triggered by a reference location on a rotating shaft or the like.
As disclosed herein, the methods can further include recording gap-free
digital waveform data simultaneously from all of the
channels of each ensemble at a relatively high rate of sampling so as to
include all frequencies deemed necessary for the proper
analysis of the machinery being monitored while it is in operation. The data
from the ensembles can be streamed gap-free to a
storage medium for subsequent processing that can be connected to a cloud
network facility, a local data link, Bluetooth connectivity,
cellular data connectivity, or the like.
[0195] In embodiments, the methods disclosed herein include strategies for
collecting data from the various ensembles including
digital signal processing techniques that can be subsequently applied to data
from the ensembles to emphasize or better isolate
specific frequencies or waveform phenomena. This can be in contrast with
current methods that collect multiple sets of data at
different sampling rates, or with different hardware filtering configurations
including integration, that provide relatively less post-
processing flexibility because of the commitment to these same (known as a
priori hardware configurations). These same hardware
configurations can also be shown to increase time of the vibration survey due
to the latency delays associated with configuring the
hardware for each independent test. In embodiments, the methods for collecting
data from various ensembles include data marker
technology that can be used for classifying sections of streamed data as
homogenous and belonging to a specific ensemble. In one
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example, a classification can be defined as operating speed. In doing so, a
multitude of ensembles can be created from what
conventional systems would collect as only one. The many embodiments include
post-processing analytic techniques for comparing
the relative phases of all the frequencies of interest not only between each
channel of the collected ensemble but also between all of
the channels of all of the ensembles being monitored, when applicable.
[0196] With reference to Figure 12, the many embodiments include a first
machine 2400 having rotating or oscillating components
2410, or both, each supported by a set of bearings 2420 including a bearing
pack 2422, a bearing pack 2424, a bearing pack 2426,
and more as needed. The first machine 2400 can be monitored by a first sensor
ensemble 2450. The first sensor ensemble 2450 can
be configured to receive signals from sensors originally installed (or added
later) on the first machine 2400. The sensors on the first
machine 2400 can include single-axis sensors 2460, such as a single-axis
sensor 2462, a single-axis sensor 2464, and more as
needed. In many examples, the single axis-sensors 2460 can be positioned in
the first machine 2400 at locations that allow for the
sensing of one of the rotating or oscillating components 2410 of the first
machine 2400.
[0197] The first machine 2400 can also have tri-axial (e.g., orthogonal axes)
sensors 2480, such as a tri-axial sensor 2482, a tri-
axial sensor 2484, and more as needed. In many examples, the tri-axial sensors
2480 can be positioned in the first machine 2400 at
locations that allow for the sensing of one of each of the bearing packs in
the sets of bearings 2420 that is associated with the rotating
or oscillating components of the first machine 2400. The first machine 2400
can also have temperature sensors 2500, such as a
temperature sensor 2502, a temperature sensor 2504, and more as needed. The
first machine 2400 can also have a tachometer sensor
2510 or more as needed that each detail the RPMs of one of its rotating
components. By way of the above example, the first sensor
ensemble 2450 can survey the above sensors associated with the first machine
2400. To that end, the first sensor ensemble 2450 can
be configured to receive eight channels. In other examples, the first sensor
ensemble 2450 can be configured to have more than
eight channels, or less than eight channels as needed. In this example, the
eight channels include two channels that can each monitor
a single-axis reference sensor signal and three channels that can monitor a
tri-axial sensor signal. The remaining three channels can
monitor two temperature signals and a signal from a tachometer. In one
example, the first sensor ensemble 2450 can monitor the
single-axis sensor 2462, the single-axis sensor 2464, the tri-axial sensor
2482, the temperature sensor 2502, the temperature sensor
2504, and the tachometer sensor 2510 in accordance with the present
disclosure. During a vibration survey on the first machine
2400, the first sensor ensemble 2450 can first monitor the tri-axial sensor
2482 and then move next to the tri-axial sensor 2484.
[0198] After monitoring the tri-axial sensor 2484, the first sensor ensemble
2450 can monitor additional tri-axial sensors on the
first machine 2400 as needed and that are part of the predetermined route list
associated with the vibration survey of the first machine
2400, in accordance with the present disclosure. During this vibration survey,
the first sensor ensemble 2450 can continually monitor
the single-axis sensor 2462, the single-axis sensor 2464, the two temperature
sensors 2502, 2504, and the tachometer sensor 2510
while the first sensor ensemble 2450 can serially monitor the multiple tri-
axial sensors 2480 in the pre-determined route plan for
this vibration survey.
[0199] With reference to Figure 12, the many embodiments include a second
machine 2600 having rotating or oscillating
components 2610, or both, each supported by a set of bearings 2620 including a
bearing pack 2622, a bearing pack 2624, a bearing
pack 2626, and more as needed. The second machine 2600 can be monitored by a
second sensor ensemble 2650. The second sensor
ensemble 2650 can be configured to receive signals from sensors originally
installed (or added later) on the second machine 2600.
The sensors on the second machine 2600 can include single-axis sensors 2660,
such as a single-axis sensor 2662, a single-axis
sensor 2664, and more as needed. In many examples, the single axis-sensors
2660 can be positioned in the second machine 2600 at
locations that allow for the sensing of one of the rotating or oscillating
components 2610 of the second machine 2600.
[0200] The second machine 2600 can also have tri-axial (e.g., orthogonal axes)
sensors 2680, such as a tri-axial sensor 2682, a tri-
axial sensor 2684, a tri-axial sensor 2686, a tri-axial sensor 2688, and more
as needed. In many examples, the tri-axial sensors 2680
can be positioned in the second machine 2600 at locations that allow for the
sensing of one of each of the bearing packs in the sets
of bearings 2620 that is associated with the rotating or oscillating
components of the second machine 2600. The second machine
2600 can also have temperature sensors 2700, such as a temperature sensor
2702, a temperature sensor 2704, and more as needed.
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The machine 2600 can also have a tachometer sensor 2710 or more as needed that
each detail the RPMs of one of its rotating
components.
[0201] By way of the above example, the second sensor ensemble 2650 can survey
the above sensors associated with the second
machine 2600. To that end, the second sensor ensemble 2650 can be configured
to receive eight channels. In other examples, the
second sensor ensemble 2650 can be configured to have more than eight channels
or less than eight channels as needed. In this
example, the eight channels include one channel that can monitor a single-axis
reference sensor signal and six channels that can
monitor two tri-axial sensor signals. The remaining channel can monitor a
temperature signal. In one example, the second sensor
ensemble 2650 can monitor the single axis sensor 2662, the tri-axial sensor
2682, the tri-axial sensor 2684, and the temperature
sensor 2702. During a vibration survey on the machine 2600 in accordance with
the present disclosure, the second sensor ensemble
2650 can first monitor the tri-axial sensor 2682 simultaneously with the tri-
axial sensor 2684 and then move onto the tri-axial sensor
2686 simultaneously with the tri-axial sensor 2688.
[0202] After monitoring the tri-axial sensors 2680, the second sensor ensemble
2650 can monitor additional tri-axial sensors (in
simultaneous pairs) on the machine 2600 as needed and that are part of the
predetermined route list associated with the vibration
survey of the machine 2600 in accordance with the present disclosure. During
this vibration survey, the second sensor ensemble
2650 can continually monitor the single-axis sensor 2662 at its unchanging
location and the temperature sensor 2702 while the
second sensor ensemble 2650 can serially monitor the multiple tri-axial
sensors in the pre-determined route plan for this vibration
survey.
[0203] With continuing reference to Figure 12, the many embodiments include a
third machine 2800 having rotating or oscillating
components 2810, or both, each supported by a set of bearings including a
bearing pack 2822, a bearing pack 2824, a bearing pack
2826, and more as needed. The third machine 2800 can be monitored by a third
sensor ensemble 2850. The third sensor ensemble
2850 can be configured withtwo single-axis sensors 2860, 2864 and two tri-
axial (e.g., orthogonal axes) sensors 2880, 2882. In
many examples, the single axis-sensor 2860 can be secured by the user on the
third machine 2800 at a location that allows for the
sensing of one of the rotating or oscillating components of the third machine
2800. The tri-axial sensors 2880, 2882 can be also be
located on the third machine 2800 by the user at locations that allow for the
sensing of one of each of the bearings in the sets of
bearings that each associated with the rotating or oscillating components of
the third machine 2800. The third sensor ensemble
2850 can also include a temperature sensor 2900. The third sensor ensemble
2850 and its sensors can be moved to other machines
unlike the first and second sensor ensembles 2450, 2650.
[0204] The many embodiments also include a fourth machine 2950 having rotating
or oscillating components 2960, or both, each
supported by a set of bearings including a bearing pack 2972, a bearing pack
2974, a bearing pack 2976, and more as needed. The
fourth machine 2950 can be also monitored by the third sensor ensemble 2850
when the user moves it to the fourth machine 2950.
The many embodiments also include a fifth machine 3000 having rotating or
oscillating components 3010, or both. The fifth machine
3000 may not be explicitly monitored by any sensor or any sensor ensembles in
operation but it can create vibrations or other
impulse energy of sufficient magnitude to be recorded in the data associated
with any one the machines 2400, 2600, 2800, 2950
under a vibration survey.
[0205] The many embodiments include monitoring the first sensor ensemble 2450
on the first machine 2400 through the
predetermined route as disclosed herein. The many embodiments also include
monitoring the second sensor ensemble 2650 on the
second machine 2600 through the predetermined route. The locations of first
machine 2400 being close to machine 2600 can be
included in the contextual metadata of both vibration surveys. The third
sensor ensemble 2850 can be moved between third machine
2800, fourth machine 2950, and other suitable machines. The machine 3000 has
no sensors onboard as configured, but could be
monitored as needed by the third sensor ensemble 2850. The machine 3000 and
its operational characteristics can be recorded in
the metadata in relation to the vibration surveys on the other machines to
note its contribution due to its proximity.
[0206] The many embodiments include hybrid database adaptation for harmonizing
relational metadata and streaming raw data
formats. Unlike older systems that utilized traditional database structure for
associating nameplate and operational parameters
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(sometimes deemed metadata) with individual data measurements that are
discrete and relatively simple, it will be appreciated in
light of the disclosure that more modern systems can collect relatively larger
quantities of raw streaming data with higher sampling
rates and greater resolutions. At the same time, it will also be appreciated
in light of the disclosure that the network of metadata with
which to link and obtain this raw data or correlate with this raw data, or
both, is expanding at ever-increasing rates.
[0207] In one example, a single overall vibration level can be collected as
part of a route or prescribed list of measurement points.
This data collected can then be associated with database measurement location
information for a point located on a surface of a
bearing housing on a specific piece of the machine adjacent to a coupling in a
vertical direction. Machinery analysis parameters
relevant to the proper analysis can be associated with the point located on
the surface. Examples of machinery analysis parameters
relevant to the proper analysis can include a running speed of a shaft passing
through the measurement point on the surface. Further
examples of machinery analysis parameters relevant to the proper analysis can
include one of, or a combination of: running speeds
of all component shafts for that piece of equipment and/or machine, bearing
types being analyzed such as sleeve or rolling element
bearings, the number of gear teeth on gears should there be a gearbox, the
number of poles in a motor, slip and line frequency of a
motor, roller bearing element dimensions, number of fan blades, or the like.
Examples of machinery analysis parameters relevant to
the proper analysis can further include machine operating conditions such as
the load on the machines and whether load is expressed
in percentage, wattage, airflow, head pressure, horsepower, and the like.
Further examples of machinery analysis parameters include
information relevant to adjacent machines that might influence the data
obtained during the vibration study.
[0208] It will be appreciated in light of the disclosure that the vast array
of equipment and machinery types can support many
different classifications, each of which can be analyzed in distinctly
different ways. For example, some machines, like screw
compressors and hammer mills, can be shown to run much noisier and can be
expected to vibrate significantly more than other
machines. Machines known to vibrate more significantly can be shown to require
a change in vibration levels that can be considered
acceptable relative to quieter machines.
[0209] The present disclosure further includes hierarchical relationships
found in the vibrational data collected that can be used to
support proper analysis of the data. One example of the hierarchical data
includes the interconnection of mechanical componentry
such as a bearing being measured in a vibration survey and the relationship
between that bearing, including how that bearing
connects to a particular shaft on which is mounted a specific pinion within a
particular gearbox, and the relationship between the
shaft, the pinion, and the gearbox. The hierarchical data can further include
in what particular spot within a machinery gear train
that the bearing being monitored is located relative to other components in
the machine. The hierarchical data can also detail whether
the bearing being measured in a machine is in close proximity to another
machine whose vibrations may affect what is being
measured in the machine that is the subject of the vibration study.
[0210] The analysis of the vibration data from the bearing or other components
related to one another in the hierarchical data can
use table lookups, searches for correlations between frequency patterns
derived from the raw data, and specific frequencies from
the metadata of the machine. In some embodiments, the above can be stored in
and retrieved from a relational database. In
embodiments, National Instrument's Technical Data Management Solution (TDMS)
file format can be used. The TDMS file format
can be optimized for streaming various types of measurement data (i.e., binary
digital samples of waveforms), as well as also being
able to handle hierarchical metadata.
[0211] The many embodiments include a hybrid relational metadata - binary
storage approach (HRM-BSA). The HRM-BSA can
include a structured query language (SQL) based relational database engine.
The structured query language based relational database
engine can also include a raw data engine that can be optimized for throughput
and storage density for data that is flat and relatively
structureless. It will be appreciated in light of the disclosure that benefits
can be shown in the cooperation between the hierarchical
metadata and the SQL relational database engine. In one example, marker
technologies and pointer sign-posts can be used to make
correlations between the raw database engine and the SQL relational database
engine. Three examples of correlations between the
raw database engine and the SQL relational database engine linkages include:
(1) pointers from the SQL database to the raw data;
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(2) pointers from the ancillary metadata tables or similar grouping of the raw
data to the SQL database; and (3) independent storage
tables outside the domain of either the SQL data base or raw data
technologies.
[0212] With reference to Figure 13, the present disclosure can include
pointers for Group 1 and Group 2 that can include associated
filenames, path information, table names, database key fields as employed with
existing SQL database technologies that can be used
to associate a specific database segments or locations, asset properties to
specific measurement raw data streams, records with
associated time/date stamps, or associated metadata such as operating
parameters, panel conditions and the like. By way of this
example, a plant 3200 can include machine one 3202, machine two 3204, and many
others in the plant 3200. The machine one 3202
can include a gearbox 3212, a motor 3210, and other elements. The machine two
3204 can include a motor 3220, and other elements.
Many waveforms 3230 including waveform 3240, waveform 3242, waveform 3244, and
additional waveforms as needed can be
acquired from the machines 3202, 3204 in the plant 3200. The waveforms 3230
can be associated with the local marker linking
tables 3300 and the linking raw data tables 3400. The machines 3202, 3204 and
their elements can be associated with linking tables
having relational databases 3500. The linking tables raw data tables 3400 and
the linking tables having relational databases 3500
can be associated with the linking tables with optional independent storage
tables 3600.
[0213] The present disclosure can include markers that can be applied to a
time mark or a sample length within the raw waveform
data. The markers generally fall into two categories: preset or dynamic. The
preset markers can correlate to preset or existing
operating conditions (e.g., load, head pressure, air flow cubic feet per
minute, ambient temperature, RPMs, and the like.). These
preset markers can be fed into the data acquisition system directly. In
certain instances, the preset markers can be collected on data
channels in parallel with the waveform data (e.g., waveforms for vibration,
current, voltage, etc.). Alternatively, the values for the
preset markers can be entered manually.
[0214] For dynamic markers such as trending data, it can be important to
compare similar data like comparing vibration amplitudes
and patterns with a repeatable set of operating parameters. One example of the
present disclosure includes one of the parallel channel
inputs being a key phasor trigger pulse from an operating shaft that can
provide RPM information at the instantaneous time of
collection. In this example of dynamic markers, sections of collected waveform
data can be marked with appropriate speeds or speed
ranges.
[0215] The present disclosure can also include dynamic markers that can
correlate to data that can be derived from post processing
and analytics performed on the sample waveform. In further embodiments, the
dynamic markers can also correlate to post-collection
derived parameters including RPMs, as well as other operationally derived
metrics such as alarm conditions like a maximum RPM.
In certain examples, many modern pieces of equipment that are candidates for a
vibration survey with the portable data collection
systems described herein do not include tachometer information. This can be
true because it is not always practical or cost-justifiable
to add a tachometer even though the measurement of RPM can be of primary
importance for the vibration survey and analysis. It
will be appreciated that for fixed speed machinery obtaining an accurate RPM
measurement can be less important especially when
the approximate speed of the machine can be ascertained before-hand; however,
variable-speed drives are becoming more and more
prevalent. It will also be appreciated in light of the disclosure that various
signal processing techniques can permit the derivation of
RPM from the raw data without the need for a dedicated tachometer signal.
[0216] In many embodiments, the RPM information can be used to mark segments
of the raw waveform data over its collection
history. Further embodiments include techniques for collecting instrument data
following a prescribed route of a vibration study.
The dynamic markers can enable analysis and trending software to utilize
multiple segments of the collection interval indicated by
the markers (e.g., two minutes) as multiple historical collection ensembles,
rather than just one as done in previous systems where
route collection systems would historically store data for only one RPM
setting. This could, in turn, be extended to any other
operational parameter such as load setting, ambient temperature, and the like,
as previously described. The dynamic markers,
however, that can be placed in a type of index file pointing to the raw data
stream can classify portions of the stream in homogenous
entities that can be more readily compared to previously collected portions of
the raw data stream
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[0217] The many embodiments include the hybrid relational metadata-binary
storage approach that can use the best of pre-existing
technologies for both relational and raw data streams. In embodiments, the
hybrid relational metadata - binary storage approach can
marry them together with a variety of marker linkages. The marker linkages can
permit rapid searches through the relational
metadata and can allow for more efficient analyses of the raw data using
conventional SQL techniques with pre-existing technology.
This can be shown to permit utilization of many of the capabilities, linkages,
compatibilities, and extensions that conventional
database technologies do not provide.
[0218] The marker linkages can also permit rapid and efficient storage of the
raw data using conventional binary storage and data
compression techniques. This can be shown to permit utilization of many of the
capabilities, linkages, compatibilities, and extensions
that conventional raw data technologies provide such as TMDS (National
Instruments), UFF (Universal File Format such as UFF58),
and the like. The marker linkages can further permit using the marker
technology links where a vastly richer set of data from the
ensembles can be amassed in the same collection time as more conventional
systems. The richer set of data from the ensembles can
store data snapshots associated with predetermined collection criterion and
the proposed system can derive multiple snapshots from
the collected data streams utilizing the marker technology. In doing so, it
can be shown that a relatively richer analysis of the
collected data can be achieved. One such benefit can include more trending
points of vibration at a specific frequency or order of
running speed versus RPM, load, operating temperature, flow rates and the
like, which can be collected for a similar time relative
to what is spent collecting data with a conventional system.
[0219] In embodiments, the platform 100 may include the local data collection
system 102 deployed in the environment 104 to
monitor signals from machines, elements of the machines and the environment of
the machines including heavy duty machines
deployed at a local job site or at distributed job sites under common control.
The heavy-duty machines may include earthmoving
equipment, heavy duty on-road industrial vehicles, heavy duty off-road
industrial vehicles, industrial machines deployed in various
settings such as turbines, turbomachinery, generators, pumps, pulley systems,
manifold and valve systems, and the like. In
embodiments, heavy industrial machinery may also include earth-moving
equipment, earth-compacting equipment, hauling
equipment, hoisting equipment, conveying equipment, aggregate production
equipment, equipment used in concrete construction,
and piledriving equipment. In examples, earth moving equipment may include
excavators, backhoes, loaders, bulldozers, skid steer
loaders, trenchers, motor graders, motor scrapers, crawker loaders, and
wheeled loading shovels. In examples, construction vehicles
may include dumpers, tankers, tippers, and trailers. In examples, material
handling equipment may include cranes, conveyors,
forklift, and hoists. In examples, construction equipment may include tunnel
and handling equipment, road rollers, concrete mixers,
hot mix plants, road making machines (compactors), stone crashers, pavers,
slurry seal machines, spraying and plastering machines,
and heavy-duty pumps. Further examples of heavy industrial equipment may
include different systems such as implement traction,
structure, power train, control, and information. Heavy industrial equipment
may include many different powertrains and
combinations thereof to provide power for locomotion and to also provide power
to accessories and onboard functionality. In each
of these examples, the platform 100 may deploy the local data collection
system 102 into the environment 104 in which these
machines, motors, pumps, and the like, operate and directly connected
integrated into each of the machines, motors, pumps, and the
like.
[0220] In embodiments, the platform 100 may include the local data collection
system 102 deployed in the environment 104 to
monitor signals from machines in operation and machines in being constructed
such as turbine and generator sets like SiemensTM
SGT6-5000FTm gas turbine, an SST-900Tm steam turbine, an SGen61000ATM
generator, and an SGen6100ATM generator, and the
like. In embodiments, the local data collection system 102 may be deployed to
monitor steam turbines as they rotate in the currents
caused by hot water vapor that may be directed through the turbine but
otherwise generated from a different source such as from
gas-fired burners, nuclear cores, molten salt loops and the like. In these
systems, the local data collection system 102 may monitor
the turbines and the water or other fluids in a closed loop cycle in which
water condenses and is then heated until it evaporates again.
The local data collection system 102 may monitor the steam turbines separately
from the fuel source deployed to heat the water to
steam. In examples, working temperatures of steam turbines may be between 500
and 650 C. In many embodiments, an array of
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steam turbines may be arranged and configured for high, medium, and low
pressure, so they may optimally convert the respective
steam pressure into rotational movement.
[0221] The local data collection system 102 may also be deployed in a gas
turbines arrangement and therefore not only monitor
the turbine in operation but also monitor the hot combustion gases feed into
the turbine that may be in excess of 1,500 C. Because
these gases are much hotter than those in steam turbines, the blades may be
cooled with air that may flow out of small openings to
create a protective film or boundary layer between the exhaust gases and the
blades. This temperature profile may be monitored by
the local data collection system 102. Gas turbine engines, unlike typical
steam turbines, include a compressor, a combustion
chamber, and a turbine all of which are journaled for rotation with a rotating
shaft. The construction and operation of each of these
components may be monitored by the local data collection system 102.
[0222] In embodiments, the platform 100 may include the local data collection
system 102 deployed in the environment 104 to
monitor signals from water turbines serving as rotary engines that may harvest
energy from moving water and are used for electric
power generation. The type of water turbine or hydro-power selected for a
project may be based on the height of standing water,
often referred to as head, and the flow, or volume of water, at the site. In
this example, a generator may be placed at the top of a
shaft that connects to the water turbine. As the turbine catches the naturally
moving water in its blade and rotates, the turbine sends
rotational power to the generator to generate electrical energy. In doing so,
the platform 100 may monitor signals from the generators,
the turbines, the local water system, flow controls such as dam windows and
sluices. Moreover, the platform 100 may monitor local
conditions on the electric grid including load, predicted demand, frequency
response, and the like, and include such information in
the monitoring and control deployed by platform 100 in these hydroelectric
settings.
[0223] In embodiments, the platform 100 may include the local data collection
system 102 deployed in the environment 104 to
monitor signals from energy production environments, such as thermal, nuclear,
geothermal, chemical, biomass, carbon-based fuels,
hybrid-renewable energy plants, and the like. Many of these plants may use
multiple forms of energy harvesting equipment like
wind turbines, hydro turbines, and steam turbines powered by heat from
nuclear, gas-fired, solar, and molten salt heat sources. In
embodiments, elements in such systems may include transmission lines, heat
exchangers, desulphurization scrubbers, pumps,
coolers, recuperators, chillers, and the like. In embodiments, certain
implementations of turbomachinery, turbines, scroll
compressors, and the like may be configured in arrayed control so as to
monitor large facilities creating electricity for consumption,
providing refrigeration, creating steam for local manufacture and heating, and
the like, and that arrayed control platforms may be
provided by the provider of the industrial equipment such as Honeywell and
their ExperionTM PKS platform. In embodiments, the
platform 100 may specifically communicate with and integrate the local
manufacturer-specific controls and may allow equipment
from one manufacturer to communicate with other equipment. Moreover, the
platform 100 provides allows for the local data
collection system 102 to collect information across systems from many
different manufacturers. In embodiments, the platform 100
may include the local data collection system 102 deployed in the environment
104 to monitor signals from marine industrial
equipment, marine diesel engines, shipbuilding, oil and gas plants,
refineries, petrochemical plant, ballast water treatment solutions,
marine pumps and turbines and the like.
[0224] In embodiments, the platform 100 may include the local data collection
system 102 deployed in the environment 104 to
monitor signals from heavy industrial equipment and processes including
monitoring one or more sensors. By way of this example,
sensors may be devices that may be used to detect or respond to some type of
input from a physical environment, such as an
electrical, heat, or optical signal. In embodiments, the local data collection
system 102 may include multiple sensors such as, without
limitation, a temperature sensor, a pressure sensor, a torque sensor, a flow
sensor, a heat sensors, a smoke sensor, an arc sensor, a
radiation sensor, a position sensor, an acceleration sensor, a strain sensor,
a pressure cycle sensor, a pressure sensor, an air
temperature sensor, and the like. The torque sensor may encompass a magnetic
twist angle sensor. In one example, the torque and
speed sensors in the local data collection system 102 may be similar to those
discussed in U.S. Patent Number 8,352,149 to
Meachem, issued 8 January 2013 and hereby incorporated by reference as if
fully set forth herein. In embodiments, one or more
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sensors may be provided such as a tactile sensor, a biosensor, a chemical
sensor, an image sensor, a humidity sensor, an inertial
sensor, and the like.
[0225] In embodiments, the platform 100 may include the local data collection
system 102 deployed in the environment 104 to
monitor signals from sensors that may provide signals for fault detection
including excessive vibration, incorrect material, incorrect
material properties, trueness to the proper size, trueness to the proper
shape, proper weight, trueness to balance. Additional fault
sensors include those for inventory control and for inspections such as to
confirming that parts packaged to plan, parts are to
tolerance in a plan, occurrence of packaging damage or stress, and sensors
that may indicate the occurrence of shock or damage in
transit. Additional fault sensors may include detection of the lack of
lubrication, over lubrication, the need for cleaning of the sensor
detection window, the need for maintenance due to low lubrication, the need
for maintenance due to blocking or reduced flow in a
lubrication region, and the like.
[0226] In embodiments, the platform 100 may include the local data collection
system 102 deployed in the environment 104 that
includes aircraft operations and manufacture including monitoring signals from
sensors for specialized applications such as sensors
used in an aircraft's Attitude and Heading Reference System (AHRS), such as
gyroscopes, accelerometers, and magnetometers. In
embodiments, the platform 100 may include the local data collection system 102
deployed in the environment 104 to monitor signals
from image sensors such as semiconductor charge coupled devices (CCDs), active
pixel sensors, in complementary metal¨oxide¨
semiconductor (CMOS) or N-type metal-oxide-semiconductor (NMOS, Live MOS)
technologies. In embodiments, the platform
100 may include the local data collection system 102 deployed in the
environment 104 to monitor signals from sensors such as an
infra-red (IR) sensor, an ultraviolet (UV) sensor, a touch sensor, a proximity
sensor, and the like. In embodiments, the platform 100
may include the local data collection system 102 deployed in the environment
104 to monitor signals from sensors configured for
optical character recognition (OCR), reading barcodes, detecting surface
acoustic waves, detecting transponders, communicating
with home automation systems, medical diagnostics, health monitoring, and the
like.
[0227] In embodiments, the platform 100 may include the local data collection
system 102 deployed in the environment 104 to
monitor signals from sensors such as a Micro-Electro-Mechanical Systems (MEMS)
sensor, such as ST Microelectronic' STM
LSM303AH smart MEMS sensor, which may include an ultra-low-power high-
performance system-in-package featuring a 3D
digital linear acceleration sensor and a 3D digital magnetic sensor.
[0228] In embodiments, the platform 100 may include the local data collection
system 102 deployed in the environment 104 to
monitor signals from additional large machines such as turbines, windmills,
industrial vehicles, robots, and the like. These large
mechanical machines include multiple components and elements providing
multiple subsystems on each machine. Toward that end,
the platform 100 may include the local data collection system 102 deployed in
the environment 104 to monitor signals from
individual elements such as axles, bearings, belts, buckets, gears, shafts,
gear boxes, cams, carriages, camshafts, clutches, brakes,
drums, dynamos, feeds, flywheels, gaskets, pumps, jaws, robotic arms, seals,
sockets, sleeves, valves, wheels, actuators, motors,
servomotor, and the like. Many of the machines and their elements may include
servomotors. The local data collection system 102
may monitor the motor, the rotary encoder, and the potentiometer of the
servomechanism to provide three-dimensional detail of
position, placement, and progress of industrial processes.
[0229] In embodiments, the platform 100 may include the local data collection
system 102 deployed in the environment 104 to
monitor signals from gear drives, powertrains, transfer cases, multispeed
axles, transmissions, direct drives, chain drives, belt-drives,
shaft-drives, magnetic drives, and similar meshing mechanical drives. In
embodiments, the platform 100 may include the local data
collection system 102 deployed in the environment 104 to monitor signals from
fault conditions of industrial machines that may
include overheating, noise, grinding gears, locked gears, excessive vibration,
wobbling, under-inflation, over-inflation, and the like.
Operation faults, maintenance indicators, and interactions from other machines
may cause maintenance or operational issues may
occur during operation, during installation, and during maintenance. The
faults may occur in the mechanisms of the industrial
machines but may also occur in infrastructure that supports the machine such
as its wiring and local installation platforms. In
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embodiments, the large industrial machines may face different types of fault
conditions such as overheating, noise, grinding gears,
excessive vibration of machine parts, fan vibration problems, problems with
large industrial machines rotating parts.
[0230] In embodiments, the platform 100 may include the local data collection
system 102 deployed in the environment 104 to
monitor signals from industrial machinery including failures that may be
caused by premature bearing failure that may occur due to
contamination or loss of bearing lubricant. In another example, a mechanical
defect such as misalignment of bearings may occur.
Many factors may contribute to the failure such as metal fatigue, therefore,
the local data collection system 102 may monitor cycles
and local stresses. By way of this example, the platform 100 may monitor
incorrect operation of machine parts, lack of maintenance
and servicing of parts, corrosion of vital machine parts, such as couplings or
gearboxes, misalignment of machine parts, and the
like. Though the fault occurrences cannot be completely stopped, many
industrial breakdowns may be mitigated to reduce
operational and financial losses. The platform 100 provides real-time
monitoring and predictive maintenance in many industrial
environments wherein it has been shown to present a cost-savings over
regularly-scheduled maintenance processes that replace parts
according to a rigid expiration of time and not actual load and wear and tear
on the element or machine. To that end, the platform
100 may provide reminders of, or perform some, preventive measures such as
adhering to operating manual and mode instructions
for machines, proper lubrication, and maintenance of machine parts, minimizing
or eliminating overrun of machines beyond their
defined capacities, replacement of worn but still functional parts as needed,
properly training the personnel for machine use, and the
like.
[0231] In embodiments, the platform 100 may include the local data collection
system 102 deployed in the environment 104 to
monitor multiple signals that may be carried by a plurality of physical,
electronic, and symbolic formats or signals. The platform
100 may employ signal processing including a plurality of mathematical,
statistical, computational, heuristic, and linguistic
representations and processing of signals and a plurality of operations needed
for extraction of useful information from signal
processing operations such as techniques for representation, modeling,
analysis, synthesis, sensing, acquisition, and extraction of
information from signals. In examples, signal processing may be performed
using a plurality of techniques, including but not limited
to transformations, spectral estimations, statistical operations,
probabilistic and stochastic operations, numerical theory analysis,
data mining, and the like. The processing of various types of signals forms
the basis of many electrical or computational process.
As a result, signal processing applies to almost all disciplines and
applications in the industrial environment such as audio and video
processing, image processing, wireless communications, process control,
industrial automation, financial systems, feature
extraction, quality improvements such as noise reduction, image enhancement,
and the like. Signal processing for images may
include pattern recognition for manufacturing inspections, quality inspection,
and automated operational inspection and
maintenance. The platform 100 may employ many pattern recognition techniques
including those that may classify input data into
classes based on key features with the objective of recognizing patterns or
regularities in data. The platform 100 may also implement
pattern recognition processes with machine learning operations and may be used
in applications such as computer vision, speech
and text processing, radar processing, handwriting recognition, CAD systems,
and the like. The platform 100 may employ supervised
classification and unsupervised classification. The supervised learning
classification algorithms may be based to create classifiers
for image or pattern recognition, based on training data obtained from
different object classes. The unsupervised learning
classification algorithms may operate by finding hidden structures in
unlabeled data using advanced analysis techniques such as
segmentation and clustering. For example, some of the analysis techniques used
in unsupervised learning may include K-means
clustering, Gaussian mixture models, Hidden Markov models, and the like. The
algorithms used in supervised and unsupervised
learning methods of pattern recognition enable the use of pattern recognition
in various high precision applications. The platform
100 may use pattern recognition in face detection related applications such as
security systems, tracking, sports related applications,
fingerprint analysis, medical and forensic applications, navigation and
guidance systems, vehicle tracking, public infrastructure
systems such as transport systems, license plate monitoring, and the like.
[0232] Additional details are provided below in connection with the methods,
systems, devices, and components depicted in
connection with Figures 1 through 6. In embodiments, methods and systems are
disclosed herein for cloud-based, machine pattern
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recognition based on fusion of remote, analog industrial sensors. For example,
data streams from vibration, pressure, temperature,
accelerometer, magnetic, electrical field, and other analog sensors may be
multiplexed or otherwise fused, relayed over a network,
and fed into a cloud-based machine learning facility, which may employ one or
more models relating to an operating characteristic
of an industrial machine, an industrial process, or a component or element
thereof. A model may be created by a human who has
experience with the industrial environment and may be associated with a
training data set (such as created by human analysis or
machine analysis of data that is collected by the sensors in the environment,
or sensors in other similar environments. The learning
machine may then operate on other data, initially using a set of rules or
elements of a model, such as to provide a variety of outputs,
such as classification of data into types, recognition of certain patterns
(such as ones indicating the presence of faults, or ones
indicating operating conditions, such as fuel efficiency, energy production,
or the like). The machine learning facility may take
feedback, such as one or more inputs or measures of success, such that it may
train, or improve, its initial model (such as by adjusting
weights, rules, parameters, or the like, based on the feedback). For example,
a model of fuel consumption by an industrial machine
may include physical model parameters that characterize weights, motion,
resistance, momentum, inertia, acceleration, and other
factors that indicate consumption, and chemical model parameters (such as ones
that predict energy produced and/or consumed e.g.,
such as through combustion, through chemical reactions in battery charging and
discharging, and the like). The model may be
refined by feeding in data from sensors disposed in the environment of a
machine, in the machine, and the like, as well as data
indicating actual fuel consumption, so that the machine can provide
increasingly accurate, sensor-based, estimates of fuel
consumption and can also provide output that indicate what changes can be made
to increase fuel consumption (such as changing
operation parameters of the machine or changing other elements of the
environment, such as the ambient temperature, the operation
of a nearby machine, or the like). For example, if a resonance effect between
two machines is adversely affecting one of them, the
model may account for this and automatically provide an output that results in
changing the operation of one of the machines (such
as to reduce the resonance, to increase fuel efficiency of one or both
machines). By continuously adjusting parameters to cause
outputs to match actual conditions, the machine learning facility may self-
organize to provide a highly accurate model of the
conditions of an environment (such as for predicting faults, optimizing
operational parameters, and the like). This may be used to
increase fuel efficiency, to reduce wear, to increase output, to increase
operating life, to avoid fault conditions, and for many other
purposes.
[0233] Figure 14 illustrates components and interactions of a data collection
architecture involving application of cognitive and
machine learning systems to data collection and processing. Referring to
Figure 14, a data collection system 102 may be disposed
in an environment (such as an industrial environment where one or more complex
systems, such as electro-mechanical systems and
machines are manufactured, assembled, or operated). The data collection system
102 may include onboard sensors and may take
input, such as through one or more input interfaces or ports 4008, from one or
more sensors (such as analog or digital sensors of
any type disclosed herein) and from one or more input sources 116 (such as
sources that may be available through Wi-Fi, Bluetooth,
NFC, or other local network connections or over the Internet). Sensors may be
combined and multiplexed (such as with one or more
multiplexers 4002). Data may be cached or buffered in a cache/buffer 4022 and
made available to external systems, such as a remote
host processing system 112 as described elsewhere in this disclosure (which
may include an extensive processing architecture 4024,
including any of the elements described in connection with other embodiments
described throughout this disclosure and in the
Figure), though one or more output interfaces and ports 4010 (which may in
embodiments be separate from or the same as the input
interfaces and ports 4008). The data collection system 102 may be configured
to take input from a host processing system 112, such
as input from an analytic system 4018, which may operate on data from the data
collection system 102 and data from other input
sources 116 to provide analytic results, which in turn may be provided as a
learning feedback input 4012 to the data collection
system, such as to assist in configuration and operation of the data
collection system 102.
[0234] Combination of inputs (including selection of what sensors or input
sources to turn "on" or "off') may be performed under
the control of machine-based intelligence, such as using a local cognitive
input selection system 4004, an optionally remote cognitive
input selection system 4014, or a combination of the two. The cognitive input
selection systems 4004, 4014 may use intelligence
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and machine learning capabilities described elsewhere in this disclosure, such
as using detected conditions (such as informed by the
input sources 116 or sensors), state information (including state information
determined by a machine state recognition system 4021
that may determine a state), such as relating to an operational state, an
environmental state, a state within a known process or
workflow, a state involving a fault or diagnostic condition, or many others.
This may include optimization of input selection and
configuration based on learning feedback from the learning feedback system
4012, which may include providing training data (such
as from the host processing system 112 or from other data collection systems
102 either directly or from the host processing system
112) and may include providing feedback metrics, such as success metrics
calculated within the analytic system 4018 of the host
processing system 112. For example, if a data stream consisting of a
particular combination of sensors and inputs yields positive
results in a given set of conditions (such as providing improved pattern
recognition, improved prediction, improved diagnosis,
improved yield, improved return on investment, improved efficiency, or the
like), then metrics relating to such results from the
analytic system 4018 can be provided via the learning feedback system 4012 to
the cognitive input selection systems 4004, 4014 to
help configure future data collection to select that combination in those
conditions (allowing other input sources to be de-selected,
such as by powering down the other sensors). In embodiments, selection and de-
selection of sensor combinations, under control of
one or more of the cognitive input selection systems 4004, may occur with
automated variation, such as using genetic programming
techniques, such that over time, based on learning feedback 4012, such as from
the analytic system 4018, effective combinations
for a given state or set of conditions are promoted, and less effective
combinations are demoted, resulting in progressive optimization
and adaptation of the local data collection system to each unique environment.
Thus, an automatically adapting, multi-sensor data
collection system is provided, where cognitive input selection is used, with
feedback, to improve the effectiveness, efficiency, or
other performance parameter of the data collection system within its
particular environment. Performance parameters may relate to
overall system metrics (such as financial yields, process optimization
results, energy production or usage, and the like), analytic
metrics (such as success in recognizing patterns, making predictions,
classifying data, or the like), and local system metrics (such
as bandwidth utilization, storage utilization, power consumption, and the
like). In embodiments, the analytic system 4018, the
machine state recognition system 4021, policy automation engine 4032 and the
cognitive input selection system 4014 of a host may
take data from multiple data collection systems 102, such that optimization
(including of input selection) may be undertaken through
coordinated operation of multiple data collection systems 102. For example,
the cognitive input selection system 4014 may
understand that if one data collection system 102 is already collecting
vibration data for an X-axis, the X-axis vibration sensor for
the other data collection system might be turned off, in favor of getting Y-
axis data from the other data collector 102. Thus, through
coordinated collection by the host cognitive input selection system 4014, the
activity of multiple collectors 102, across a host of
different sensors, can provide for a rich data set for the host processing
system 112, without wasting energy, bandwidth, storage
space, or the like. As noted above, optimization may be based on overall
system success metrics, analytic success metrics, and local
system metrics, or a combination of the above.
[0235] In embodiments, the local cognitive input selection system 4004 may
organize fusion of data for various onboard sensors,
external sensors (such as in the local environment) and other input sources
116 to the local collection system 102 into one or more
fused data streams, such as using the multiplexer 4002 to create various
signals that represent combinations, permutations, mixes,
layers, abstractions, data-metadata combinations, and the like of the source
analog and/or digital data that is handled by the data
collection system 102. The selection of a particular fusion of sensors may be
determined locally by the cognitive input selection
system 4004, such as based on learning feedback from the learning feedback
system 4012, such as various overall system, analytic
system and local system results and metrics. In embodiments, the system may
learn to fuse particular combinations and permutations
of sensors, such as in order to best achieve correct anticipation of state, as
indicated by feedback of the analytic system 4018
regarding its ability to predict future states, such as the various states
handled by the machine state recognition system 4021. For
example, the cognitive input selection system 4004 may indicate selection of a
sub-set of sensors among a larger set of available
sensors, and the inputs from the selected sensors may be combined, such as by
placing input from each of them into a byte of a
defined, multi-bit data structure (such as by taking a signal from each at a
given sampling rate or time and placing the result into the
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byte structure, then collecting and processing the bytes over time), by
multiplexing in the multiplexer 4002, such as by additive
mixing of continuous signals, and the like. Any of a wide range of signal
processing and data processing techniques for combination
and fusing may be used, including convolutional techniques, coercion
techniques, transformation techniques, and the like. The
particular fusion in question may be adapted to a given situation by cognitive
learning, such as by having the cognitive input selection
system 4004 learn, based on learning feedback 4012 from results (such as
conveyed by the analytic system 4018), such that the local
data collection system 102 executes context-adaptive sensor fusion. In
embodiments the data collection system 102 may comprise
self organizing storage 4028.
[0236] In embodiments, the analytic system 4018 may apply to any of a wide
range of analytic techniques, including statistical
and econometric techniques (such as linear regression analysis, use similarity
matrices, heat map based techniques, and the like),
reasoning techniques (such as Bayesian reasoning, rule-based reasoning,
inductive reasoning, and the like), iterative techniques
(such as feedback, recursion, feed-forward and other techniques), signal
processing techniques (such as Fourier and other
transforms), pattern recognition techniques (such as Kalman and other
filtering techniques), search techniques, probabilistic
techniques (such as random walks, random forest algorithms, and the like),
simulation techniques (such as random walks, random
forest algorithms, linear optimization and the like), and others. This may
include computation of various statistics or measures. In
embodiments, the analytic system 4018 may be disposed, at least in part, on a
data collection system 102, such that a local analytic
system can calculate one or more measures, such as relating to any of the
items noted throughout this disclosure. For example,
measures of efficiency, power utilization, storage utilization, redundancy,
entropy, and other factors may be calculated onboard, so
that the data collection 102 can enable various cognitive and learning
functions noted throughout this disclosure without dependence
on a remote (e.g., cloud-based) analytic system.
[0237] In embodiments, the host processing system 112, a data collection
system 102, or both, may include, connect to, or integrate
with, a self-organizing networking system 4031, which may comprise a cognitive
system for providing machine-based, intelligent
or organization of network utilization for transport of data in a data
collection system, such as for handling analog and other sensor
data, or other source data, such as among one or more local data collection
systems 102 and a host processing system 112. This may
include organizing network utilization for source data delivered to data
collection systems, for feedback data, such as analytic data
provided to or via a learning feedback system 4012, data for supporting a
marketplace (such as described in connection with other
embodiments), and output data provided via output interfaces and ports 4010
from one or more data collection systems 102.
[0238] In embodiments (Figures 15 and 16), a cognitive data packaging system
4110 of the cognitive data marketplace 4102 may
use machine-based intelligence to package data, such as by automatically
configuring packages of data in batches, streams, pools,
or the like. In embodiments, packaging may be according to one or more rules,
models, or parameters, such as by packaging or
aggregating data that is likely to supplement or complement an existing model.
For example, operating data from a group of similar
machines (such as one or more industrial machines noted throughout this
disclosure) may be aggregated together, such as based on
metadata indicating the type of data or by recognizing features or
characteristics in the data stream that indicate the nature of the
data. In embodiments, packaging may occur using machine learning and cognitive
capabilities, such as by learning what
combinations, permutations, mixes, layers, and the like of input sources 116,
sensors, information from data pools 4120 and
information from data collection systems 102 are likely to satisfy user
requirements or result in measures of success. Learning may
be based on learning feedback 4012, such as based on measures determined in an
analytic system 4018, such as system performance
measures, data collection measures, analytic measures, and the like. In
embodiments, success measures may be correlated to
marketplace success measures, such as viewing of packages, engagement with
packages, purchase or licensing of packages,
payments made for packages, and the like. Such measures may be calculated in
an analytic system 4018, including associating
particular feedback measures with search terms and other inputs, so that the
cognitive packaging system 4110 can find and configure
packages that are designed to provide increased value to consumers and
increased returns for data suppliers. In embodiments, the
cognitive data packaging system 4110 can automatically vary packaging, such as
using different combinations, permutations, mixes,
and the like, and varying weights applied to given input sources, sensors,
data pools and the like, using learning feedback 4012 to
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promote favorable packages and de-emphasize less favorable packages. This may
occur using genetic programming and similar
techniques that compare outcomes for different packages. Feedback may include
state information from the state system 4020 (such
as about various operating states, and the like), as well as about marketplace
conditions and states, such as pricing and availability
information for other data sources. Thus, an adaptive cognitive data packaging
system 4110 is provided that automatically adapts
to conditions to provide favorable packages of data for the marketplace 4102.
[0239] In embodiments, a cognitive data pricing system 4112 may be provided to
set pricing for data packages. In embodiments,
the cognitive data pricing system 4112 may use a set of rules, models, or the
like, such as setting pricing based on supply conditions,
demand conditions, pricing of various available sources, and the like. For
example, pricing for a package may be configured to be
set based on the sum of the prices of constituent elements (such as input
sources, sensor data, or the like), or to be set based on a
rule-based discount to the sum of prices for constituent elements, or the
like. Rules and conditional logic may be applied, such as
rules that factor in cost factors (such as bandwidth and network usage, peak
demand factors, scarcity factors, and the like), rules that
factor in utilization parameters (such as the purpose, domain, user, role,
duration, or the like for a package) and many others. In
embodiments, the cognitive data pricing system 4112 may include fully
cognitive, intelligent features, such as using genetic
programming including automatically varying pricing and tracking feedback on
outcomes. Outcomes on which tracking feedback
may be based include various financial yield metrics, utilization metrics and
the like that may be provided by calculating metrics in
an analytic system 4018 on data from the data transaction system 4114 or the
distributed ledger 4104. In embodiments, the cognitive
data marketplace 4102 may have a data marketplace interface 4108 enabling a
data market search 4118
[0240] Methods and systems are disclosed herein for self-organizing data pools
which may include self-organization of data pools
based on utilization and/or yield metrics, including utilization and/or yield
metrics that are tracked for a plurality of data pools. The
data pools may initially comprise unstructured or loosely structured pools of
data that contain data from industrial environments,
such as sensor data from or about industrial machines or components. For
example, a data pool might take streams of data from
various machines or components in an environment, such as turbines,
compressors, batteries, reactors, engines, motors, vehicles,
pumps, rotors, axles, bearings, valves, and many others, with the data streams
containing analog and/or digital sensor data (of a
wide range of types), data published about operating conditions, diagnostic
and fault data, identifying data for machines or
components, asset tracking data, and many other types of data. Each stream may
have an identifier in the pool, such as indicating
its source, and optionally its type. The data pool may be accessed by external
systems, such as through one or more interfaces or
APIs (e.g., RESTful APIs), or by data integration elements (such as gateways,
brokers, bridges, connectors, or the like), and the
data pool may use similar capabilities to get access to available data
streams. A data pool may be managed by a self-organizing
machine learning facility, which may configure the data pool, such as by
managing what sources are used for the pool, managing
what streams are available, and managing APIs or other connections into and
out of the data pool. The self-organization may take
feedback such as based on measures of success that may include measures of
utilization and yield. The measures of utilization and
yield that may include may account for the cost of acquiring and/or storing
data, as well as the benefits of the pool, measured either
by profit or by other measures that may include user indications of
usefulness, and the like. For example, a self-organizing data pool
might recognize that chemical and radiation data for an energy production
environment are regularly accessed and extracted, while
vibration and temperature data have not been used, in which case the data pool
might automatically reorganize, such as by ceasing
storage of vibration and/or temperature data, or by obtaining better sources
of such data. This automated reorganization can also
apply to data structures, such as promoting different data types, different
data sources, different data structures, and the like, through
progressive iteration and feedback.
[0241] In embodiments, a platform is provided having self-organization of data
pools based on utilization and/or yield metrics. In
embodiments, the data pools 4020 may be self-organizing data pools 4020, such
as being organized by cognitive capabilities as
described throughout this disclosure. The data pools 4020 may self-organize in
response to learning feedback 4012, such as based
on feedback of measures and results, including calculated in an analytic
system 4018. Organization may include determining what
data or packages of data to store in a pool (such as representing particular
combinations, permutations, aggregations, and the like),
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the structure of such data (such as in flat, hierarchical, linked, or other
structures), the duration of storage, the nature of storage
media (such as hard disks, flash memory, SSDs, network-based storage, or the
like), the arrangement of storage bits, and other
parameters. The content and nature of storage may be varied, such that a data
pool 4020 may learn and adapt, such as based on states
of the host processing system 112, one or more data collection systems 102,
storage environment parameters (such as capacity, cost,
and performance factors), data collection environment parameters, marketplace
parameters, and many others. In embodiments, pools
4020 may learn and adapt, such as by variation of the above and other
parameters in response to yield metrics (such as return on
investment, optimization of power utilization, optimization of revenue, and
the like).
[0242] Methods and systems are disclosed herein for a self-organizing
collector, including a self-organizing, multi-sensor data
collector that can optimize data collection, power and/or yield based on
conditions in its environment. The collector may, for
example, organize data collection by turning on and off particular sensors,
such as based on past utilization patterns or measures of
success, as managed by a machine learning facility that iterates
configurations and tracks measures of success. For example, a multi-
sensor collector may learn to turn off certain sensors when power levels are
low or during time periods where utilization of the data
from such sensors is low, or vice versa. Self-organization can also
automatically organize how data is collected (which sensors,
from what external sources), how data is stored (at what level of granularity
or compression, for how long, etc.), how data is
presented (such as in fused or multiplexed structures, in byte-like
structures, or in intermediate statistical structures (such as after
summing, subtraction, division, multiplication, squaring, normalization,
scaling, or other operations, and the like). This may be
improved over time, from an initial configuration, by training the self-
organizing facility based on data sets from real operating
environments, such as based on feedback measures, including many of the types
of feedback described throughout this disclosure.
[0243] In embodiments (Figure 17), signals from various sensors or input
sources (or selective combinations, permutations, mixes,
and the like as managed by one or more of the cognitive input selection
systems 4004, 4014) may provide input data to populate,
configure, modify, or otherwise determine the AR/VR element. Visual elements
may include a wide range of icons, map elements,
menu elements, sliders, toggles, colors, shapes, sizes, and the like, for
representation of analog sensor signals, digital signals, input
source information, and various combinations. In many examples, colors,
shapes, and sizes of visual overlay elements may represent
varying levels of input along the relevant dimensions for a sensor or
combination of sensors. In further examples, if a nearby
industrial machine is overheating, an AR element may alert a user by showing
an icon representing that type of machine in flashing
red color in a portion of the display of a pair of AR glasses. If a system is
experiencing unusual vibrations, a virtual reality interface
showing visualization of the components of the machine (such as overlaying a
camera view of the machine with 3D visualization
elements) may show a vibrating component in a highlighted color, with motion,
or the like, so that it stands out in a virtual reality
environment being used to help a user monitor or service the machine.
Clicking, touching, moving eyes toward, or otherwise
interacting with a visual element in an AR/VR interface may allow a user to
drill down and see underlying sensor or input data that
is used as an input to the display. Thus, through various forms of display, a
data collection system 102 may inform users of the need
to attend to one or more devices, machines, or other factors (such as in an
industrial environment), without requiring them to read
text-based messages or input or divert attention from the applicable
environment (whether it is a real environment with AR features
or a virtual environment, such as for simulation, training, or the like).
[0244] The AR/VR interface control system 4308, and selection and
configuration of what outputs or displays should be provided,
may be handled in the cognitive input selection systems 4004, 4014. For
example, user behavior (such as responses to inputs or
displays) may be monitored and analyzed in an analytic system 4018, and
feedback may be provided through the learning feedback
system 4012, so that AR/VR display signals may be provided based on the right
collection or package of sensors and inputs, at the
right time and in the right manner, to optimize the effectiveness of the AR/VR
UI 4308. This may include rule-based or model-
based feedback (such as providing outputs that correspond in some logical
fashion to the source data that is being conveyed). In
embodiments, a cognitively tuned AR/VR interface control system 4308 may be
provided, where selection of inputs or triggers for
AR/VR display elements, selection of outputs (such as colors, visual
representation elements, timing, intensity levels, durations and
other parameters [or weights applied to them]) and other parameters of a VR/AR
environment may be varied in a process of
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variation, promotion and selection (such as using genetic programming) with
feedback based on real world responses in actual
situations or based on results of simulation and testing of user behavior.
Thus, an adaptive, tuned AR/VR interface control system
4308 for a data collection system 102, or data collected thereby 102, or data
handled by a host processing system 112, is provided,
which may learn and adapt feedback to satisfy requirements and to optimize the
impact on user behavior and reaction, such as for
overall system outcomes, data collection outcomes, analytic outcomes, and the
like.
[0245] As noted above, methods and systems are disclosed herein for continuous
ultrasonic monitoring, including providing
continuous ultrasonic monitoring of rotating elements and bearings of an
energy production facility. Embodiments include using
continuous ultrasonic monitoring of an industrial environment as a source for
a cloud-deployed pattern recognizer. Embodiments
include using continuous ultrasonic monitoring to provide updated state
information to a state machine that is used as an input to a
cloud-based pattern recognizer. Embodiments include making available
continuous ultrasonic monitoring information to a user
based on a policy declared in a policy engine. Embodiments include storing
ultrasonic continuous monitoring data with other data
in a fused data structure on an industrial sensor device. Embodiments include
making a stream of continuous ultrasonic monitoring
data from an industrial environment available as a service from a data
marketplace. Embodiments include feeding a stream of
continuous ultrasonic data into a self-organizing data pool. Embodiments
include training a machine learning model to monitor a
continuous ultrasonic monitoring data stream where the model is based on a
training set created from human analysis of such a data
stream, and is improved based on data collected on performance in an
industrial environment. Embodiments include a swarm of
data collectors 4202 that include at least one data collector for continuous
ultrasonic monitoring of an industrial environment and at
least one other type of data collector. Embodiments include using a
distributed ledger to store time-series data from continuous
ultrasonic monitoring across multiple devices. Embodiments include collecting
a stream of continuous ultrasonic data in a self-
organizing data collector. Embodiments include collecting a stream of
continuous ultrasonic data in a network-sensitive data
collector.
[0246] Embodiments include collecting a stream of continuous ultrasonic data
in a remotely organized data collector.
Embodiments include collecting a stream of continuous ultrasonic data in a
data collector having self-organized storage 4028.
Embodiments include using self-organizing network coding to transport a stream
of ultrasonic data collected from an industrial
environment. Embodiments include conveying an indicator of a parameter of a
continuously collected ultrasonic data stream via a
sensory interface of a wearable device. Embodiments include conveying an
indicator of a parameter of a continuously collected
ultrasonic data stream via a heat map visual interface of a wearable device.
Embodiments include conveying an indicator of a
parameter of a continuously collected ultrasonic data stream via an interface
that operates with self-organized tuning of the interface
layer.
[0247] As noted above, methods and systems are disclosed herein for cloud-
based, machine pattern recognition based on fusion
of remote, analog industrial sensors. Embodiments include taking input from a
plurality of analog sensors disposed in an industrial
environment, multiplexing the sensors into a multiplexed data stream, feeding
the data stream into a cloud-deployed machine
learning facility, and training a model of the machine learning facility to
recognize a defined pattern associated with the industrial
environment. Embodiments include using a cloud-based pattern recognizer on
input states from a state machine that characterizes
states of an industrial environment. Embodiments include deploying policies by
a policy engine that govern what data can be used
by what users and for what purpose in cloud-based, machine learning.
Embodiments include feeding inputs from multiple devices
that have fused, on-device storage of multiple sensor streams into a cloud-
based pattern recognizer. Embodiments include making
an output from a cloud-based machine pattern recognizer that analyzes fused
data from remote, analog industrial sensors available
as a data service in a data marketplace. Embodiments include using a cloud-
based platform to identify patterns in data across a
plurality of data pools that contain data published from industrial sensors.
Embodiments include training a model to identify
preferred sensor sets to diagnose a condition of an industrial environment,
where a training set is created by a human user and the
model is improved based on feedback from data collected about conditions in an
industrial environment.
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[0248] Embodiments include a swarm of data collectors that is governed by a
policy that is automatically propagated through the
swarm. Embodiments include using a distributed ledger to store sensor fusion
information across multiple devices. Embodiments
include feeding input from a set of self-organizing data collectors into a
cloud-based pattern recognizer that uses data from multiple
sensors for an industrial environment. Embodiments include feeding input from
a set of network-sensitive data collectors into a
cloud-based pattern recognizer that uses data from multiple sensors from the
industrial environment. Embodiments include feeding
input from a set of remotely organized data collectors into a cloud-based
pattern recognizer that determines user data from multiple
sensors from the industrial environment. Embodiments include feeding input
from a set of data collectors having self-organized
storage into a cloud-based pattern recognizer that uses data from multiple
sensors from the industrial environment. Embodiments
include a system for data collection in an industrial environment with self-
organizing network coding for data transport of data fused
from multiple sensors in the environment. Embodiments include conveying
information formed by fusing inputs from multiple
sensors in an industrial data collection system in a multi-sensory interface.
Embodiments include conveying information formed by
fusing inputs from multiple sensors in an industrial data collection system in
a heat map interface. Embodiments include conveying
information formed by fusing inputs from multiple sensors in an industrial
data collection system in an interface that operates with
self-organized tuning of the interface layer.
[0249] As noted above, methods and systems are disclosed herein for cloud-
based, machine pattern analysis of state information
from multiple analog industrial sensors to provide anticipated state
information for an industrial system. Embodiments include
providing cloud-based pattern analysis of state information from multiple
analog industrial sensors to provide anticipated state
information for an industrial system. Embodiments include using a policy
engine to determine what state information can be used
for cloud-based machine analysis. Embodiments include feeding inputs from
multiple devices that have fused and on-device storage
of multiple sensor streams into a cloud-based pattern recognizer to determine
an anticipated state of an industrial environment.
Embodiments include making anticipated state information from a cloud-based
machine pattern recognizer that analyzes fused data
from remote, analog industrial sensors available as a data service in a data
marketplace. Embodiments include using a cloud-based
pattern recognizer to determine an anticipated state of an industrial
environment based on data collected from data pools that contain
streams of information from machines in the environment. Embodiments include
training a model to identify preferred state
information to diagnose a condition of an industrial environment, where a
training set is created by a human user and the model is
improved based on feedback from data collected about conditions in an
industrial environment. Embodiments include a swarm of
data collectors that feeds a state machine that maintains current state
information for an industrial environment. Embodiments
include using a distributed ledger to store historical state information for
fused sensor states a self-organizing data collector that
feeds a state machine that maintains current state information for an
industrial environment. Embodiments include a network-
sensitive data collector that feeds a state machine that maintains current
state information for an industrial environment.
Embodiments include a remotely organized data collector that feeds a state
machine that maintains current state information for an
industrial environment. Embodiments include a data collector with self-
organized storage that feeds a state machine that maintains
current state information for an industrial environment. Embodiments include a
system for data collection in an industrial
environment with self-organizing network coding for data transport and
maintains anticipated state information for the environment.
Embodiments include conveying anticipated state information determined by
machine learning in an industrial data collection
system in a multi-sensory interface. Embodiments include conveying anticipated
state information determined by machine learning
in an industrial data collection system in a heat map interface. Embodiments
include conveying anticipated state information
determined by machine learning in an industrial data collection system in an
interface that operates with self-organized tuning of
the interface layer.
[0250] As noted above, methods and systems are disclosed herein for a cloud-
based policy automation engine for IoT, with
creation, deployment, and management of IoT devices, including a cloud-based
policy automation engine for IoT, enabling creation,
deployment and management of policies that apply to IoT devices. Embodiments
include deploying a policy regarding data usage
to an on-device storage system that stores fused data from multiple industrial
sensors. Embodiments include deploying a policy
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relating to what data can be provided to whom in a self-organizing marketplace
for IoT sensor data. Embodiments include deploying
a policy across a set of self-organizing pools of data that contain data
streamed from industrial sensing devices to govern use of data
from the pools. Embodiments include training a model to determine what
policies should be deployed in an industrial data collection
system. Embodiments include deploying a policy that governs how a self-
organizing swarm should be organized for a particular
industrial environment. Embodiments include storing a policy on a device that
governs use of storage capabilities of the device for
a distributed ledger. Embodiments include deploying a policy that governs how
a self-organizing data collector should be organized
for a particular industrial environment. Embodiments include deploying a
policy that governs how a network-sensitive data collector
should use network bandwidth for a particular industrial environment.
Embodiments include deploying a policy that governs how a
remotely organized data collector should collect, and make available, data
relating to a specified industrial environment.
Embodiments include deploying a policy that governs how a data collector
should self-organize storage for a particular industrial
environment. Embodiments include a system for data collection in an industrial
environment with a policy engine for deploying
policy within the system and self-organizing network coding for data
transport. Embodiments include a system for data collection
in an industrial environment with a policy engine for deploying a policy
within the system, where a policy applies to how data will
be presented in a multi-sensory interface. Embodiments include a system for
data collection in an industrial environment with a
policy engine for deploying a policy within the system, where a policy applies
to how data will be presented in a heat map visual
interface. Embodiments include a system for data collection in an industrial
environment with a policy engine for deploying a policy
within the system, where a policy applies to how data will be presented in an
interface that operates with self-organized tuning of
the interface layer.
[0251] As noted above, methods and systems are disclosed herein for on-device
sensor fusion and data storage for industrial IoT
devices, including on-device sensor fusion and data storage for an industrial
IoT device, where data from multiple sensors is
multiplexed at the device for storage of a fused data stream. Embodiments
include a self-organizing marketplace that presents fused
sensor data that is extracted from on-device storage of IoT devices.
Embodiments include streaming fused sensor information from
multiple industrial sensors and from an on-device data storage facility to a
data pool. Embodiments include training a model to
determine what data should be stored on a device in a data collection
environment. Embodiments include a self-organizing swarm
of industrial data collectors that organize among themselves to optimize data
collection, where at least some of the data collectors
have on-device storage of fused data from multiple sensors. Embodiments
include storing distributed ledger information with fused
sensor information on an industrial IoT device. Embodiments include on-device
sensor fusion and data storage for a self-organizing
industrial data collector. Embodiments include on-device sensor fusion and
data storage for a network-sensitive industrial data
collector. Embodiments include on-device sensor fusion and data storage for a
remotely organized industrial data collector.
Embodiments include on-device sensor fusion and self-organizing data storage
for an industrial data collector. Embodiments include
a system for data collection in an industrial environment with on-device
sensor fusion and self-organizing network coding for data
transport. Embodiments include a system for data collection with on-device
sensor fusion of industrial sensor data, where data
structures are stored to support alternative, multi-sensory modes of
presentation. Embodiments include a system for data collection
with on-device sensor fusion of industrial sensor data, where data structures
are stored to support visual heat map modes of
presentation. Embodiments include a system for data collection with on-device
sensor fusion of industrial sensor data, where data
structures are stored to support an interface that operates with self-
organized tuning of the interface layer.
[0252] As noted above, methods and systems are disclosed herein for a self-
organizing data marketplace for industrial IoT data,
including a self-organizing data marketplace for industrial IoT data, where
available data elements are organized in the marketplace
for consumption by consumers based on training a self-organizing facility with
a training set and feedback from measures of
marketplace success. Embodiments include organizing a set of data pools in a
self-organizing data marketplace based on utilization
metrics for the data pools. Embodiments include training a model to determine
pricing for data in a data marketplace. Embodiments
include feeding a data marketplace with data streams from a self-organizing
swarm of industrial data collectors. Embodiments
include using a distributed ledger to store transactional data for a self-
organizing marketplace for industrial IoT data. Embodiments
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include feeding a data marketplace with data streams from self-organizing
industrial data collectors. Embodiments include feeding
a data marketplace with data streams from a set of network-sensitive
industrial data collectors. Embodiments include feeding a data
marketplace with data streams from a set of remotely organized industrial data
collectors. Embodiments include feeding a data
marketplace with data streams from a set of industrial data collectors that
have self-organizing storage. Embodiments include using
self-organizing network coding for data transport to a marketplace for sensor
data collected in industrial environments.
Embodiments include providing a library of data structures suitable for
presenting data in alternative, multi-sensory interface modes
in a data marketplace. Embodiments include providing a library in a data
marketplace of data structures suitable for presenting data
in heat map visualization. Embodiments include providing a library in a data
marketplace of data structures suitable for presenting
data in interfaces that operate with self-organized tuning of the interface
layer.
[0253] As noted above, methods and systems are disclosed herein for self-
organizing data pools, including self-organization of
data pools based on utilization and/or yield metrics, including utilization
and/or yield metrics that are tracked for a plurality of data
pools. Embodiments include training a model to present the most valuable data
in a data marketplace, where training is based on
industry-specific measures of success. Embodiments include populating a set of
self-organizing data pools with data from a self-
organizing swarm of data collectors. Embodiments include using a distributed
ledger to store transactional information for data that
is deployed in data pools, where the distributed ledger is distributed across
the data pools. Embodiments include self-organizing of
data pools based on utilization and/or yield metrics that are tracked for a
plurality of data pools, where the pools contain data from
self-organizing data collectors. Embodiments include populating a set of self-
organizing data pools with data from a set of network-
sensitive data collectors. Embodiments include populating a set of self-
organizing data pools with data from a set of remotely
organized data collectors. Embodiments include populating a set of self-
organizing data pools with data from a set of data collectors
having self-organizing storage. Embodiments include a system for data
collection in an industrial environment with self-organizing
pools for data storage and self-organizing network coding for data transport.
Embodiments include a system for data collection in
an industrial environment with self-organizing pools for data storage that
include a source data structure for supporting data
presentation in a multi-sensory interface. Embodiments include a system for
data collection in an industrial environment with self-
organizing pools for data storage that include a source data structure for
supporting data presentation in a heat map interface.
Embodiments include a system for data collection in an industrial environment
with self-organizing pools for data storage that
include source a data structure for supporting data presentation in an
interface that operates with self-organized tuning of the
interface layer.
[0254] As noted above, methods and systems are disclosed herein for training
Al models based on industry-specific feedback,
including training an Al model based on industry-specific feedback that
reflects a measure of utilization, yield, or impact, where the
Al model operates on sensor data from an industrial environment. Embodiments
include training a swarm of data collectors based
on industry-specific feedback. Embodiments include training an Al model to
identify and use available storage locations in an
industrial environment for storing distributed ledger information. Embodiments
include training a swarm of self-organizing data
collectors based on industry-specific feedback. Embodiments include training a
network-sensitive data collector based on network
and industrial conditions in an industrial environment. Embodiments include
training a remote organizer for a remotely organized
data collector based on industry-specific feedback measures. Embodiments
include training a self-organizing data collector to
configure storage based on industry-specific feedback. Embodiments include a
system for data collection in an industrial
environment with cloud-based training of a network coding model for organizing
network coding for data transport. Embodiments
include a system for data collection in an industrial environment with cloud-
based training of a facility that manages presentation
of data in a multi-sensory interface. Embodiments include a system for data
collection in an industrial environment with cloud-
based training of a facility that manages presentation of data in a heat map
interface. Embodiments include a system for data
collection in an industrial environment with cloud-based training of a
facility that manages presentation of data in an interface that
operates with self-organized tuning of the interface layer.
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[0255] As noted above, methods and systems are disclosed herein for a self-
organized swarm of industrial data collectors,
including a self-organizing swarm of industrial data collectors that organize
among themselves to optimize data collection based on
the capabilities and conditions of the members of the swarm. Embodiments
include deploying distributed ledger data structures
across a swarm of data. Embodiments include a self-organizing swarm of self-
organizing data collectors for data collection in
industrial environments. Embodiments include a self-organizing swarm of
network-sensitive data collectors for data collection in
industrial environments. Embodiments include a self-organizing swarm of
network-sensitive data collectors for data collection in
industrial environments, where the swarm is also configured for remote
organization. Embodiments include a self-organizing swarm
of data collectors having self-organizing storage for data collection in
industrial environments. Embodiments include a system for
data collection in an industrial environment with a self-organizing swarm of
data collectors and self-organizing network coding for
data transport. Embodiments include a system for data collection in an
industrial environment with a self-organizing swarm of data
collectors that relay information for use in a multi-sensory interface.
Embodiments include a system for data collection in an
industrial environment with a self-organizing swarm of data collectors that
relay information for use in a heat map interface.
Embodiments include a system for data collection in an industrial environment
with a self-organizing swarm of data collectors that
relay information for use in an interface that operates with self-organized
tuning of the interface layer.
[0256] As noted above, methods and systems are disclosed herein for an
industrial IoT distributed ledger, including a distributed
ledger supporting the tracking of transactions executed in an automated data
marketplace for industrial IoT data. Embodiments
include a self-organizing data collector that is configured to distribute
collected information to a distributed ledger. Embodiments
include a network-sensitive data collector that is configured to distribute
collected information to a distributed ledger based on
network conditions. Embodiments include a remotely organized data collector
that is configured to distribute collected information
to a distributed ledger based on intelligent, remote management of the
distribution. Embodiments include a data collector with self-
organizing local storage that is configured to distribute collected
information to a distributed ledger. Embodiments include a system
for data collection in an industrial environment using a distributed ledger
for data storage and self-organizing network coding for
data transport. Embodiments include a system for data collection in an
industrial environment using a distributed ledger for data
storage of a data structure supporting a haptic interface 4302 for data
presentation. Embodiments include a system for data collection
in an industrial environment using a distributed ledger for data storage of a
data structure supporting a heat map interface 4304 for
data presentation. Embodiments include a system for data collection in an
industrial environment using a distributed ledger for data
storage of a data structure supporting an interface that operates with self-
organized tuning of the interface layer.
[0257] As noted above, methods and systems are disclosed herein for a self-
organizing collector, including a self-organizing,
multi-sensor data collector that can optimize data collection, power and/or
yield based on conditions in its environment.
Embodiments include a self-organizing data collector that organizes at least
in part based on network conditions. Embodiments
include a self-organizing data collector that is also responsive to remote
organization. Embodiments include a self-organizing data
collector with self-organizing storage for data collected in an industrial
data collection environment. Embodiments include a system
for data collection in an industrial environment with self-organizing data
collection and self-organizing network coding for data
transport. Embodiments include a system for data collection in an industrial
environment with a self-organizing data collector that
feeds a data structure supporting a haptic or multi-sensory wearable interface
for data presentation. Embodiments include a system
for data collection in an industrial environment with a self-organizing data
collector that feeds a data structure supporting a heat
map interface for data presentation. Embodiments include a system for data
collection in an industrial environment with a self-
organizing data collector that feeds a data structure supporting an interface
that operates with self-organized tuning of the interface
layer.
[0258] In embodiments, a data collection and processing system is provided
having IP front-end signal conditioning on a
multiplexer for improved signal-to-noise ratio. In embodiments, a data
collection and processing system is provided having IP front-
end signal conditioning on a multiplexer for improved signal-to-noise ratio
and having multiplexer continuous monitoring alarming
features. In embodiments, a data collection and processing system is provided
having IP front-end signal conditioning on a
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multiplexer for improved signal-to-noise ratio and having the use of
distributed CPLD chips with dedicated bus for logic control of
multiple MUX and data acquisition sections. In embodiments, a data collection
and processing system is provided having IP front-
end signal conditioning on a multiplexer for improved signal-to-noise ratio
and having high-amperage input capability using solid
state relays and design topology. In embodiments, a data collection and
processing system is provided having IP front-end signal
conditioning on a multiplexer for improved signal-to-noise ratio and having
power-down capability of at least one analog sensor
channel and of a component board. In embodiments, a data collection and
processing system is provided having IP front-end signal
conditioning on a multiplexer for improved signal-to-noise ratio and having
unique electrostatic protection for trigger and vibration
inputs. In embodiments, a data collection and processing system is provided
having IP front-end signal conditioning on a multiplexer
for improved signal-to-noise ratio and having precise voltage reference for
A/D zero reference. In embodiments, a data collection
and processing system is provided having IP front-end signal conditioning on a
multiplexer for improved signal-to-noise ratio and
having a phase-lock loop band-pass tracking filter for obtaining slow-speed
RPMs and phase information. In embodiments, a data
collection and processing system is provided having IP front-end signal
conditioning on a multiplexer for improved signal-to-noise
ratio and having digital derivation of phase relative to input and trigger
channels using on-board timers. In embodiments, a data
collection and processing system is provided having IP front-end signal
conditioning on a multiplexer for improved signal-to-noise
ratio and having a peak-detector for auto-scaling that is routed into a
separate analog-to-digital converter for peak detection. In
embodiments, a data collection and processing system is provided having IP
front-end signal conditioning on a multiplexer for
improved signal-to-noise ratio and having routing of a trigger channel that is
either raw or buffered into other analog channels. In
embodiments, a data collection and processing system is provided having IP
front-end signal conditioning on a multiplexer for
improved signal-to-noise ratio and having the use of higher input oversampling
for delta-sigma A/D for lower sampling rate outputs
to minimize AA filter requirements. In embodiments, a data collection and
processing system is provided having IP front-end signal
conditioning on a multiplexer for improved signal-to-noise ratio and having
the use of a CPLD as a clock-divider for a delta-sigma
analog-to-digital converter to achieve lower sampling rates without the need
for digital resampling. In embodiments, a data
collection and processing system is provided having IP front-end signal
conditioning on a multiplexer for improved signal-to-noise
ratio and having long blocks of data at a high-sampling rate as opposed to
multiple sets of data taken at different sampling rates. In
embodiments, a data collection and processing system is provided having IP
front-end signal conditioning on a multiplexer for
improved signal-to-noise ratio and having storage of calibration data with
maintenance history on-board card set. In embodiments,
a data collection and processing system is provided having IP front-end signal
conditioning on a multiplexer for improved signal-
to-noise ratio and having a rapid route creation capability using hierarchical
templates. In embodiments, a data collection and
processing system is provided having IP front-end signal conditioning on a
multiplexer for improved signal-to-noise ratio and having
intelligent management of data collection bands. In embodiments, a data
collection and processing system is provided having IP
front-end signal conditioning on a multiplexer for improved signal-to-noise
ratio and having a neural net expert system using
intelligent management of data collection bands. In embodiments, a data
collection and processing system is provided having IP
front-end signal conditioning on a multiplexer for improved signal-to-noise
ratio and having use of a database hierarchy in sensor
data analysis. In embodiments, a data collection and processing system is
provided having IP front-end signal conditioning on a
multiplexer for improved signal-to-noise ratio and having an expert system GUI
graphical approach to defining intelligent data
collection bands and diagnoses for the expert system. In embodiments, a data
collection and processing system is provided having
IP front-end signal conditioning on a multiplexer for improved signal-to-noise
ratio and having a graphical approach for back-
calculation definition. In embodiments, a data collection and processing
system is provided having IP front-end signal conditioning
on a multiplexer for improved signal-to-noise ratio and having proposed
bearing analysis methods. In embodiments, a data collection
and processing system is provided having IP front-end signal conditioning on a
multiplexer for improved signal-to-noise ratio and
having torsional vibration detection/analysis utilizing transitory signal
analysis. In embodiments, a data collection and processing
system is provided having IP front-end signal conditioning on a multiplexer
for improved signal-to-noise ratio and having improved
integration using both analog and digital methods. In embodiments, a data
collection and processing system is provided having IP
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front-end signal conditioning on a multiplexer for improved signal-to-noise
ratio and having adaptive scheduling techniques for
continuous monitoring of analog data in a local environment. In embodiments, a
data collection and processing system is provided
having IP front-end signal conditioning on a multiplexer for improved signal-
to-noise ratio and having data acquisition parking
features. In embodiments, a data collection and processing system is provided
having IP front-end signal conditioning on a
multiplexer for improved signal-to-noise ratio and having a self-sufficient
data acquisition box. In embodiments, a data collection
and processing system is provided having IP front-end signal conditioning on a
multiplexer for improved signal-to-noise ratio and
having SD card storage. In embodiments, a data collection and processing
system is provided having IP front-end signal conditioning
on a multiplexer for improved signal-to-noise ratio and having extended
onboard statistical capabilities for continuous monitoring.
In embodiments, a data collection and processing system is provided having IP
front-end signal conditioning on a multiplexer for
improved signal-to-noise ratio and having the use of ambient, local and
vibration noise for prediction. In embodiments, a data
collection and processing system is provided having IP front-end signal
conditioning on a multiplexer for improved signal-to-noise
ratio and having smart route changes route based on incoming data or alarms to
enable simultaneous dynamic data for analysis or
correlation. In embodiments, a data collection and processing system is
provided having IP front-end signal conditioning on a
multiplexer for improved signal-to-noise ratio and having smart ODS and
transfer functions. In embodiments, a data collection and
processing system is provided having IP front-end signal conditioning on a
multiplexer for improved signal-to-noise ratio and having
a hierarchical multiplexer. In embodiments, a data collection and processing
system is provided having IP front-end signal
conditioning on a multiplexer for improved signal-to-noise ratio and having
identification of sensor overload. In embodiments, a
data collection and processing system is provided having IP front-end signal
conditioning on a multiplexer for improved signal-to-
noise ratio and having RF identification and an inclinometer. In embodiments,
a data collection and processing system is provided
having IP front-end signal conditioning on a multiplexer for improved signal-
to-noise ratio and having continuous ultrasonic
monitoring. In embodiments, a data collection and processing system is
provided having IP front-end signal conditioning on a
multiplexer for improved signal-to-noise ratio and having cloud-based, machine
pattern recognition based on the fusion of remote,
analog industrial sensors. In embodiments, a data collection and processing
system is provided having IP front-end signal
conditioning on a multiplexer for improved signal-to-noise ratio and having
cloud-based, machine pattern analysis of state
information from multiple analog industrial sensors to provide anticipated
state information for an industrial system. In
embodiments, a data collection and processing system is provided having IP
front-end signal conditioning on a multiplexer for
improved signal-to-noise ratio and having cloud-based policy automation engine
for IoT, with creation, deployment, and
management of IoT devices. In embodiments, a data collection and processing
system is provided having IP front-end signal
conditioning on a multiplexer for improved signal-to-noise ratio and having on-
device sensor fusion and data storage for industrial
IoT devices. In embodiments, a data collection and processing system is
provided having IP front-end signal conditioning on a
multiplexer for improved signal-to-noise ratio and having a self-organizing
data marketplace for industrial IoT data. In
embodiments, a data collection and processing system is provided having IP
front-end signal conditioning on a multiplexer for
improved signal-to-noise ratio and having self-organization of data pools
based on utilization and/or yield metrics. In embodiments,
a data collection and processing system is provided having IP front-end signal
conditioning on a multiplexer for improved signal-
to-noise ratio and having training Al models based on industry-specific
feedback. In embodiments, a data collection and processing
system is provided having IP front-end signal conditioning on a multiplexer
for improved signal-to-noise ratio and having a self-
organized swarm of industrial data collectors. In embodiments, a data
collection and processing system is provided having IP front-
end signal conditioning on a multiplexer for improved signal-to-noise ratio
and having an IoT distributed ledger. In embodiments,
a data collection and processing system is provided having IP front-end signal
conditioning on a multiplexer for improved signal-
to-noise ratio and having a self-organizing collector. In embodiments, a data
collection and processing system is provided having
IP front-end signal conditioning on a multiplexer for improved signal-to-noise
ratio and having a network-sensitive collector. In
embodiments, a data collection and processing system is provided having IP
front-end signal conditioning on a multiplexer for
improved signal-to-noise ratio and having a remotely organized collector. In
embodiments, a data collection and processing system
49
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is provided having IP front-end signal conditioning on a multiplexer for
improved signal-to-noise ratio and having a self-organizing
storage for a multi-sensor data collector. In embodiments, a data collection
and processing system is provided having IP front-end
signal conditioning on a multiplexer for improved signal-to-noise ratio and
having a self-organizing network coding for multi-sensor
data network. In embodiments, a data collection and processing system is
provided having IP front-end signal conditioning on a
multiplexer for improved signal-to-noise ratio and having a wearable haptic
user interface for an industrial sensor data collector,
with vibration, heat, electrical, and/or sound outputs. In embodiments, a data
collection and processing system is provided having
IP front-end signal conditioning on a multiplexer for improved signal-to-noise
ratio and having heat maps displaying collected data
for AR/VR. In embodiments, a data collection and processing system is provided
having IP front-end signal conditioning on a
multiplexer for improved signal-to-noise ratio and having automatically tuned
AR/VR visualization of data collected by a data
collector.
[0259] In embodiments, a data collection and processing system is provided
having multiplexer continuous monitoring alarming
features. In embodiments, a data collection and processing system is provided
having multiplexer continuous monitoring alarming
features and having the use of distributed CPLD chips with dedicated bus for
logic control of multiple MUX and data acquisition
sections. In embodiments, a data collection and processing system is provided
having multiplexer continuous monitoring alarming
features and having high-amperage input capability using solid state relays
and design topology. In embodiments, a data collection
and processing system is provided having multiplexer continuous monitoring
alarming features and having power-down capability
of at least one of an analog sensor channel and of a component board. In
embodiments, a data collection and processing system is
provided having multiplexer continuous monitoring alarming features and having
unique electrostatic protection for trigger and
vibration inputs. In embodiments, a data collection and processing system is
provided having multiplexer continuous monitoring
alarming features and having precise voltage reference for A/D zero reference.
In embodiments, a data collection and processing
system is provided having multiplexer continuous monitoring alarming features
and having a phase-lock loop band-pass tracking
filter for obtaining slow-speed RPMs and phase information. In embodiments, a
data collection and processing system is provided
having multiplexer continuous monitoring alarming features and having digital
derivation of phase relative to input and trigger
channels using on-board timers. In embodiments, a data collection and
processing system is provided having multiplexer continuous
monitoring alarming features and having a peak-detector for auto-scaling that
is routed into a separate analog-to-digital converter
for peak detection. In embodiments, a data collection and processing system is
provided having multiplexer continuous monitoring
alarming features and having routing of a trigger channel that is either raw
or buffered into other analog channels. In embodiments,
a data collection and processing system is provided having multiplexer
continuous monitoring alarming features and having the use
of higher input oversampling for delta-sigma A/D for lower sampling rate
outputs to minimize AA filter requirements. In
embodiments, a data collection and processing system is provided having
multiplexer continuous monitoring alarming features and
having the use of a CPLD as a clock-divider for a delta-sigma analog-to-
digital converter to achieve lower sampling rates without
the need for digital resampling. In embodiments, a data collection and
processing system is provided having multiplexer continuous
monitoring alarming features and having long blocks of data at a high-sampling
rate as opposed to multiple sets of data taken at
different sampling rates. In embodiments, a data collection and processing
system is provided having multiplexer continuous
monitoring alarming features and having storage of calibration data with
maintenance history on-board card set. In embodiments, a
data collection and processing system is provided having multiplexer
continuous monitoring alarming features and having a rapid
route creation capability using hierarchical templates. In embodiments, a data
collection and processing system is provided having
multiplexer continuous monitoring alarming features and having intelligent
management of data collection bands. In embodiments,
a data collection and processing system is provided having multiplexer
continuous monitoring alarming features and having a neural
net expert system using intelligent management of data collection bands. In
embodiments, a data collection and processing system
is provided having multiplexer continuous monitoring alarming features and
having use of a database hierarchy in sensor data
analysis. In embodiments, a data collection and processing system is provided
having multiplexer continuous monitoring alarming
features and having an expert system GUI graphical approach to defining
intelligent data collection bands and diagnoses for the
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expert system. In embodiments, a data collection and processing system is
provided having multiplexer continuous monitoring
alarming features and having a graphical approach for back-calculation
definition. In embodiments, a data collection and processing
system is provided having multiplexer continuous monitoring alarming features
and having proposed bearing analysis methods. In
embodiments, a data collection and processing system is provided having
multiplexer continuous monitoring alarming features and
having torsional vibration detection/analysis utilizing transitory signal
analysis. In embodiments, a data collection and processing
system is provided having multiplexer continuous monitoring alarming features
and having improved integration using both analog
and digital methods. In embodiments, a data collection and processing system
is provided having multiplexer continuous monitoring
alarming features and having adaptive scheduling techniques for continuous
monitoring of analog data in a local environment. In
embodiments, a data collection and processing system is provided having
multiplexer continuous monitoring alarming features and
having data acquisition parking features. In embodiments, a data collection
and processing system is provided having multiplexer
continuous monitoring alarming features and having a self-sufficient data
acquisition box. In embodiments, a data collection and
processing system is provided having multiplexer continuous monitoring
alarming features and having SD card storage. In
embodiments, a data collection and processing system is provided having
multiplexer continuous monitoring alarming features and
having extended onboard statistical capabilities for continuous monitoring. In
embodiments, a data collection and processing system
is provided having multiplexer continuous monitoring alarming features and
having the use of ambient, local and vibration noise
for prediction. In embodiments, a data collection and processing system is
provided having multiplexer continuous monitoring
alarming features and having smart route changes route based on incoming data
or alarms to enable simultaneous dynamic data for
analysis or correlation. In embodiments, a data collection and processing
system is provided having multiplexer continuous
monitoring alarming features and having smart ODS and transfer functions. In
embodiments, a data collection and processing system
is provided having multiplexer continuous monitoring alarming features and
having a hierarchical multiplexer. In embodiments, a
data collection and processing system is provided having multiplexer
continuous monitoring alarming features and having
identification of sensor overload. In embodiments, a data collection and
processing system is provided having multiplexer
continuous monitoring alarming features, and having RF identification, and an
inclinometer. In embodiments, a data collection and
processing system is provided having multiplexer continuous monitoring
alarming features and having continuous ultrasonic
monitoring. In embodiments, a data collection and processing system is
provided having multiplexer continuous monitoring
alarming features and having cloud-based, machine pattern recognition based on
the fusion of remote, analog industrial sensors. In
embodiments, a data collection and processing system is provided having
multiplexer continuous monitoring alarming features and
having cloud-based, machine pattern analysis of state information from
multiple analog industrial sensors to provide anticipated
state information for an industrial system. In embodiments, a data collection
and processing system is provided having multiplexer
continuous monitoring alarming features and having cloud-based policy
automation engine for IoT, with creation, deployment, and
management of IoT devices. In embodiments, a data collection and processing
system is provided having multiplexer continuous
monitoring alarming features and having on-device sensor fusion and data
storage for industrial IoT devices. In embodiments, a
data collection and processing system is provided having multiplexer
continuous monitoring alarming features and having a self-
organizing data marketplace for industrial IoT data. In embodiments, a data
collection and processing system is provided having
multiplexer continuous monitoring alarming features and having self-
organization of data pools based on utilization and/or yield
metrics. In embodiments, a data collection and processing system is provided
having multiplexer continuous monitoring alarming
features and having training Al models based on industry-specific feedback. In
embodiments, a data collection and processing
system is provided having multiplexer continuous monitoring alarming features
and having a self-organized swarm of industrial
data collectors. In embodiments, a data collection and processing system is
provided having multiplexer continuous monitoring
alarming features and having an IoT distributed ledger. In embodiments, a data
collection and processing system is provided having
multiplexer continuous monitoring alarming features and having a self-
organizing collector. In embodiments, a data collection and
processing system is provided having multiplexer continuous monitoring
alarming features and having a network-sensitive collector.
In embodiments, a data collection and processing system is provided having
multiplexer continuous monitoring alarming features
51
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and having a remotely organized collector. In embodiments, a data collection
and processing system is provided having multiplexer
continuous monitoring alarming features and having a self-organizing storage
for a multi-sensor data collector. In embodiments, a
data collection and processing system is provided having multiplexer
continuous monitoring alarming features and having a self-
organizing network coding for multi-sensor data network. In embodiments, a
data collection and processing system is provided
having multiplexer continuous monitoring alarming features and having a
wearable haptic user interface for an industrial sensor
data collector, with vibration, heat, electrical, and/or sound outputs. In
embodiments, a data collection and processing system is
provided having multiplexer continuous monitoring alarming features and having
heat maps displaying collected data for AR/VR.
In embodiments, a data collection and processing system is provided having
multiplexer continuous monitoring alarming features
and having automatically tuned AR/VR visualization of data collected by a data
collector.
[0260] In embodiments, a data collection and processing system is provided
having high-amperage input capability using solid
state relays and design topology. In embodiments, a data collection and
processing system is provided having high-amperage input
capability using solid state relays and design topology and having power-down
capability of at least one of an analog sensor channel
and of a component board. In embodiments, a data collection and processing
system is provided having high-amperage input
capability using solid state relays and design topology and having unique
electrostatic protection for trigger and vibration inputs. In
embodiments, a data collection and processing system is provided having high-
amperage input capability using solid state relays
and design topology and having precise voltage reference for A/D zero
reference. In embodiments, a data collection and processing
system is provided having high-amperage input capability using solid state
relays and design topology and having a phase-lock loop
band-pass tracking filter for obtaining slow-speed RPMs and phase information.
In embodiments, a data collection and processing
system is provided having high-amperage input capability using solid state
relays and design topology and having digital derivation
of phase relative to input and trigger channels using on-board timers. In
embodiments, a data collection and processing system is
provided having high-amperage input capability using solid state relays and
design topology and having a peak-detector for auto-
scaling that is routed into a separate analog-to-digital converter for peak
detection. In embodiments, a data collection and processing
system is provided having high-amperage input capability using solid state
relays and design topology and having routing of a
trigger channel that is either raw or buffered into other analog channels. In
embodiments, a data collection and processing system
is provided having high-amperage input capability using solid state relays and
design topology and having the use of higher input
oversampling for delta-sigma A/D for lower sampling rate outputs to minimize
AA filter requirements. In embodiments, a data
collection and processing system is provided having high-amperage input
capability using solid state relays and design topology
and having the use of a CPLD as a clock-divider for a delta-sigma analog-to-
digital converter to achieve lower sampling rates
without the need for digital resampling. In embodiments, a data collection and
processing system is provided having high-amperage
input capability using solid state relays and design topology and having long
blocks of data at a high-sampling rate as opposed to
multiple sets of data taken at different sampling rates. In embodiments, a
data collection and processing system is provided having
high-amperage input capability using solid state relays and design topology
and having storage of calibration data with maintenance
history on-board card set. In embodiments, a data collection and processing
system is provided having high-amperage input
capability using solid state relays and design topology and having a rapid
route creation capability using hierarchical templates. In
embodiments, a data collection and processing system is provided having high-
amperage input capability using solid state relays
and design topology and having intelligent management of data collection
bands. In embodiments, a data collection and processing
system is provided having high-amperage input capability using solid state
relays and design topology and having a neural net expert
system using intelligent management of data collection bands. In embodiments,
a data collection and processing system is provided
having high-amperage input capability using solid state relays and design
topology and having use of a database hierarchy in sensor
data analysis. In embodiments, a data collection and processing system is
provided having high-amperage input capability using
solid state relays and design topology and having an expert system GUI
graphical approach to defining intelligent data collection
bands and diagnoses for the expert system. In embodiments, a data collection
and processing system is provided having high-
amperage input capability using solid state relays and design topology and
having a graphical approach for back-calculation
52
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definition. In embodiments, a data collection and processing system is
provided having high-amperage input capability using solid
state relays and design topology and having proposed bearing analysis methods.
In embodiments, a data collection and processing
system is provided having high-amperage input capability using solid state
relays and design topology and having torsional vibration
detection/analysis utilizing transitory signal analysis. In embodiments, a
data collection and processing system is provided having
high-amperage input capability using solid state relays and design topology
and having improved integration using both analog and
digital methods. In embodiments, a data collection and processing system is
provided having high-amperage input capability using
solid state relays and design topology and having adaptive scheduling
techniques for continuous monitoring of analog data in a local
environment. In embodiments, a data collection and processing system is
provided having high-amperage input capability using
solid state relays and design topology and having data acquisition parking
features. In embodiments, a data collection and processing
system is provided having high-amperage input capability using solid state
relays and design topology and having a self-sufficient
data acquisition box. In embodiments, a data collection and processing system
is provided having high-amperage input capability
using solid state relays and design topology and having SD card storage. In
embodiments, a data collection and processing system
is provided having high-amperage input capability using solid state relays and
design topology and having extended onboard
statistical capabilities for continuous monitoring. In embodiments, a data
collection and processing system is provided having high-
amperage input capability using solid state relays and design topology and
having the use of ambient, local and vibration noise for
prediction. In embodiments, a data collection and processing system is
provided having high-amperage input capability using solid
state relays and design topology and having smart route changes route based on
incoming data or alarms to enable simultaneous
dynamic data for analysis or correlation. In embodiments, a data collection
and processing system is provided having high-amperage
input capability using solid state relays and design topology and having smart
ODS and transfer functions. In embodiments, a data
collection and processing system is provided having high-amperage input
capability using solid state relays and design topology
and having a hierarchical multiplexer. In embodiments, a data collection and
processing system is provided having high-amperage
input capability using solid state relays and design topology and having
identification of sensor overload. In embodiments, a data
collection and processing system is provided having high-amperage input
capability using solid state relays and design topology
and having RF identification and an inclinometer. In embodiments, a data
collection and processing system is provided having high-
amperage input capability using solid state relays and design topology and
having continuous ultrasonic monitoring. In
embodiments, a data collection and processing system is provided having high-
amperage input capability using solid state relays
and design topology and having cloud-based, machine pattern recognition based
on fusion of remote, analog industrial sensors. In
embodiments, a data collection and processing system is provided having high-
amperage input capability using solid state relays
and design topology and having cloud-based, machine pattern analysis of state
information from multiple analog industrial sensors
to provide anticipated state information for an industrial system. In
embodiments, a data collection and processing system is provided
having high-amperage input capability using solid state relays and design
topology and having cloud-based policy automation engine
for IoT, with creation, deployment, and management of IoT devices. In
embodiments, a data collection and processing system is
provided having high-amperage input capability using solid state relays and
design topology and having on-device sensor fusion
and data storage for industrial IoT devices. In embodiments, a data collection
and processing system is provided having high-
amperage input capability using solid state relays and design topology and
having a self-organizing data marketplace for industrial
IoT data. In embodiments, a data collection and processing system is provided
having high-amperage input capability using solid
state relays and design topology and having self-organization of data pools
based on utilization and/or yield metrics. In
embodiments, a data collection and processing system is provided having high-
amperage input capability using solid state relays
and design topology and having training Al models based on industry-specific
feedback. In embodiments, a data collection and
processing system is provided having high-amperage input capability using
solid state relays and design topology and having a self-
organized swarm of industrial data collectors. In embodiments, a data
collection and processing system is provided having high-
amperage input capability using solid state relays and design topology and
having an IoT distributed ledger. In embodiments, a data
collection and processing system is provided having high-amperage input
capability using solid state relays and design topology
53
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and having a self-organizing collector. In embodiments, a data collection and
processing system is provided having high-amperage
input capability using solid state relays and design topology and having a
network-sensitive collector. In embodiments, a data
collection and processing system is provided having high-amperage input
capability using solid state relays and design topology
and having a remotely organized collector. In embodiments, a data collection
and processing system is provided having high-
amperage input capability using solid state relays and design topology and
having a self-organizing storage for a multi-sensor data
collector. In embodiments, a data collection and processing system is provided
having high-amperage input capability using solid
state relays and design topology and having a self-organizing network coding
for multi-sensor data network. In embodiments, a data
collection and processing system is provided having high-amperage input
capability using solid state relays and design topology
and having a wearable haptic user interface for an industrial sensor data
collector, with vibration, heat, electrical, and/or sound
outputs. In embodiments, a data collection and processing system is provided
having high-amperage input capability using solid
state relays and design topology and having heat maps displaying collected
data for AR/VR. In embodiments, a data collection and
processing system is provided having high-amperage input capability using
solid state relays and design topology and having
automatically tuned AR/VR visualization of data collected by a data collector.
[0261] In embodiments, a data collection and processing system is provided
having unique electrostatic protection for trigger and
vibration inputs. In embodiments, a data collection and processing system is
provided having unique electrostatic protection for
trigger and vibration inputs and having precise voltage reference for A/D zero
reference. In embodiments, a data collection and
processing system is provided having unique electrostatic protection for
trigger and vibration inputs and having a phase-lock loop
band-pass tracking filter for obtaining slow-speed RPMs and phase information.
In embodiments, a data collection and processing
system is provided having unique electrostatic protection for trigger and
vibration inputs and having digital derivation of phase
relative to input and trigger channels using on-board timers. In embodiments,
a data collection and processing system is provided
having unique electrostatic protection for trigger and vibration inputs and
having a peak-detector for auto-scaling that is routed into
a separate analog-to-digital converter for peak detection. In embodiments, a
data collection and processing system is provided having
unique electrostatic protection for trigger and vibration inputs and having
routing of a trigger channel that is either raw or buffered
into other analog channels. In embodiments, a data collection and processing
system is provided having unique electrostatic
protection for trigger and vibration inputs and having the use of higher input
oversampling for delta-sigma A/D for lower sampling
rate outputs to minimize AA filter requirements. In embodiments, a data
collection and processing system is provided having unique
electrostatic protection for trigger and vibration inputs and having the use
of a CPLD as a clock-divider for a delta-sigma analog-
to-digital converter to achieve lower sampling rates without the need for
digital resampling. In embodiments, a data collection and
processing system is provided having unique electrostatic protection for
trigger and vibration inputs and having long blocks of data
at a high-sampling rate as opposed to multiple sets of data taken at different
sampling rates. In embodiments, a data collection and
processing system is provided having unique electrostatic protection for
trigger and vibration inputs and having storage of calibration
data with maintenance history on-board card set. In embodiments, a data
collection and processing system is provided having unique
electrostatic protection for trigger and vibration inputs and having a rapid
route creation capability using hierarchical templates. In
embodiments, a data collection and processing system is provided having unique
electrostatic protection for trigger and vibration
inputs and having intelligent management of data collection bands. In
embodiments, a data collection and processing system is
provided having unique electrostatic protection for trigger and vibration
inputs and having a neural net expert system using
intelligent management of data collection bands. In embodiments, a data
collection and processing system is provided having unique
electrostatic protection for trigger and vibration inputs and having use of a
database hierarchy in sensor data analysis. In
embodiments, a data collection and processing system is provided having unique
electrostatic protection for trigger and vibration
inputs and having an expert system GUI graphical approach to defining
intelligent data collection bands and diagnoses for the expert
system. In embodiments, a data collection and processing system is provided
having unique electrostatic protection for trigger and
vibration inputs and having a graphical approach for back-calculation
definition. In embodiments, a data collection and processing
system is provided having unique electrostatic protection for trigger and
vibration inputs and having proposed bearing analysis
54
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methods. In embodiments, a data collection and processing system is provided
having unique electrostatic protection for trigger and
vibration inputs and having torsional vibration detection/analysis utilizing
transitory signal analysis. In embodiments, a data
collection and processing system is provided having unique electrostatic
protection for trigger and vibration inputs and having
improved integration using both analog and digital methods. In embodiments, a
data collection and processing system is provided
having unique electrostatic protection for trigger and vibration inputs and
having adaptive scheduling techniques for continuous
monitoring of analog data in a local environment. In embodiments, a data
collection and processing system is provided having
unique electrostatic protection for trigger and vibration inputs and having
data acquisition parking features. In embodiments, a data
collection and processing system is provided having unique electrostatic
protection for trigger and vibration inputs and having a
self-sufficient data acquisition box. In embodiments, a data collection and
processing system is provided having unique electrostatic
protection for trigger and vibration inputs and having SD card storage. In
embodiments, a data collection and processing system is
provided having unique electrostatic protection for trigger and vibration
inputs and having extended onboard statistical capabilities
for continuous monitoring. In embodiments, a data collection and processing
system is provided having unique electrostatic
protection for trigger and vibration inputs and having the use of ambient,
local and vibration noise for prediction. In embodiments,
a data collection and processing system is provided having unique
electrostatic protection for trigger and vibration inputs and having
smart route changes route based on incoming data or alarms to enable
simultaneous dynamic data for analysis or correlation. In
embodiments, a data collection and processing system is provided having unique
electrostatic protection for trigger and vibration
inputs and having smart ODS and transfer functions. In embodiments, a data
collection and processing system is provided having
unique electrostatic protection for trigger and vibration inputs and having a
hierarchical multiplexer. In embodiments, a data
collection and processing system is provided having unique electrostatic
protection for trigger and vibration inputs and having
identification of sensor overload. In embodiments, a data collection and
processing system is provided having unique electrostatic
protection for trigger and vibration inputs and having RF identification and
an inclinometer. In embodiments, a data collection and
processing system is provided having unique electrostatic protection for
trigger and vibration inputs and having continuous
ultrasonic monitoring. In embodiments, a data collection and processing system
is provided having unique electrostatic protection
for trigger and vibration inputs and having cloud-based, machine pattern
recognition based on fusion of remote, analog industrial
sensors. In embodiments, a data collection and processing system is provided
having unique electrostatic protection for trigger and
vibration inputs and having cloud-based, machine pattern analysis of state
information from multiple analog industrial sensors to
provide anticipated state information for an industrial system. In
embodiments, a data collection and processing system is provided
having unique electrostatic protection for trigger and vibration inputs and
having cloud-based policy automation engine for IoT,
with creation, deployment, and management of IoT devices. In embodiments, a
data collection and processing system is provided
having unique electrostatic protection for trigger and vibration inputs and
having on-device sensor fusion and data storage for
industrial IoT devices. In embodiments, a data collection and processing
system is provided having unique electrostatic protection
for trigger and vibration inputs and having a self-organizing data marketplace
for industrial IoT data. In embodiments, a data
collection and processing system is provided having unique electrostatic
protection for trigger and vibration inputs and having self-
organization of data pools based on utilization and/or yield metrics. In
embodiments, a data collection and processing system is
provided having unique electrostatic protection for trigger and vibration
inputs and having training Al models based on industry-
specific feedback. In embodiments, a data collection and processing system is
provided having unique electrostatic protection for
trigger and vibration inputs and having a self-organized swarm of industrial
data collectors. In embodiments, a data collection and
processing system is provided having unique electrostatic protection for
trigger and vibration inputs and having an IoT distributed
ledger. In embodiments, a data collection and processing system is provided
having unique electrostatic protection for trigger and
vibration inputs and having a self-organizing collector. In embodiments, a
data collection and processing system is provided having
unique electrostatic protection for trigger and vibration inputs and having a
network-sensitive collector. In embodiments, a data
collection and processing system is provided having unique electrostatic
protection for trigger and vibration inputs and having a
remotely organized collector. In embodiments, a data collection and processing
system is provided having unique electrostatic
CA 03082398 2020-05-11
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protection for trigger and vibration inputs and having a self-organizing
storage for a multi-sensor data collector. In embodiments, a
data collection and processing system is provided having unique electrostatic
protection for trigger and vibration inputs and having
a self-organizing network coding for multi-sensor data network. In
embodiments, a data collection and processing system is provided
having unique electrostatic protection for trigger and vibration inputs and
having a wearable haptic user interface for an industrial
sensor data collector, with vibration, heat, electrical and/or sound outputs.
In embodiments, a data collection and processing system
is provided having unique electrostatic protection for trigger and vibration
inputs and having heat maps displaying collected data
for AR/VR. In embodiments, a data collection and processing system is provided
having unique electrostatic protection for trigger
and vibration inputs and having automatically tuned AR/VR visualization of
data collected by a data collector.
[0262] In embodiments, a data collection and processing system is provided
having precise voltage reference for A/D zero
reference. In embodiments, a data collection and processing system is provided
having precise voltage reference for A/D zero
reference and having a phase-lock loop band-pass tracking filter for obtaining
slow-speed RPMs and phase information. In
embodiments, a data collection and processing system is provided having
precise voltage reference for A/D zero reference and
having digital derivation of phase relative to input and trigger channels
using on-board timers. In embodiments, a data collection
and processing system is provided having precise voltage reference for A/D
zero reference and having a peak-detector for auto-
scaling that is routed into a separate analog-to-digital converter for peak
detection. In embodiments, a data collection and processing
system is provided having precise voltage reference for A/D zero reference and
having routing of a trigger channel that is either raw
or buffered into other analog channels. In embodiments, a data collection and
processing system is provided having precise voltage
reference for A/D zero reference and having the use of higher input
oversampling for delta-sigma A/D for lower sampling rate
outputs to minimize AA filter requirements. In embodiments, a data collection
and processing system is provided having precise
voltage reference for A/D zero reference and having the use of a CPLD as a
clock-divider for a delta-sigma analog-to-digital
converter to achieve lower sampling rates without the need for digital
resampling. In embodiments, a data collection and processing
system is provided having precise voltage reference for A/D zero reference and
having long blocks of data at a high-sampling rate
as opposed to multiple sets of data taken at different sampling rates. In
embodiments, a data collection and processing system is
provided having precise voltage reference for A/D zero reference and having
storage of calibration data with maintenance history
on-board card set. In embodiments, a data collection and processing system is
provided having precise voltage reference for A/D
zero reference and having a rapid route creation capability using hierarchical
templates. In embodiments, a data collection and
processing system is provided having precise voltage reference for A/D zero
reference and having intelligent management of data
collection bands. In embodiments, a data collection and processing system is
provided having precise voltage reference for A/D
zero reference and having a neural net expert system using intelligent
management of data collection bands. In embodiments, a data
collection and processing system is provided having precise voltage reference
for A/D zero reference and having use of a database
hierarchy in sensor data analysis. In embodiments, a data collection and
processing system is provided having precise voltage
reference for A/D zero reference and having an expert system GUI graphical
approach to defining intelligent data collection bands
and diagnoses for the expert system. In embodiments, a data collection and
processing system is provided having precise voltage
reference for A/D zero reference and having a graphical approach for back-
calculation definition. In embodiments, a data collection
and processing system is provided having precise voltage reference for A/D
zero reference and having proposed bearing analysis
methods. In embodiments, a data collection and processing system is provided
having precise voltage reference for A/D zero
reference and having torsional vibration detection/analysis utilizing
transitory signal analysis. In embodiments, a data collection and
processing system is provided having precise voltage reference for A/D zero
reference and having improved integration using both
analog and digital methods. In embodiments, a data collection and processing
system is provided having precise voltage reference
for A/D zero reference and having adaptive scheduling techniques for
continuous monitoring of analog data in a local environment.
In embodiments, a data collection and processing system is provided having
precise voltage reference for A/D zero reference and
having data acquisition parking features. In embodiments, a data collection
and processing system is provided having precise voltage
reference for A/D zero reference and having a self-sufficient data acquisition
box. In embodiments, a data collection and processing
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system is provided having precise voltage reference for A/D zero reference and
having SD card storage. In embodiments, a data
collection and processing system is provided having precise voltage reference
for A/D zero reference and having extended onboard
statistical capabilities for continuous monitoring. In embodiments, a data
collection and processing system is provided having
precise voltage reference for A/D zero reference and having the use of
ambient, local and vibration noise for prediction. In
embodiments, a data collection and processing system is provided having
precise voltage reference for A/D zero reference and
having smart route changes route based on incoming data or alarms to enable
simultaneous dynamic data for analysis or correlation.
In embodiments, a data collection and processing system is provided having
precise voltage reference for A/D zero reference and
having smart ODS and transfer functions. In embodiments, a data collection and
processing system is provided having precise
voltage reference for A/D zero reference and having a hierarchical
multiplexer. In embodiments, a data collection and processing
system is provided having precise voltage reference for A/D zero reference and
having identification of sensor overload. In
embodiments, a data collection and processing system is provided having
precise voltage reference for A/D zero reference and
having RF identification and an inclinometer. In embodiments, a data
collection and processing system is provided having precise
voltage reference for A/D zero reference and having continuous ultrasonic
monitoring. In embodiments, a data collection and
processing system is provided having precise voltage reference for A/D zero
reference and having cloud-based, machine pattern
recognition based on fusion of remote, analog industrial sensors. In
embodiments, a data collection and processing system is
provided having precise voltage reference for A/D zero reference and having
cloud-based, machine pattern analysis of state
information from multiple analog industrial sensors to provide anticipated
state information for an industrial system. In
embodiments, a data collection and processing system is provided having
precise voltage reference for A/D zero reference and
having cloud-based policy automation engine for IoT, with creation,
deployment, and management of IoT devices. In embodiments,
a data collection and processing system is provided having precise voltage
reference for A/D zero reference and having on-device
sensor fusion and data storage for industrial IoT devices. In embodiments, a
data collection and processing system is provided
having precise voltage reference for A/D zero reference and having a self-
organizing data marketplace for industrial IoT data. In
embodiments, a data collection and processing system is provided having
precise voltage reference for A/D zero reference and
having self-organization of data pools based on utilization and/or yield
metrics. In embodiments, a data collection and processing
system is provided having precise voltage reference for A/D zero reference and
having training Al models based on industry-specific
feedback. In embodiments, a data collection and processing system is provided
having precise voltage reference for A/D zero
reference and having a self-organized swarm of industrial data collectors. In
embodiments, a data collection and processing system
is provided having precise voltage reference for A/D zero reference and having
an IoT distributed ledger. In embodiments, a data
collection and processing system is provided having precise voltage reference
for A/D zero reference and having a self-organizing
collector. In embodiments, a data collection and processing system is provided
having precise voltage reference for A/D zero
reference and having a network-sensitive collector. In embodiments, a data
collection and processing system is provided having
precise voltage reference for A/D zero reference and having a remotely
organized collector. In embodiments, a data collection and
processing system is provided having precise voltage reference for A/D zero
reference and having a self-organizing storage for a
multi-sensor data collector. In embodiments, a data collection and processing
system is provided having precise voltage reference
for A/D zero reference and having a self-organizing network coding for multi-
sensor data network. In embodiments, a data collection
and processing system is provided having precise voltage reference for A/D
zero reference and having a wearable haptic user
interface for an industrial sensor data collector, with vibration, heat,
electrical and/or sound outputs. In embodiments, a data
collection and processing system is provided having precise voltage reference
for A/D zero reference and having heat maps
displaying collected data for AR/VR. In embodiments, a data collection and
processing system is provided having precise voltage
reference for A/D zero reference and having automatically tuned AR/VR
visualization of data collected by a data collector.
[0263] In embodiments, a data collection and processing system is provided
having a phase-lock loop band-pass tracking filter for
obtaining slow-speed RPMs and phase information. In embodiments, a data
collection and processing system is provided having a
phase-lock loop band-pass tracking filter for obtaining slow-speed RPMs and
phase information and having digital derivation of
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phase relative to input and trigger channels using on-board timers. In
embodiments, a data collection and processing system is
provided having a phase-lock loop band-pass tracking filter for obtaining slow-
speed RPMs and phase information and having a
peak-detector for auto-scaling that is routed into a separate analog-to-
digital converter for peak detection. In embodiments, a data
collection and processing system is provided having a phase-lock loop band-
pass tracking filter for obtaining slow-speed RPMs and
phase information and having routing of a trigger channel that is either raw
or buffered into other analog channels. In embodiments,
a data collection and processing system is provided having a phase-lock loop
band-pass tracking filter for obtaining slow-speed
RPMs and phase information and having the use of higher input oversampling for
delta-sigma A/D for lower sampling rate outputs
to minimize AA filter requirements. In embodiments, a data collection and
processing system is provided having a phase-lock loop
band-pass tracking filter for obtaining slow-speed RPMs and phase information
and having the use of a CPLD as a clock-divider
for a delta-sigma analog-to-digital converter to achieve lower sampling rates
without the need for digital resampling. In
embodiments, a data collection and processing system is provided having a
phase-lock loop band-pass tracking filter for obtaining
slow-speed RPMs and phase information and having long blocks of data at a high-
sampling rate as opposed to multiple sets of data
taken at different sampling rates. In embodiments, a data collection and
processing system is provided having a phase-lock loop
band-pass tracking filter for obtaining slow-speed RPMs and phase information
and having storage of calibration data with
maintenance history on-board card set. In embodiments, a data collection and
processing system is provided having a phase-lock
loop band-pass tracking filter for obtaining slow-speed RPMs and phase
information and having a rapid route creation capability
using hierarchical templates. In embodiments, a data collection and processing
system is provided having a phase-lock loop band-
pass tracking filter for obtaining slow-speed RPMs and phase information and
having intelligent management of data collection
bands. In embodiments, a data collection and processing system is provided
having a phase-lock loop band-pass tracking filter for
obtaining slow-speed RPMs and phase information and having a neural net expert
system using intelligent management of data
collection bands. In embodiments, a data collection and processing system is
provided having a phase-lock loop band-pass tracking
filter for obtaining slow-speed RPMs and phase information and having use of a
database hierarchy in sensor data analysis. In
embodiments, a data collection and processing system is provided having a
phase-lock loop band-pass tracking filter for obtaining
slow-speed RPMs and phase information and having an expert system GUI
graphical approach to defining intelligent data collection
bands and diagnoses for the expert system. In embodiments, a data collection
and processing system is provided having a phase-
lock loop band-pass tracking filter for obtaining slow-speed RPMs and phase
information and having a graphical approach for back-
calculation definition. In embodiments, a data collection and processing
system is provided having a phase-lock loop band-pass
tracking filter for obtaining slow-speed RPMs and phase information and having
proposed bearing analysis methods. In
embodiments, a data collection and processing system is provided having a
phase-lock loop band-pass tracking filter for obtaining
slow-speed RPMs and phase information and having torsional vibration
detection/analysis utilizing transitory signal analysis. In
embodiments, a data collection and processing system is provided having a
phase-lock loop band-pass tracking filter for obtaining
slow-speed RPMs and phase information and having improved integration using
both analog and digital methods. In embodiments,
a data collection and processing system is provided having a phase-lock loop
band-pass tracking filter for obtaining slow-speed
RPMs and phase information and having adaptive scheduling techniques for
continuous monitoring of analog data in a local
environment. In embodiments, a data collection and processing system is
provided having a phase-lock loop band-pass tracking
filter for obtaining slow-speed RPMs and phase information and having data
acquisition parking features. In embodiments, a data
collection and processing system is provided having a phase-lock loop band-
pass tracking filter for obtaining slow-speed RPMs and
phase information and having a self-sufficient data acquisition box. In
embodiments, a data collection and processing system is
provided having a phase-lock loop band-pass tracking filter for obtaining slow-
speed RPMs and phase information and having SD
card storage. In embodiments, a data collection and processing system is
provided having a phase-lock loop band-pass tracking
filter for obtaining slow-speed RPMs and phase information and having extended
onboard statistical capabilities for continuous
monitoring. In embodiments, a data collection and processing system is
provided having a phase-lock loop band-pass tracking filter
for obtaining slow-speed RPMs and phase information and having the use of
ambient, local and vibration noise for prediction. In
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embodiments, a data collection and processing system is provided having a
phase-lock loop band-pass tracking filter for obtaining
slow-speed RPMs and phase information and having smart route changes route
based on incoming data or alarms to enable
simultaneous dynamic data for analysis or correlation. In embodiments, a data
collection and processing system is provided having
a phase-lock loop band-pass tracking filter for obtaining slow-speed RPMs and
phase information and having smart ODS and
transfer functions. In embodiments, a data collection and processing system is
provided having a phase-lock loop band-pass tracking
filter for obtaining slow-speed RPMs and phase information and having a
hierarchical multiplexer. In embodiments, a data collection
and processing system is provided having a phase-lock loop band-pass tracking
filter for obtaining slow-speed RPMs and phase
information and having identification of sensor overload. In embodiments, a
data collection and processing system is provided
having a phase-lock loop band-pass tracking filter for obtaining slow-speed
RPMs and phase information and having RF
identification and an inclinometer. In embodiments, a data collection and
processing system is provided having a phase-lock loop
band-pass tracking filter for obtaining slow-speed RPMs and phase information
and having continuous ultrasonic monitoring. In
embodiments, a data collection and processing system is provided having a
phase-lock loop band-pass tracking filter for obtaining
slow-speed RPMs and phase information and having cloud-based, machine pattern
recognition based on fusion of remote, analog
industrial sensors. In embodiments, a data collection and processing system is
provided having a phase-lock loop band-pass tracking
filter for obtaining slow-speed RPMs and phase information and having cloud-
based, machine pattern analysis of state information
from multiple analog industrial sensors to provide anticipated state
information for an industrial system. In embodiments, a data
collection and processing system is provided having a phase-lock loop band-
pass tracking filter for obtaining slow-speed RPMs and
phase information and having cloud-based policy automation engine for IoT,
with creation, deployment, and management of IoT
devices. In embodiments, a data collection and processing system is provided
having a phase-lock loop band-pass tracking filter for
obtaining slow-speed RPMs and phase information and having on-device sensor
fusion and data storage for industrial IoT devices.
In embodiments, a data collection and processing system is provided having a
phase-lock loop band-pass tracking filter for obtaining
slow-speed RPMs and phase information and having a self-organizing data
marketplace for industrial IoT data. In embodiments, a
data collection and processing system is provided having a phase-lock loop
band-pass tracking filter for obtaining slow-speed RPMs
and phase information and having self-organization of data pools based on
utilization and/or yield metrics. In embodiments, a data
collection and processing system is provided having a phase-lock loop band-
pass tracking filter for obtaining slow-speed RPMs and
phase information and having training Al models based on industry-specific
feedback. In embodiments, a data collection and
processing system is provided having a phase-lock loop band-pass tracking
filter for obtaining slow-speed RPMs and phase
information and having a self-organized swarm of industrial data collectors.
In embodiments, a data collection and processing
system is provided having a phase-lock loop band-pass tracking filter for
obtaining slow-speed RPMs and phase information and
having an IoT distributed ledger. In embodiments, a data collection and
processing system is provided having a phase-lock loop
band-pass tracking filter for obtaining slow-speed RPMs and phase information
and having a self-organizing collector. In
embodiments, a data collection and processing system is provided having a
phase-lock loop band-pass tracking filter for obtaining
slow-speed RPMs and phase information and having a network-sensitive
collector. In embodiments, a data collection and processing
system is provided having a phase-lock loop band-pass tracking filter for
obtaining slow-speed RPMs and phase information and
having a remotely organized collector. In embodiments, a data collection and
processing system is provided having a phase-lock
loop band-pass tracking filter for obtaining slow-speed RPMs and phase
information and having a self-organizing storage for a
multi-sensor data collector. In embodiments, a data collection and processing
system is provided having a phase-lock loop band-
pass tracking filter for obtaining slow-speed RPMs and phase information and
having a self-organizing network coding for multi-
sensor data network. In embodiments, a data collection and processing system
is provided having a phase-lock loop band-pass
tracking filter for obtaining slow-speed RPMs and phase information and having
a wearable haptic user interface for an industrial
sensor data collector, with vibration, heat, electrical and/or sound outputs.
In embodiments, a data collection and processing system
is provided having a phase-lock loop band-pass tracking filter for obtaining
slow-speed RPMs and phase information and having
heat maps displaying collected data for AR/VR. In embodiments, a data
collection and processing system is provided having a
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phase-lock loop band-pass tracking filter for obtaining slow-speed RPMs and
phase information and having automatically tuned
AR/VR visualization of data collected by a data collector.
[0264] In embodiments, a data collection and processing system is provided
having digital derivation of phase relative to input
and trigger channels using on-board timers. In embodiments, a data collection
and processing system is provided having digital
derivation of phase relative to input and trigger channels using on-board
timers and having a peak-detector for auto-scaling that is
routed into a separate analog-to-digital converter for peak detection. In
embodiments, a data collection and processing system is
provided having digital derivation of phase relative to input and trigger
channels using on-board timers and having routing of a
trigger channel that is either raw or buffered into other analog channels. In
embodiments, a data collection and processing system
is provided having digital derivation of phase relative to input and trigger
channels using on-board timers and having the use of
higher input oversampling for delta-sigma A/D for lower sampling rate outputs
to minimize AA filter requirements. In embodiments,
a data collection and processing system is provided having digital derivation
of phase relative to input and trigger channels using
on-board timers and having the use of a CPLD as a clock-divider for a delta-
sigma analog-to-digital converter to achieve lower
sampling rates without the need for digital resampling. In embodiments, a data
collection and processing system is provided having
digital derivation of phase relative to input and trigger channels using on-
board timers and having long blocks of data at a high-
sampling rate as opposed to multiple sets of data taken at different sampling
rates. In embodiments, a data collection and processing
system is provided having digital derivation of phase relative to input and
trigger channels using on-board timers and having storage
of calibration data with maintenance history on-board card set. In
embodiments, a data collection and processing system is provided
having digital derivation of phase relative to input and trigger channels
using on-board timers and having a rapid route creation
capability using hierarchical templates. In embodiments, a data collection and
processing system is provided having digital
derivation of phase relative to input and trigger channels using on-board
timers and having intelligent management of data collection
bands. In embodiments, a data collection and processing system is provided
having digital derivation of phase relative to input and
trigger channels using on-board timers and having a neural net expert system
using intelligent management of data collection bands.
In embodiments, a data collection and processing system is provided having
digital derivation of phase relative to input and trigger
channels using on-board timers and having use of a database hierarchy in
sensor data analysis. In embodiments, a data collection
and processing system is provided having digital derivation of phase relative
to input and trigger channels using on-board timers
and having an expert system GUI graphical approach to defining intelligent
data collection bands and diagnoses for the expert
system. In embodiments, a data collection and processing system is provided
having digital derivation of phase relative to input and
trigger channels using on-board timers and having a graphical approach for
back-calculation definition. In embodiments, a data
collection and processing system is provided having digital derivation of
phase relative to input and trigger channels using on-board
timers and having proposed bearing analysis methods. In embodiments, a data
collection and processing system is provided having
digital derivation of phase relative to input and trigger channels using on-
board timers and having torsional vibration
detection/analysis utilizing transitory signal analysis. In embodiments, a
data collection and processing system is provided having
digital derivation of phase relative to input and trigger channels using on-
board timers and having improved integration using both
analog and digital methods. In embodiments, a data collection and processing
system is provided having digital derivation of phase
relative to input and trigger channels using on-board timers and having
adaptive scheduling techniques for continuous monitoring
of analog data in a local environment. In embodiments, a data collection and
processing system is provided having digital derivation
of phase relative to input and trigger channels using on-board timers and
having data acquisition parking features. In embodiments,
a data collection and processing system is provided having digital derivation
of phase relative to input and trigger channels using
on-board timers and having a self-sufficient data acquisition box. In
embodiments, a data collection and processing system is
provided having digital derivation of phase relative to input and trigger
channels using on-board timers and having SD card storage.
In embodiments, a data collection and processing system is provided having
digital derivation of phase relative to input and trigger
channels using on-board timers and having extended onboard statistical
capabilities for continuous monitoring. In embodiments, a
data collection and processing system is provided having digital derivation of
phase relative to input and trigger channels using on-
CA 03082398 2020-05-11
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board timers and having the use of ambient, local and vibration noise for
prediction. In embodiments, a data collection and
processing system is provided having digital derivation of phase relative to
input and trigger channels using on-board timers and
having smart route changes route based on incoming data or alarms to enable
simultaneous dynamic data for analysis or correlation.
In embodiments, a data collection and processing system is provided having
digital derivation of phase relative to input and trigger
channels using on-board timers and having smart ODS and transfer functions. In
embodiments, a data collection and processing
system is provided having digital derivation of phase relative to input and
trigger channels using on-board timers and having a
hierarchical multiplexer. In embodiments, a data collection and processing
system is provided having digital derivation of phase
relative to input and trigger channels using on-board timers and having
identification of sensor overload. In embodiments, a data
collection and processing system is provided having digital derivation of
phase relative to input and trigger channels using on-board
timers and having RF identification and an inclinometer. In embodiments, a
data collection and processing system is provided
having digital derivation of phase relative to input and trigger channels
using on-board timers and having continuous ultrasonic
monitoring. In embodiments, a data collection and processing system is
provided having digital derivation of phase relative to input
and trigger channels using on-board timers and having cloud-based, machine
pattern recognition based on fusion of remote, analog
industrial sensors. In embodiments, a data collection and processing system is
provided having digital derivation of phase relative
to input and trigger channels using on-board timers and having cloud-based,
machine pattern analysis of state information from
multiple analog industrial sensors to provide anticipated state information
for an industrial system. In embodiments, a data collection
and processing system is provided having digital derivation of phase relative
to input and trigger channels using on-board timers
and having cloud-based policy automation engine for IoT, with creation,
deployment, and management of IoT devices. In
embodiments, a data collection and processing system is provided having
digital derivation of phase relative to input and trigger
channels using on-board timers and having on-device sensor fusion and data
storage for industrial IoT devices. In embodiments, a
data collection and processing system is provided having digital derivation of
phase relative to input and trigger channels using on-
board timers and having a self-organizing data marketplace for industrial IoT
data. In embodiments, a data collection and processing
system is provided having digital derivation of phase relative to input and
trigger channels using on-board timers and having self-
organization of data pools based on utilization and/or yield metrics. In
embodiments, a data collection and processing system is
provided having digital derivation of phase relative to input and trigger
channels using on-board timers and having training Al
models based on industry-specific feedback. In embodiments, a data collection
and processing system is provided having digital
derivation of phase relative to input and trigger channels using on-board
timers and having a self-organized swarm of industrial data
collectors. In embodiments, a data collection and processing system is
provided having digital derivation of phase relative to input
and trigger channels using on-board timers and having an IoT distributed
ledger. In embodiments, a data collection and processing
system is provided having digital derivation of phase relative to input and
trigger channels using on-board timers and having a self-
organizing collector. In embodiments, a data collection and processing system
is provided having digital derivation of phase relative
to input and trigger channels using on-board timers and having a network-
sensitive collector. In embodiments, a data collection and
processing system is provided having digital derivation of phase relative to
input and trigger channels using on-board timers and
having a remotely organized collector. In embodiments, a data collection and
processing system is provided having digital derivation
of phase relative to input and trigger channels using on-board timers and
having a self-organizing storage for a multi-sensor data
collector. In embodiments, a data collection and processing system is provided
having digital derivation of phase relative to input
and trigger channels using on-board timers and having a self-organizing
network coding for multi-sensor data network. In
embodiments, a data collection and processing system is provided having
digital derivation of phase relative to input and trigger
channels using on-board timers and having a wearable haptic user interface for
an industrial sensor data collector, with vibration,
heat, electrical and/or sound outputs. In embodiments, a data collection and
processing system is provided having digital derivation
of phase relative to input and trigger channels using on-board timers and
having heat maps displaying collected data for AR/VR. In
embodiments, a data collection and processing system is provided having
digital derivation of phase relative to input and trigger
channels using on-board timers and having automatically tuned AR/VR
visualization of data collected by a data collector.
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[0265] In embodiments, a data collection and processing system is provided
having a peak-detector for auto-scaling that is routed
into a separate analog-to-digital converter for peak detection. In
embodiments, a data collection and processing system is provided
having a peak-detector for auto-scaling that is routed into a separate analog-
to-digital converter for peak detection and having routing
of a trigger channel that is either raw or buffered into other analog
channels. In embodiments, a data collection and processing
system is provided having a peak-detector for auto-scaling that is routed into
a separate analog-to-digital converter for peak detection
and having the use of higher input oversampling for delta-sigma A/D for lower
sampling rate outputs to minimize AA filter
requirements. In embodiments, a data collection and processing system is
provided having a peak-detector for auto-scaling that is
routed into a separate analog-to-digital converter for peak detection and
having the use of a CPLD as a clock-divider for a delta-
sigma analog-to-digital converter to achieve lower sampling rates without the
need for digital resampling. In embodiments, a data
collection and processing system is provided having a peak-detector for auto-
scaling that is routed into a separate analog-to-digital
converter for peak detection and having long blocks of data at a high-sampling
rate as opposed to multiple sets of data taken at
different sampling rates. In embodiments, a data collection and processing
system is provided having a peak-detector for auto-
scaling that is routed into a separate analog-to-digital converter for peak
detection and having storage of calibration data with
maintenance history on-board card set. In embodiments, a data collection and
processing system is provided having a peak-detector
for auto-scaling that is routed into a separate analog-to-digital converter
for peak detection and having a rapid route creation
capability using hierarchical templates. In embodiments, a data collection and
processing system is provided having a peak-detector
for auto-scaling that is routed into a separate analog-to-digital converter
for peak detection and having intelligent management of
data collection bands. In embodiments, a data collection and processing system
is provided having a peak-detector for auto-scaling
that is routed into a separate analog-to-digital converter for peak detection
and having a neural net expert system using intelligent
management of data collection bands. In embodiments, a data collection and
processing system is provided having a peak-detector
for auto-scaling that is routed into a separate analog-to-digital converter
for peak detection and having use of a database hierarchy
in sensor data analysis. In embodiments, a data collection and processing
system is provided having a peak-detector for auto-scaling
that is routed into a separate analog-to-digital converter for peak detection
and having an expert system GUI graphical approach to
defining intelligent data collection bands and diagnoses for the expert
system. In embodiments, a data collection and processing
system is provided having a peak-detector for auto-scaling that is routed into
a separate analog-to-digital converter for peak detection
and having a graphical approach for back-calculation definition. In
embodiments, a data collection and processing system is
provided having a peak-detector for auto-scaling that is routed into a
separate analog-to-digital converter for peak detection and
having proposed bearing analysis methods. In embodiments, a data collection
and processing system is provided having a peak-
detector for auto-scaling that is routed into a separate analog-to-digital
converter for peak detection and having torsional vibration
detection/analysis utilizing transitory signal analysis. In embodiments, a
data collection and processing system is provided having
a peak-detector for auto-scaling that is routed into a separate analog-to-
digital converter for peak detection and having improved
integration using both analog and digital methods. In embodiments, a data
collection and processing system is provided having a
peak-detector for auto-scaling that is routed into a separate analog-to-
digital converter for peak detection and having adaptive
scheduling techniques for continuous monitoring of analog data in a local
environment. In embodiments, a data collection and
processing system is provided having a peak-detector for auto-scaling that is
routed into a separate analog-to-digital converter for
peak detection and having data acquisition parking features. In embodiments, a
data collection and processing system is provided
having a peak-detector for auto-scaling that is routed into a separate analog-
to-digital converter for peak detection and having a self-
sufficient data acquisition box. In embodiments, a data collection and
processing system is provided having a peak-detector for
auto-scaling that is routed into a separate analog-to-digital converter for
peak detection and having SD card storage. In embodiments,
a data collection and processing system is provided having a peak-detector for
auto-scaling that is routed into a separate analog-to-
digital converter for peak detection and having extended onboard statistical
capabilities for continuous monitoring. In embodiments,
a data collection and processing system is provided having a peak-detector for
auto-scaling that is routed into a separate analog-to-
digital converter for peak detection and having the use of ambient, local and
vibration noise for prediction. In embodiments, a data
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collection and processing system is provided having a peak-detector for auto-
scaling that is routed into a separate analog-to-digital
converter for peak detection and having smart route changes route based on
incoming data or alarms to enable simultaneous dynamic
data for analysis or correlation. In embodiments, a data collection and
processing system is provided having a peak-detector for
auto-scaling that is routed into a separate analog-to-digital converter for
peak detection and having smart ODS and transfer functions.
In embodiments, a data collection and processing system is provided having a
peak-detector for auto-scaling that is routed into a
separate analog-to-digital converter for peak detection and having a
hierarchical multiplexer. In embodiments, a data collection and
processing system is provided having a peak-detector for auto-scaling that is
routed into a separate analog-to-digital converter for
peak detection and having identification of sensor overload. In embodiments, a
data collection and processing system is provided
having a peak-detector for auto-scaling that is routed into a separate analog-
to-digital converter for peak detection and having RF
identification and an inclinometer. In embodiments, a data collection and
processing system is provided having a peak-detector for
auto-scaling that is routed into a separate analog-to-digital converter for
peak detection and having continuous ultrasonic monitoring.
In embodiments, a data collection and processing system is provided having a
peak-detector for auto-scaling that is routed into a
separate analog-to-digital converter for peak detection and having cloud-
based, machine pattern recognition based on fusion of
remote, analog industrial sensors. In embodiments, a data collection and
processing system is provided having a peak-detector for
auto-scaling that is routed into a separate analog-to-digital converter for
peak detection and having cloud-based, machine pattern
analysis of state information from multiple analog industrial sensors to
provide anticipated state information for an industrial system.
In embodiments, a data collection and processing system is provided having a
peak-detector for auto-scaling that is routed into a
separate analog-to-digital converter for peak detection and having cloud-based
policy automation engine for IoT, with creation,
deployment, and management of IoT devices. In embodiments, a data collection
and processing system is provided having a peak-
detector for auto-scaling that is routed into a separate analog-to-digital
converter for peak detection and having on-device sensor
fusion and data storage for industrial IoT devices. In embodiments, a data
collection and processing system is provided having a
peak-detector for auto-scaling that is routed into a separate analog-to-
digital converter for peak detection and having a self-
organizing data marketplace for industrial IoT data. In embodiments, a data
collection and processing system is provided having a
peak-detector for auto-scaling that is routed into a separate analog-to-
digital converter for peak detection and having self-
organization of data pools based on utilization and/or yield metrics. In
embodiments, a data collection and processing system is
provided having a peak-detector for auto-scaling that is routed into a
separate analog-to-digital converter for peak detection and
having training Al models based on industry-specific feedback. In embodiments,
a data collection and processing system is provided
having a peak-detector for auto-scaling that is routed into a separate analog-
to-digital converter for peak detection and having a self-
organized swarm of industrial data collectors. In embodiments, a data
collection and processing system is provided having a peak-
detector for auto-scaling that is routed into a separate analog-to-digital
converter for peak detection and having an IoT distributed
ledger. In embodiments, a data collection and processing system is provided
having a peak-detector for auto-scaling that is routed
into a separate analog-to-digital converter for peak detection and having a
self-organizing collector. In embodiments, a data
collection and processing system is provided having a peak-detector for auto-
scaling that is routed into a separate analog-to-digital
converter for peak detection and having a network-sensitive collector. In
embodiments, a data collection and processing system is
provided having a peak-detector for auto-scaling that is routed into a
separate analog-to-digital converter for peak detection and
having a remotely organized collector. In embodiments, a data collection and
processing system is provided having a peak-detector
for auto-scaling that is routed into a separate analog-to-digital converter
for peak detection and having a self-organizing storage for
a multi-sensor data collector. In embodiments, a data collection and
processing system is provided having a peak-detector for auto-
scaling that is routed into a separate analog-to-digital converter for peak
detection and having a self-organizing network coding for
multi-sensor data network. In embodiments, a data collection and processing
system is provided having a peak-detector for auto-
scaling that is routed into a separate analog-to-digital converter for peak
detection and having a wearable haptic user interface for
an industrial sensor data collector, with vibration, heat, electrical and/or
sound outputs. In embodiments, a data collection and
processing system is provided having a peak-detector for auto-scaling that is
routed into a separate analog-to-digital converter for
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peak detection and having heat maps displaying collected data for AR/VR. In
embodiments, a data collection and processing system
is provided having a peak-detector for auto-scaling that is routed into a
separate analog-to-digital converter for peak detection and
having automatically tuned AR/VR visualization of data collected by a data
collector.
[0266] In embodiments, a data collection and processing system is provided
having the use of a CPLD as a clock-divider for a
delta-sigma analog-to-digital converter to achieve lower sampling rates
without the need for digital resampling. In embodiments, a
data collection and processing system is provided having the use of a CPLD as
a clock-divider for a delta-sigma analog-to-digital
converter to achieve lower sampling rates without the need for digital
resampling and having long blocks of data at a high-sampling
rate as opposed to multiple sets of data taken at different sampling rates. In
embodiments, a data collection and processing system
is provided having the use of a CPLD as a clock-divider for a delta-sigma
analog-to-digital converter to achieve lower sampling
rates without the need for digital resampling and having storage of
calibration data with maintenance history on-board card set. In
embodiments, a data collection and processing system is provided having the
use of a CPLD as a clock-divider for a delta-sigma
analog-to-digital converter to achieve lower sampling rates without the need
for digital resampling and having a rapid route creation
capability using hierarchical templates. In embodiments, a data collection and
processing system is provided having the use of a
CPLD as a clock-divider for a delta-sigma analog-to-digital converter to
achieve lower sampling rates without the need for digital
resampling and having intelligent management of data collection bands. In
embodiments, a data collection and processing system
is provided having the use of a CPLD as a clock-divider for a delta-sigma
analog-to-digital converter to achieve lower sampling
rates without the need for digital resampling and having a neural net expert
system using intelligent management of data collection
bands. In embodiments, a data collection and processing system is provided
having the use of a CPLD as a clock-divider for a delta-
sigma analog-to-digital converter to achieve lower sampling rates without the
need for digital resampling and having use of a
database hierarchy in sensor data analysis. In embodiments, a data collection
and processing system is provided having the use of a
CPLD as a clock-divider for a delta-sigma analog-to-digital converter to
achieve lower sampling rates without the need for digital
resampling and having an expert system GUI graphical approach to defining
intelligent data collection bands and diagnoses for the
expert system. In embodiments, a data collection and processing system is
provided having the use of a CPLD as a clock-divider
for a delta-sigma analog-to-digital converter to achieve lower sampling rates
without the need for digital resampling and having a
graphical approach for back-calculation definition. In embodiments, a data
collection and processing system is provided having the
use of a CPLD as a clock-divider for a delta-sigma analog-to-digital converter
to achieve lower sampling rates without the need for
digital resampling and having proposed bearing analysis methods. In
embodiments, a data collection and processing system is
provided having the use of a CPLD as a clock-divider for a delta-sigma analog-
to-digital converter to achieve lower sampling rates
without the need for digital resampling and having torsional vibration
detection/analysis utilizing transitory signal analysis. In
embodiments, a data collection and processing system is provided having the
use of a CPLD as a clock-divider for a delta-sigma
analog-to-digital converter to achieve lower sampling rates without the need
for digital resampling and having improved integration
using both analog and digital methods. In embodiments, a data collection and
processing system is provided having the use of a
CPLD as a clock-divider for a delta-sigma analog-to-digital converter to
achieve lower sampling rates without the need for digital
resampling and having adaptive scheduling techniques for continuous monitoring
of analog data in a local environment. In
embodiments, a data collection and processing system is provided having the
use of a CPLD as a clock-divider for a delta-sigma
analog-to-digital converter to achieve lower sampling rates without the need
for digital resampling and having data acquisition
parking features. In embodiments, a data collection and processing system is
provided having the use of a CPLD as a clock-divider
for a delta-sigma analog-to-digital converter to achieve lower sampling rates
without the need for digital resampling and having a
self-sufficient data acquisition box. In embodiments, a data collection and
processing system is provided having the use of a CPLD
as a clock-divider for a delta-sigma analog-to-digital converter to achieve
lower sampling rates without the need for digital
resampling and having SD card storage. In embodiments, a data collection and
processing system is provided having the use of a
CPLD as a clock-divider for a delta-sigma analog-to-digital converter to
achieve lower sampling rates without the need for digital
resampling and having extended onboard statistical capabilities for continuous
monitoring. In embodiments, a data collection and
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processing system is provided having the use of a CPLD as a clock-divider for
a delta-sigma analog-to-digital converter to achieve
lower sampling rates without the need for digital resampling and having the
use of ambient, local and vibration noise for prediction.
In embodiments, a data collection and processing system is provided having the
use of a CPLD as a clock-divider for a delta-sigma
analog-to-digital converter to achieve lower sampling rates without the need
for digital resampling and having smart route changes
route based on incoming data or alarms to enable simultaneous dynamic data for
analysis or correlation. In embodiments, a data
collection and processing system is provided having the use of a CPLD as a
clock-divider for a delta-sigma analog-to-digital
converter to achieve lower sampling rates without the need for digital
resampling and having smart ODS and transfer functions. In
embodiments, a data collection and processing system is provided having the
use of a CPLD as a clock-divider for a delta-sigma
analog-to-digital converter to achieve lower sampling rates without the need
for digital resampling and having a hierarchical
multiplexer. In embodiments, a data collection and processing system is
provided having the use of a CPLD as a clock-divider for
a delta-sigma analog-to-digital converter to achieve lower sampling rates
without the need for digital resampling and having
identification of sensor overload. In embodiments, a data collection and
processing system is provided having the use of a CPLD as
a clock-divider for a delta-sigma analog-to-digital converter to achieve lower
sampling rates without the need for digital resampling
and having RF identification and an inclinometer. In embodiments, a data
collection and processing system is provided having the
use of a CPLD as a clock-divider for a delta-sigma analog-to-digital converter
to achieve lower sampling rates without the need for
digital resampling and having continuous ultrasonic monitoring. In
embodiments, a data collection and processing system is
provided having the use of a CPLD as a clock-divider for a delta-sigma analog-
to-digital converter to achieve lower sampling rates
without the need for digital resampling and having cloud-based, machine
pattern recognition based on fusion of remote, analog
industrial sensors. In embodiments, a data collection and processing system is
provided having the use of a CPLD as a clock-divider
for a delta-sigma analog-to-digital converter to achieve lower sampling rates
without the need for digital resampling and having
cloud-based, machine pattern analysis of state information from multiple
analog industrial sensors to provide anticipated state
information for an industrial system. In embodiments, a data collection and
processing system is provided having the use of a CPLD
as a clock-divider for a delta-sigma analog-to-digital converter to achieve
lower sampling rates without the need for digital
resampling and having cloud-based policy automation engine for IoT, with
creation, deployment, and management of IoT devices.
In embodiments, a data collection and processing system is provided having the
use of a CPLD as a clock-divider for a delta-sigma
analog-to-digital converter to achieve lower sampling rates without the need
for digital resampling and having on-device sensor
fusion and data storage for industrial IoT devices. In embodiments, a data
collection and processing system is provided having the
use of a CPLD as a clock-divider for a delta-sigma analog-to-digital converter
to achieve lower sampling rates without the need for
digital resampling and having a self-organizing data marketplace for
industrial IoT data. In embodiments, a data collection and
processing system is provided having the use of a CPLD as a clock-divider for
a delta-sigma analog-to-digital converter to achieve
lower sampling rates without the need for digital resampling and having self-
organization of data pools based on utilization and/or
yield metrics. In embodiments, a data collection and processing system is
provided having the use of a CPLD as a clock-divider for
a delta-sigma analog-to-digital converter to achieve lower sampling rates
without the need for digital resampling and having training
Al models based on industry-specific feedback. In embodiments, a data
collection and processing system is provided having the use
of a CPLD as a clock-divider for a delta-sigma analog-to-digital converter to
achieve lower sampling rates without the need for
digital resampling and having a self-organized swarm of industrial data
collectors. In embodiments, a data collection and processing
system is provided having the use of a CPLD as a clock-divider for a delta-
sigma analog-to-digital converter to achieve lower
sampling rates without the need for digital resampling and having an IoT
distributed ledger. In embodiments, a data collection and
processing system is provided having the use of a CPLD as a clock-divider for
a delta-sigma analog-to-digital converter to achieve
lower sampling rates without the need for digital resampling and having a self-
organizing collector. In embodiments, a data
collection and processing system is provided having the use of a CPLD as a
clock-divider for a delta-sigma analog-to-digital
converter to achieve lower sampling rates without the need for digital
resampling and having a network-sensitive collector. In
embodiments, a data collection and processing system is provided having the
use of a CPLD as a clock-divider for a delta-sigma
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analog-to-digital converter to achieve lower sampling rates without the need
for digital resampling and having a remotely organized
collector. In embodiments, a data collection and processing system is provided
having the use of a CPLD as a clock-divider for a
delta-sigma analog-to-digital converter to achieve lower sampling rates
without the need for digital resampling and having a self-
organizing storage for a multi-sensor data collector. In embodiments, a data
collection and processing system is provided having
the use of a CPLD as a clock-divider for a delta-sigma analog-to-digital
converter to achieve lower sampling rates without the need
for digital resampling and having a self-organizing network coding for multi-
sensor data network. In embodiments, a data collection
and processing system is provided having the use of a CPLD as a clock-divider
for a delta-sigma analog-to-digital converter to
achieve lower sampling rates without the need for digital resampling and
having a wearable haptic user interface for an industrial
sensor data collector, with vibration, heat, electrical and/or sound outputs.
In embodiments, a data collection and processing system
is provided having the use of a CPLD as a clock-divider for a delta-sigma
analog-to-digital converter to achieve lower sampling
rates without the need for digital resampling and having heat maps displaying
collected data for AR/VR. In embodiments, a data
collection and processing system is provided having the use of a CPLD as a
clock-divider for a delta-sigma analog-to-digital
converter to achieve lower sampling rates without the need for digital
resampling and having automatically tuned AR/VR
visualization of data collected by a data collector.
[0267] In embodiments, a data collection and processing system is provided
having storage of calibration data with maintenance
history on-board card set. In embodiments, a data collection and processing
system is provided having storage of calibration data
with maintenance history on-board card set and having a rapid route creation
capability using hierarchical templates. In
embodiments, a data collection and processing system is provided having
storage of calibration data with maintenance history on-
board card set and having intelligent management of data collection bands. In
embodiments, a data collection and processing system
is provided having storage of calibration data with maintenance history on-
board card set and having a neural net expert system
using intelligent management of data collection bands. In embodiments, a data
collection and processing system is provided having
storage of calibration data with maintenance history on-board card set and
having use of a database hierarchy in sensor data analysis.
In embodiments, a data collection and processing system is provided having
storage of calibration data with maintenance history
on-board card set and having an expert system GUI graphical approach to
defining intelligent data collection bands and diagnoses
for the expert system. In embodiments, a data collection and processing system
is provided having storage of calibration data with
maintenance history on-board card set and having a graphical approach for back-
calculation definition. In embodiments, a data
collection and processing system is provided having storage of calibration
data with maintenance history on-board card set and
having proposed bearing analysis methods. In embodiments, a data collection
and processing system is provided having storage of
calibration data with maintenance history on-board card set and having
torsional vibration detection/analysis utilizing transitory
signal analysis. In embodiments, a data collection and processing system is
provided having storage of calibration data with
maintenance history on-board card set and having improved integration using
both analog and digital methods. In embodiments, a
data collection and processing system is provided having storage of
calibration data with maintenance history on-board card set and
having adaptive scheduling techniques for continuous monitoring of analog data
in a local environment. In embodiments, a data
collection and processing system is provided having storage of calibration
data with maintenance history on-board card set and
having data acquisition parking features. In embodiments, a data collection
and processing system is provided having storage of
calibration data with maintenance history on-board card set and having a self-
sufficient data acquisition box. In embodiments, a
data collection and processing system is provided having storage of
calibration data with maintenance history on-board card set and
having SD card storage. In embodiments, a data collection and processing
system is provided having storage of calibration data with
maintenance history on-board card set and having extended onboard statistical
capabilities for continuous monitoring. In
embodiments, a data collection and processing system is provided having
storage of calibration data with maintenance history on-
board card set and having the use of ambient, local and vibration noise for
prediction. In embodiments, a data collection and
processing system is provided having storage of calibration data with
maintenance history on-board card set and having smart route
changes route based on incoming data or alarms to enable simultaneous dynamic
data for analysis or correlation. In embodiments,
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a data collection and processing system is provided having storage of
calibration data with maintenance history on-board card set
and having smart ODS and transfer functions. In embodiments, a data collection
and processing system is provided having storage
of calibration data with maintenance history on-board card set and having a
hierarchical multiplexer. In embodiments, a data
collection and processing system is provided having storage of calibration
data with maintenance history on-board card set and
having identification of sensor overload. In embodiments, a data collection
and processing system is provided having storage of
calibration data with maintenance history on-board card set and having RF
identification and an inclinometer. In embodiments, a
data collection and processing system is provided having storage of
calibration data with maintenance history on-board card set and
having continuous ultrasonic monitoring. In embodiments, a data collection and
processing system is provided having storage of
calibration data with maintenance history on-board card set and having cloud-
based, machine pattern recognition based on fusion
of remote, analog industrial sensors. In embodiments, a data collection and
processing system is provided having storage of
calibration data with maintenance history on-board card set and having cloud-
based, machine pattern analysis of state information
from multiple analog industrial sensors to provide anticipated state
information for an industrial system. In embodiments, a data
collection and processing system is provided having storage of calibration
data with maintenance history on-board card set and
having cloud-based policy automation engine for IoT, with creation,
deployment, and management of IoT devices. In embodiments,
a data collection and processing system is provided having storage of
calibration data with maintenance history on-board card set
and having on-device sensor fusion and data storage for industrial IoT
devices. In embodiments, a data collection and processing
system is provided having storage of calibration data with maintenance history
on-board card set and having a self-organizing data
marketplace for industrial IoT data. In embodiments, a data collection and
processing system is provided having storage of
calibration data with maintenance history on-board card set and having self-
organization of data pools based on utilization and/or
yield metrics. In embodiments, a data collection and processing system is
provided having storage of calibration data with
maintenance history on-board card set and having training Al models based on
industry-specific feedback. In embodiments, a data
collection and processing system is provided having storage of calibration
data with maintenance history on-board card set and
having a self-organized swarm of industrial data collectors. In embodiments, a
data collection and processing system is provided
having storage of calibration data with maintenance history on-board card set
and having an IoT distributed ledger. In embodiments,
a data collection and processing system is provided having storage of
calibration data with maintenance history on-board card set
and having a self-organizing collector. In embodiments, a data collection and
processing system is provided having storage of
calibration data with maintenance history on-board card set and having a
network-sensitive collector. In embodiments, a data
collection and processing system is provided having storage of calibration
data with maintenance history on-board card set and
having a remotely organized collector. In embodiments, a data collection and
processing system is provided having storage of
calibration data with maintenance history on-board card set and having a self-
organizing storage for a multi-sensor data collector.
In embodiments, a data collection and processing system is provided having
storage of calibration data with maintenance history
on-board card set and having a self-organizing network coding for multi-sensor
data network. In embodiments, a data collection
and processing system is provided having storage of calibration data with
maintenance history on-board card set and having a
wearable haptic user interface for an industrial sensor data collector, with
vibration, heat, electrical, and/or sound outputs. In
embodiments, a data collection and processing system is provided having
storage of calibration data with maintenance history on-
board card set and having heat maps displaying collected data for AR/VR. In
embodiments, a data collection and processing system
is provided having storage of calibration data with maintenance history on-
board card set and having automatically tuned AR/VR
visualization of data collected by a data collector.
[0268] In embodiments, a data collection and processing system is provided
having proposed bearing analysis methods. In
embodiments, a data collection and processing system is provided having
proposed bearing analysis methods and having torsional
vibration detection/analysis utilizing transitory signal analysis. In
embodiments, a data collection and processing system is provided
having proposed bearing analysis methods and having improved integration using
both analog and digital methods. In embodiments,
a data collection and processing system is provided having proposed bearing
analysis methods and having adaptive scheduling
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techniques for continuous monitoring of analog data in a local environment. In
embodiments, a data collection and processing
system is provided having proposed bearing analysis methods and having data
acquisition parking features. In embodiments, a data
collection and processing system is provided having proposed bearing analysis
methods and having a self-sufficient data acquisition
box. In embodiments, a data collection and processing system is provided
having proposed bearing analysis methods and having
SD card storage. In embodiments, a data collection and processing system is
provided having proposed bearing analysis methods
and having extended onboard statistical capabilities for continuous
monitoring. In embodiments, a data collection and processing
system is provided having proposed bearing analysis methods and having the use
of ambient, local and vibration noise for prediction.
In embodiments, a data collection and processing system is provided having
proposed bearing analysis methods and having smart
route changes route based on incoming data or alarms to enable simultaneous
dynamic data for analysis or correlation. In
embodiments, a data collection and processing system is provided having
proposed bearing analysis methods and having smart ODS
and transfer functions. In embodiments, a data collection and processing
system is provided having proposed bearing analysis
methods and having a hierarchical multiplexer. In embodiments, a data
collection and processing system is provided having
proposed bearing analysis methods and having identification of sensor
overload. In embodiments, a data collection and processing
system is provided having proposed bearing analysis methods and having RF
identification and an inclinometer. In embodiments,
a data collection and processing system is provided having proposed bearing
analysis methods and having continuous ultrasonic
monitoring. In embodiments, a data collection and processing system is
provided having proposed bearing analysis methods and
having cloud-based, machine pattern recognition based on fusion of remote,
analog industrial sensors. In embodiments, a data
collection and processing system is provided having proposed bearing analysis
methods and having cloud-based, machine pattern
analysis of state information from multiple analog industrial sensors to
provide anticipated state information for an industrial system.
In embodiments, a data collection and processing system is provided having
proposed bearing analysis methods and having cloud-
based policy automation engine for IoT, with creation, deployment, and
management of IoT devices. In embodiments, a data
collection and processing system is provided having proposed bearing analysis
methods and having on-device sensor fusion and
data storage for industrial IoT devices. In embodiments, a data collection and
processing system is provided having proposed bearing
analysis methods and having a self-organizing data marketplace for industrial
IoT data. In embodiments, a data collection and
processing system is provided having proposed bearing analysis methods and
having self-organization of data pools based on
utilization and/or yield metrics. In embodiments, a data collection and
processing system is provided having proposed bearing
analysis methods and having training Al models based on industry-specific
feedback. In embodiments, a data collection and
processing system is provided having proposed bearing analysis methods and
having a self-organized swarm of industrial data
collectors. In embodiments, a data collection and processing system is
provided having proposed bearing analysis methods and
having an IoT distributed ledger. In embodiments, a data collection and
processing system is provided having proposed bearing
analysis methods and having a self-organizing collector. In embodiments, a
data collection and processing system is provided having
proposed bearing analysis methods and having a network-sensitive collector. In
embodiments, a data collection and processing
system is provided having proposed bearing analysis methods and having a
remotely organized collector. In embodiments, a data
collection and processing system is provided having proposed bearing analysis
methods and having a self-organizing storage for a
multi-sensor data collector. In embodiments, a data collection and processing
system is provided having proposed bearing analysis
methods and having a self-organizing network coding for multi-sensor data
network. In embodiments, a data collection and
processing system is provided having proposed bearing analysis methods and
having a wearable haptic user interface for an
industrial sensor data collector, with vibration, heat, electrical and/or
sound outputs. In embodiments, a data collection and
processing system is provided having proposed bearing analysis methods and
having heat maps displaying collected data for AR/VR.
In embodiments, a data collection and processing system is provided having
proposed bearing analysis methods and having
automatically tuned AR/VR visualization of data collected by a data collector.
[0269] In embodiments, a data collection and processing system is provided
having torsional vibration detection/analysis utilizing
transitory signal analysis. In embodiments, a data collection and processing
system is provided having torsional vibration
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detection/analysis utilizing transitory signal analysis and having improved
integration using both analog and digital methods. In
embodiments, a data collection and processing system is provided having
torsional vibration detection/analysis utilizing transitory
signal analysis and having adaptive scheduling techniques for continuous
monitoring of analog data in a local environment. In
embodiments, a data collection and processing system is provided having
torsional vibration detection/analysis utilizing transitory
signal analysis and having data acquisition parking features. In embodiments,
a data collection and processing system is provided
having torsional vibration detection/analysis utilizing transitory signal
analysis and having a self-sufficient data acquisition box. In
embodiments, a data collection and processing system is provided having
torsional vibration detection/analysis utilizing transitory
signal analysis and having SD card storage. In embodiments, a data collection
and processing system is provided having torsional
vibration detection/analysis utilizing transitory signal analysis and having
extended onboard statistical capabilities for continuous
monitoring. In embodiments, a data collection and processing system is
provided having torsional vibration detection/analysis
utilizing transitory signal analysis and having the use of ambient, local and
vibration noise for prediction. In embodiments, a data
collection and processing system is provided having torsional vibration
detection/analysis utilizing transitory signal analysis and
having smart route changes route based on incoming data or alarms to enable
simultaneous dynamic data for analysis or correlation.
In embodiments, a data collection and processing system is provided having
torsional vibration detection/analysis utilizing transitory
signal analysis and having smart ODS and transfer functions. In embodiments, a
data collection and processing system is provided
having torsional vibration detection/analysis utilizing transitory signal
analysis and having a hierarchical multiplexer. In
embodiments, a data collection and processing system is provided having
torsional vibration detection/analysis utilizing transitory
signal analysis and having identification of sensor overload. In embodiments,
a data collection and processing system is provided
having torsional vibration detection/analysis utilizing transitory signal
analysis and having RF identification and an inclinometer.
In embodiments, a data collection and processing system is provided having
torsional vibration detection/analysis utilizing transitory
signal analysis and having continuous ultrasonic monitoring. In embodiments, a
data collection and processing system is provided
having torsional vibration detection/analysis utilizing transitory signal
analysis and having cloud-based, machine pattern recognition
based on fusion of remote, analog industrial sensors. In embodiments, a data
collection and processing system is provided having
torsional vibration detection/analysis utilizing transitory signal analysis
and having cloud-based, machine pattern analysis of state
information from multiple analog industrial sensors to provide anticipated
state information for an industrial system. In
embodiments, a data collection and processing system is provided having
torsional vibration detection/analysis utilizing transitory
signal analysis and having cloud-based policy automation engine for IoT, with
creation, deployment, and management of IoT
devices. In embodiments, a data collection and processing system is provided
having torsional vibration detection/analysis utilizing
transitory signal analysis and having on-device sensor fusion and data storage
for industrial IoT devices. In embodiments, a data
collection and processing system is provided having torsional vibration
detection/analysis utilizing transitory signal analysis and
having a self-organizing data marketplace for industrial IoT data. In
embodiments, a data collection and processing system is
provided having torsional vibration detection/analysis utilizing transitory
signal analysis and having self-organization of data pools
based on utilization and/or yield metrics. In embodiments, a data collection
and processing system is provided having torsional
vibration detection/analysis utilizing transitory signal analysis and having
training Al models based on industry-specific feedback.
In embodiments, a data collection and processing system is provided having
torsional vibration detection/analysis utilizing transitory
signal analysis and having a self-organized swarm of industrial data
collectors. In embodiments, a data collection and processing
system is provided having torsional vibration detection/analysis utilizing
transitory signal analysis and having an IoT distributed
ledger. In embodiments, a data collection and processing system is provided
having torsional vibration detection/analysis utilizing
transitory signal analysis and having a self-organizing collector. In
embodiments, a data collection and processing system is provided
having torsional vibration detection/analysis utilizing transitory signal
analysis and having a network-sensitive collector. In
embodiments, a data collection and processing system is provided having
torsional vibration detection/analysis utilizing transitory
signal analysis and having a remotely organized collector. In embodiments, a
data collection and processing system is provided
having torsional vibration detection/analysis utilizing transitory signal
analysis and having a self-organizing storage for a multi-
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sensor data collector. In embodiments, a data collection and processing system
is provided having torsional vibration
detection/analysis utilizing transitory signal analysis and having a self-
organizing network coding for multi-sensor data network. In
embodiments, a data collection and processing system is provided having
torsional vibration detection/analysis utilizing transitory
signal analysis and having a wearable haptic user interface for an industrial
sensor data collector, with vibration, heat, electrical
and/or sound outputs. In embodiments, a data collection and processing system
is provided having torsional vibration
detection/analysis utilizing transitory signal analysis and having heat maps
displaying collected data for AR/VR. In embodiments,
a data collection and processing system is provided having torsional vibration
detection/analysis utilizing transitory signal analysis
and having automatically tuned AR/VR visualization of data collected by a data
collector.
[0270] In embodiments, a data collection and processing system is provided
having a self-sufficient data acquisition box. In
embodiments, a data collection and processing system is provided having a self-
sufficient data acquisition box and having SD card
storage. In embodiments, a data collection and processing system is provided
having a self-sufficient data acquisition box and having
extended onboard statistical capabilities for continuous monitoring. In
embodiments, a data collection and processing system is
provided having a self-sufficient data acquisition box and having the use of
ambient, local and vibration noise for prediction. In
embodiments, a data collection and processing system is provided having a self-
sufficient data acquisition box and having smart
route changes route based on incoming data or alarms to enable simultaneous
dynamic data for analysis or correlation. In
embodiments, a data collection and processing system is provided having a self-
sufficient data acquisition box and having smart
ODS and transfer functions. In embodiments, a data collection and processing
system is provided having a self-sufficient data
acquisition box and having a hierarchical multiplexer. In embodiments, a data
collection and processing system is provided having
a self-sufficient data acquisition box and having identification of sensor
overload. In embodiments, a data collection and processing
system is provided having a self-sufficient data acquisition box and having RF
identification and an inclinometer. In embodiments,
a data collection and processing system is provided having a self-sufficient
data acquisition box and having continuous ultrasonic
monitoring. In embodiments, a data collection and processing system is
provided having a self-sufficient data acquisition box and
having cloud-based, machine pattern recognition based on fusion of remote,
analog industrial sensors. In embodiments, a data
collection and processing system is provided having a self-sufficient data
acquisition box and having cloud-based, machine pattern
analysis of state information from multiple analog industrial sensors to
provide anticipated state information for an industrial system.
In embodiments, a data collection and processing system is provided having a
self-sufficient data acquisition box and having cloud-
based policy automation engine for IoT, with creation, deployment, and
management of IoT devices. In embodiments, a data
collection and processing system is provided having a self-sufficient data
acquisition box and having on-device sensor fusion and
data storage for industrial IoT devices. In embodiments, a data collection and
processing system is provided having a self-sufficient
data acquisition box and having a self-organizing data marketplace for
industrial IoT data. In embodiments, a data collection and
processing system is provided having a self-sufficient data acquisition box
and having self-organization of data pools based on
utilization and/or yield metrics. In embodiments, a data collection and
processing system is provided having a self-sufficient data
acquisition box and having training Al models based on industry-specific
feedback. In embodiments, a data collection and
processing system is provided having a self-sufficient data acquisition box
and having a self-organized swarm of industrial data
collectors. In embodiments, a data collection and processing system is
provided having a self-sufficient data acquisition box and
having an IoT distributed ledger. In embodiments, a data collection and
processing system is provided having a self-sufficient data
acquisition box and having a self-organizing collector. In embodiments, a data
collection and processing system is provided having
a self-sufficient data acquisition box and having a network-sensitive
collector. In embodiments, a data collection and processing
system is provided having a self-sufficient data acquisition box and having a
remotely organized collector. In embodiments, a data
collection and processing system is provided having a self-sufficient data
acquisition box and having a self-organizing storage for
a multi-sensor data collector. In embodiments, a data collection and
processing system is provided having a self-sufficient data
acquisition box and having a self-organizing network coding for multi-sensor
data network. In embodiments, a data collection and
processing system is provided having a self-sufficient data acquisition box
and having a wearable haptic user interface for an
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industrial sensor data collector, with vibration, heat, electrical, and/or
sound outputs. In embodiments, a data collection and
processing system is provided having a self-sufficient data acquisition box
and having heat maps displaying collected data for
AR/VR. In embodiments, a data collection and processing system is provided
having a self-sufficient data acquisition box and
having automatically tuned AR/VR visualization of data collected by a data
collector.
[0271] In embodiments, a platform is provided having a self-organizing
collector. In embodiments, a platform is provided having
a self-organizing collector and having a network-sensitive collector. In
embodiments, a platform is provided having a self-organizing
collector and having a remotely organized collector. In embodiments, a
platform is provided having a self-organizing collector and
having a self-organizing storage for a multi-sensor data collector. In
embodiments, a platform is provided having a self-organizing
collector and having a self-organizing network coding for multi-sensor data
network. In embodiments, a platform is provided having
a self-organizing collector and having a wearable haptic user interface for an
industrial sensor data collector, with vibration, heat,
electrical and/or sound outputs. In embodiments, a platform is provided having
a self-organizing collector and having heat maps
displaying collected data for AR/VR. In embodiments, a platform is provided
having a self-organizing collector and having
automatically tuned AR/VR visualization of data collected by a data collector.
[0272] While only a few embodiments of the present disclosure have been shown
and described, it will be obvious to those skilled
in the art that many changes and modifications may be made thereunto without
departing from the spirit and scope of the present
disclosure as described in the following claims. All patent applications and
patents, both foreign and domestic, and all other
publications referenced herein are incorporated herein in their entireties to
the full extent permitted by law.
[0273] The methods and systems described herein may be deployed in part or in
whole through a machine that executes computer
software, program codes, and/or instructions on a processor. The present
disclosure may be implemented as a method on the
machine, as a system or apparatus as part of or in relation to the machine, or
as a computer program product embodied in a computer
readable medium executing on one or more of the machines. In embodiments, the
processor may be part of a server, cloud server,
client, network infrastructure, mobile computing platform, stationary
computing platform, or other computing platform. A processor
may be any kind of computational or processing device capable of executing
program instructions, codes, binary instructions, and
the like. The processor may be or may include a signal processor, digital
processor, embedded processor, microprocessor, or any
variant such as a co-processor (math co-processor, graphic co-processor,
communication co-processor, and the like) and the like
that may directly or indirectly facilitate execution of program code or
program instructions stored thereon. In addition, the processor
may enable execution of multiple programs, threads, and codes. The threads may
be executed simultaneously to enhance the
performance of the processor and to facilitate simultaneous operations of the
application. By way of implementation, methods,
program codes, program instructions and the like described herein may be
implemented in one or more thread. The thread may
spawn other threads that may have assigned priorities associated with them;
the processor may execute these threads based on
priority or any other order based on instructions provided in the program
code. The processor, or any machine utilizing one, may
include non-transitory memory that stores methods, codes, instructions, and
programs as described herein and elsewhere. The
processor may access a non-transitory storage medium through an interface that
may store methods, codes, and instructions as
described herein and elsewhere. The storage medium associated with the
processor for storing methods, programs, codes, program
instructions or other type of instructions capable of being executed by the
computing or processing device may include but may not
be limited to one or more of a CD-ROM, DVD, memory, hard disk, flash drive,
RAM, ROM, cache, and the like.
[0274] A processor may include one or more cores that may enhance speed and
performance of a multiprocessor. In embodiments,
the process may be a dual core processor, quad core processors, other chip-
level multiprocessor and the like that combine two or
more independent cores (called a die).
[0275] The methods and systems described herein may be deployed in part or in
whole through a machine that executes computer
software on a server, client, firewall, gateway, hub, router, or other such
computer and/or networking hardware. The software
program may be associated with a server that may include a file server, print
server, domain server, internet server, intranet server,
cloud server, and other variants such as secondary server, host server,
distributed server, and the like. The server may include one
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or more of memories, processors, computer readable transitory and/or non-
transitory media, storage media, ports (physical and
virtual), communication devices, and interfaces capable of accessing other
servers, clients, machines, and devices through a wired
or a wireless medium, and the like. The methods, programs, or codes as
described herein and elsewhere may be executed by the
server. In addition, other devices required for execution of methods as
described in this application may be considered as a part of
the infrastructure associated with the server.
[0276] The server may provide an interface to other devices including, without
limitation, clients, other servers, printers, database
servers, print servers, file servers, communication servers, distributed
servers, social networks, and the like. Additionally, this
coupling and/or connection may facilitate remote execution of program across
the network. The networking of some or all of these
devices may facilitate parallel processing of a program or method at one or
more location without deviating from the scope of the
disclosure. In addition, any of the devices attached to the server through an
interface may include at least one storage medium
capable of storing methods, programs, code and/or instructions. A central
repository may provide program instructions to be
executed on different devices. In this implementation, the remote repository
may act as a storage medium for program code,
instructions, and programs.
[0277] The software program may be associated with a client that may include a
file client, print client, domain client, internet
client, intranet client and other variants such as secondary client, host
client, distributed client, and the like. The client may include
one or more of memories, processors, computer readable transitory and/or non-
transitory media, storage media, ports (physical and
virtual), communication devices, and interfaces capable of accessing other
clients, servers, machines, and devices through a wired
or a wireless medium, and the like. The methods, programs, or codes as
described herein and elsewhere may be executed by the
client. In addition, other devices required for execution of methods as
described in this application may be considered as a part of
the infrastructure associated with the client.
[0278] The client may provide an interface to other devices including, without
limitation, servers, other clients, printers, database
servers, print servers, file servers, communication servers, distributed
servers, and the like. Additionally, this coupling and/or
connection may facilitate remote execution of program across the network. The
networking of some or all of these devices may
facilitate parallel processing of a program or method at one or more location
without deviating from the scope of the disclosure. In
addition, any of the devices attached to the client through an interface may
include at least one storage medium capable of storing
methods, programs, applications, code and/or instructions. A central
repository may provide program instructions to be executed on
different devices. In this implementation, the remote repository may act as a
storage medium for program code, instructions, and
programs.
[0279] Methods and systems described herein for industrial machine sensor data
streaming, collection, processing, and storage
may be configured to operate with existing data collection, processing and
storage systems while preserving access to existing
format/frequency range/resolution compatible data. While the industrial
machine sensor data streaming facilities described herein
may collect a greater volume of data (e.g., longer duration of data
collection) from sensors at a wider range of frequencies and with
greater resolution than existing data collection systems, methods and systems
may be employed to provide access to data from the
stream of data that represents one or more ranges of frequency and/or one or
more lines of resolution that are purposely compatible
with existing systems. Further, a portion of the streamed data may be
identified, extracted, stored, and/or forwarded to existing data
processing systems to facilitate operation of existing data processing systems
that substantively matches operation of existing data
processing systems using existing collection-based data. In this way, a newly
deployed system for sensing aspects of industrial
machines, such as aspects of moving parts of industrial machines, may
facilitate continued use of existing sensed data processing
facilities, algorithms, models, pattern recognizers, user interfaces and the
like.
[0280] Through identification of existing frequency ranges, formats, and/or
resolution, such as by accessing a data structure that
defines these aspects of existing data, higher resolution streamed data may be
configured to represent a specific frequency, frequency
range, format, and/or resolution. This configured streamed data can be stored
in a data structure that is compatible with existing
sensed data structures so that existing processing systems and facilities can
access and process the data substantially as if it were
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the existing data. One approach to adapting streamed data for compatibility
with existing sensed data may include aligning the
streamed data with existing data so that portions of the streamed data that
align with the existing data can be extracted, stored, and
made available for processing with existing data processing methods.
Alternatively, data processing methods may be configured to
process portions of the streamed data that correspond, such as through
alignment, to the existing data with methods that implement
functions substantially similar to the methods used to process existing data,
such as methods that process data that contain a particular
frequency range or a particular resolution and the like.
[0281] Methods used to process existing data may be associated with certain
characteristics of sensed data, such as certain
frequency ranges, sources of data, and the like. As an example, methods for
processing bearing sensing information for a moving
part of an industrial machine may be capable of processing data from bearing
sensors that fall into a particular frequency range.
This method can thusly be at least partially identifiable by these
characteristics of the data being processed. Therefore, given a set
of conditions, such as moving device being sensed, industrial machine type,
frequency of data being sensed, and the like, a data
processing system may select an appropriate method. Also, given such as set of
conditions, an industrial machine data sensing and
processing facility may configure elements, such as data filters, routers,
processors, and the like to handle data meeting the
conditions.
[0282] With regard to Figure 18, a range of existing data sensing and
processing systems with an industrial sensing processing
and storage systems 4500 include a streaming data collector 4510 that may be
configured to accept data in a range of formats as
described herein. In embodiments, the range of formats can include a data
format A 4520, a data format B 4522, a data format C
4524, and a data format D 4528 that may be sourced from a range of sensors.
Moreover, the range of sensors can include an
instrument A 4540, an instrument B 4542, an instrument C 4544, and an
instrument D 4548. The streaming data collector 4510 may
be configured with processing capabilities that enable access to the
individual formats while leveraging the streaming, routing, self-
organizing storage, and other capabilities described herein.
[0283] Figure 19 depicts methods and systems 4600 for industrial machine
sensor data streaming collection, processing, and
storage that facilitate use a streaming data collector 4610 to collect and
obtain data from legacy instruments 4620 and streaming
instruments 4622. Legacy instruments 4620 and their data methodologies may
capture and provide data that is limited in scope due
to the legacy systems and acquisition procedures, such as existing data
described above herein, to a particular range of frequencies
and the like. The streaming data collector 4610 may be configured to capture
streaming instrument data 4632 as well as legacy
instrument data 4630. The streaming data collector 4610 may also be configured
to capture current streaming instruments 4622 and
legacy instruments 4620
[0284] and sensors using current and legacy data methodologies. These
embodiments may be useful in transition applications
from the legacy instruments and processing to the streaming instruments and
processing. In embodiments, the streaming data
collector 4610 may be configured to process the legacy instrument data 4630 so
that it can be stored compatibly with the streamed
instrument data 4642. The streaming data collector 4610 may process or parse
the streamed instrument data 4642 based on the
legacy instrument data 4640 to produce at least one extraction of the streamed
data 4654 that is compatible with the legacy instrument
data 4630 that can be processed to translated legacy data 4652. In
embodiments, extracted data 4650 that can include extracted
portions of translated legacy data 4652 and extracted streamed data 4654 may
be stored in a format that facilitates access and
processing by legacy instrument data processing and further processing that
can emulate legacy instrument data processing methods,
and the like. In embodiments, the portions of the translated legacy data 4652
may also be stored in a format that facilitates processing
with different methods that can take advantage of the greater frequencies,
resolution, and volume of data possible with a streaming
instrument.
[0285] Figure 20 depicts alternate embodiments descriptive of methods and
systems 4700 for industrial machine sensor data
streaming, collection, processing, and storage that facilitate integration of
legacy instruments and processing. In embodiments, a
streaming data collector 4710 may be connected with an industrial machine 4712
and may include a plurality of sensors, such as
streaming sensors 4720 and 4722 that may be configured to sense aspects of the
industrial machine 4712 associated with at least
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one moving part of the industrial machine 4712. The streaming sensors 4720 and
4722 (or more) may communicate with one or
more streaming devices 4740 that may facilitate streaming data from one or
more of the sensors to the streaming data collector
4710. In embodiments, the industrial machine 4712 may also interface with or
include one or more legacy instruments 4730 that
may capture data associated with one or more moving parts of the industrial
machine 4712 and store that data into a legacy data
storage facility 4732.
[0286] In embodiments, a frequency and/or resolution detection facility 4742
may be configured to facilitate detecting information
about legacy instrument sourced data, such as a frequency range of the data or
a resolution of the data, and the like. The frequency
and/or resolution detection detection facility 4742 may operate on data
directly from the legacy instruments 4730 or from data stored
in a legacy data storage facility 4732. The frequency and/or resolution
detection detection facility 4742 may communicate
information that it has detected about the legacy instruments 4730, its
sourced data, and its legacy data stored in a legacy data storage
facility 4732, or the like to the streaming data collector 4710.
Alternatively, the frequency and/or resolution detection detection
facility 4742 may access information, such as information about frequency
ranges, resolution and the like that characterizes the
sourced data from the legacy instrument 4730 and/or may be accessed from a
portion of the legacy storage facility 4732.
[0287] In embodiments, the streaming data collector 4710 may be configured
with one or more automatic processors, algorithms,
and/or other data methodologies to match up information captured by the one or
more legacy instruments 4730 with a portion of
data being provided by the one or more streaming devices 4740 from the one or
more industrial machines 4712. Data from streaming
devices 4740 may include a wider range of frequencies and resolutions than the
sourced data of legacy instruments 4730 and,
therefore, filtering and other such functions can be implemented to extract
data from the streaming devices 4740 that corresponds
to the sourced data of the legacy instruments 4730 in aspects such as
frequency range, resolution, and the like. In embodiments, the
configured streaming data collector 4710 may produce a plurality of streams of
data, including a stream of data that may correspond
to the stream of data from the streaming device 4740 and a separate stream of
data that is compatible, in some aspects, with the
legacy instrument sourced data and the infrastructure to ingest and
automatically process it. Alternatively, the streaming data
collector 4710 may output data in modes other than as a stream, such as
batches, aggregations, summaries, and the like.
[0288] Configured streaming data collector 4710 may communicate with a stream
storage facility 4764 for storing at least one of
the data output from the streaming data collector 4710 and data extracted
therefrom that may be compatible, in some aspects, with
the sourced data of the legacy instruments 4730. A legacy compatible output of
the configured streaming data collector 4710 may
also be provided to a format adaptor facility 4748, 4760 that may configure,
adapt, reformat and other adjustments to the legacy
compatible data so that it can be stored in a legacy compatible storage
facility 4762 so that legacy processing facilities 4744 may
execute data processing methods on data in the legacy compatible storage
facility 4762 and the like that are configured to process
the sourced data of the legacy instruments 4730. In embodiments in which
legacy compatible data is stored in the stream storage
facility 4764, legacy processing facility 4744 may also automatically process
this data after optionally being processed by format
adaptor 4760. By arranging the data collection, streaming, processing,
formatting, and storage elements to provide data in a format
that is fully compatible with legacy instrument sourced data, transition from
a legacy system can be simplified and the sourced data
from legacy instruments can be easily compared to newly acquired data (with
more content) without losing the legacy value of the
sourced data from the legacy instruments 4730.
[0289] Figure 21 depicts alternate embodiments of the methods and systems 4800
described herein for industrial machine sensor
data streaming, collection, processing, and storage that may be compatible
with legacy instrument data collection and processing.
In embodiments, processing industrial machine sensed data may be accomplished
in a variety of ways including aligning legacy and
streaming sources of data, such as by aligning stored legacy and streaming
data; aligning stored legacy data with a stream of sensed
data; and aligning legacy and streamed data as it is being collected. In
embodiments, an industrial machine 4810 may include,
communicate with, or be integrated with one or more stream data sensors 4820
that may sense aspects of the industrial machine
4810 such as aspects of one or more moving parts of the machine. The
industrial machine 4810 may also communicate with, include,
or be integrated with one or more legacy data sensors 4830 that may sense
similar aspects of the industrial machine 4810. In
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embodiments, the one or more legacy data sensors 4830 may provide sensed data
to one or more legacy data collectors 4840. The
stream data sensors 4820 may produce an output that encompasses all aspects of
(i.e., a richer signal) and is compatible with sensed
data from the legacy data sensors 4830. The stream data sensors 4820 may
provide compatible data to the legacy data collector
4840. By mimicking the legacy data sensors 4830 or their data streams, the
stream data sensors 4820 may replace (or serve as
suitable duplicate for) one or more legacy data sensors, such as during an
upgrade of the sensing and processing system of an
industrial machine. Frequency range, resolution and the like may be mimicked
by the stream data so as to ensure that all forms of
legacy data are captured or can be derived from the stream data. In
embodiments, format conversion, if needed, can also be
performed by the stream data sensors 4820. The stream data sensors 4820 may
also produce an alternate data stream that is suitable
for collection by the stream data collector 4850. In embodiments, such an
alternate data stream may be a superset of the legacy data
sensor data in at least one or more of frequency range, resolution, duration
of sensing the data, and the like.
[0290] In embodiments, an industrial machine sensed data processing facility
4860 may execute a wide range of sensed data
processing methods, some of which may be compatible with the data from legacy
data sensors 4830 and may produce outputs that
may meet legacy sensed data processing requirements. To facilitate use of a
wide range of data processing capabilities of processing
facility 4860, legacy and stream data may need to be aligned so that a
compatible portion of stream data may be extracted for
processing with legacy compatible methods and the like. In embodiments, Figure
21 depicts three different techniques for aligning
stream data to legacy data. A first alignment methodology 4862 includes
aligning legacy data output by the legacy data collector
4840 with stream data output by the stream data collector 4850. As data is
provided by the legacy data collector 4840, aspects of
the data may be detected, such as resolution, frequency, duration, and the
like, and may be used as control for a processing method
that identifies portions of a stream of data from the stream data collector
4850 that are purposely compatible with the legacy data.
The processing facility 4860 may apply one or more legacy compatible methods
on the identified portions of the stream data to
extract data that can be easily compared to or referenced against the legacy
data.
[0291] In embodiments, a second alignment methodology 4864 may involve
aligning streaming data with data from a legacy
storage facility 4882. In embodiments, a third alignment methodology 4868 may
involve aligning stored stream data from a stream
storage facility 4884 with legacy data from the legacy data storage facility
4882. In each of the methodologies 4862, 4864, 4868,
alignment data may be determined by processing the legacy data to detect
aspects such as resolution, duration, frequency range and
the like. Alternatively, alignment may be performed by an alignment facility,
such as facilities using methodologies 4862, 4864,
4868 that may receive or may be configured with legacy data descriptive
information such as legacy frequency range, duration,
resolution, and the like.
[0292] In embodiments, an industrial machine sensing data processing facility
4860 may have access to legacy compatible
methods and algorithms that may be stored in a legacy data methodology and
algorithm storage facility 4880. These methodologies,
algorithms, or other data in the legacy methodology and algorithm storage
facility 4880 may also be a source of alignment
information that could be communicated by the industrial machine sensed data
processing facility 4860 to the various alignment
facilities having methodologies 4862, 4864, 4868. By having access to legacy
compatible algorithms and methodologies, the data
processing facility 4860 may facilitate processing legacy data, streamed data
that is compatible with legacy data, or portions of
streamed data that represent the legacy data to produce legacy compatible
analytics 4894.
[0293] In embodiments, the data processing facility 4860 may execute a wide
range of other sensed data processing methods, such
as wavelet derivations and the like to produce streamed processed analytics
4892. In embodiments, the streaming data collector 102,
4510, 4610, 4710 (Figures 3, 6, 18, 19, 20) or data processing facility 4860
may include portable algorithms, methodologies and
inputs that may be defined and extracted from data streams. In many examples,
a user or enterprise may already have existing and
effective methods related to analyzing specific pieces of machinery and
assets. These existing methods could be imported into the
configured streaming data collector 102, 4510, 4610, 4710 or the data
processing facility 4860 as portable algorithms or
methodologies. Data processing, such as described herein for the configured
streaming data collector 102, 4510, 4610, 4710 may
also match an algorithm or methodology to a situation, then extract data from
a stream to match to the data methodology from the
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legacy acquisition or legacy acquisition techniques. In embodiments, the
streaming data collector 102, 4510, 4610, 4710 may be
compatible with many types of systems and may be compatible with systems
having varying degrees of criticality.
[0294] Exemplary industrial machine deployments of the methods and systems
described herein are now described. An industrial
machine may be a gas compressor. In an example, a gas compressor may operate
an oil pump on a very large turbo machine, such
as a very large turbo machine that includes 10,000 HP motors. The oil pump may
be a highly critical system as its failure could
cause an entire plant to shut down. The gas compressor in this example may run
four stages at a very high frequency, such as 36,000
RPM and may include tilt pad bearings that ride on an oil film. The oil pump
in this example may have roller bearings, that if an
anticipated failure is not being picked up by a user, the oil pump may stop
running and the entire turbo machine would fail.
Continuing with this example, the streaming data collector 102, 4510, 4610,
4710 may collect data related to vibrations, such as
casing vibration and proximity probe vibration. Other bearing industrial
machine examples may include generators, power plants,
boiler feed pumps, fans, forced draft fans, induced draft fans and the like.
The streaming data collector 102, 4510, 4610, 4710 for a
bearings system used in the industrial gas industry may support predictive
analysis on the motors, such as that performed by model-
based expert systems, for example, using voltage, current and vibration as
analysis metrics.
[0295] Another exemplary industrial machine deployment may be a motor and the
streaming data collector 102, 4510, 4610, 4710
that may assist in the analysis of a motor by collecting voltage and current
data on the motor, for example.
[0296] Yet another exemplary industrial machine deployment may include oil
quality sensing. An industrial machine may conduct
oil analysis and the streaming data collector 102, 4510, 4610, 4710 may assist
in searching for fragments of metal in oil, for example.
[0297] The methods and systems described herein may also be used in
combination with model-based systems. Model-based
systems may integrate with proximity probes. Proximity probes may be used to
sense problems with machinery and shut machinery
down due to sensed problems. A model-based system integrated with proximity
probes may measure a peak waveform and send a
signal that shuts down machinery based on the peak waveform measurement.
[0298] Enterprises that operate industrial machines may operate in many
diverse industries. These industries may include
industries that operate manufacturing lines, provide computing infrastructure,
support financial services, provide HVAC equipment
and the like. These industries may be highly sensitive to lost operating time
and the cost incurred due to lost operating time. HVAC
equipment enterprises in particular may be concerned with data related to
ultrasound, vibration, IR and the like and may get much
more information about machine performance related to these metrics using the
methods and systems of industrial machine sensed
data streaming collection than from legacy systems.
[0299] Methods and systems described herein for industrial machine sensor data
streaming, collection, processing, and storage
may be configured to operate and integrate with existing data collection,
processing and storage systems and may include a method
for capturing a plurality of streams of sensed data from sensors deployed to
monitor aspects of an industrial machine associated
with at least one moving part of the machine; at least one of the streams
containing a plurality of frequencies of data. The method
may include identifying a subset of data in at least one of the plurality of
streams that corresponds to data representing at least one
predefined frequency. The at least one predefined frequency is represented by
a set of data collected from alternate sensors deployed
to monitor aspects of the industrial machine associated with the at least one
moving part of the machine. The method may further
include processing the identified data with a data processing facility that
processes the identified data with data methodologies
configured to be applied to the set of data collected from alternate sensors.
Lastly the method may include storing the at least one
of the streams of data, the identified subset of data, and a result of
processing the identified data in an electronic data set.
[0300] Methods and systems described herein for industrial machine sensor data
streaming, collection, processing, and storage
may be configured to operate and integrate with existing data collection,
processing and storage systems and may include a method
for applying data captured from sensors deployed to monitor aspects of an
industrial machine associated with at least one moving
part of the machine, the data captured with predefined lines of resolution
covering a predefined frequency range to a frequency
matching facility that identifies a subset of data streamed from other sensors
deployed to monitor aspects of the industrial machine
associated with at least one moving part of the machine, the streamed data
comprising a plurality of lines of resolution and frequency
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ranges, the subset of data identified corresponding to the lines of resolution
and predefined frequency range. This method may
include storing the subset of data in an electronic data record in a format
that corresponds to a format of the data captured with
predefined lines of resolution; and signaling to a data processing facility
the presence of the stored subset of data. This method may
optionally include processing the subset of data with at least one of
algorithms, methodologies, models, and pattern recognizers that
corresponds to algorithms, methodologies, models, and pattern recognizers
associated with processing the data captured with
predefined lines of resolution covering a predefined frequency range.
[0301] Methods and systems described herein for industrial machine sensor data
streaming, collection, processing, and storage
may be configured to operate and integrate with existing data collection,
processing and storage systems and may include a method
for identifying a subset of streamed sensor data. The sensor data is captured
from sensors deployed to monitor aspects of an industrial
machine associated with at least one moving part of the machine. The subset of
streamed sensor data is at predefined lines of
resolution for a predefined frequency range. The method includes establishing
a first logical route for communicating electronically
between a first computing facility performing the identifying and a second
computing facility. The identified subset of the streamed
sensor data is communicated exclusively over the established first logical
route when communicating the subset of streamed sensor
data from the first facility to the second facility. This method may further
include establishing a second logical route for
communicating electronically between the first computing facility and the
second computing facility for at least one portion of the
streamed sensor data that is not the identified subset. This method may
further include establishing a third logical route for
communicating electronically between the first computing facility and the
second computing facility for at least one portion of the
streamed sensor data that includes the identified subset and at least one
other portion of the data not represented by the identified
subset.
[0302] Methods and systems described herein for industrial machine sensor data
streaming, collection, processing, and storage
may be configured to operate and integrate with existing data collection,
processing and storage systems and may include a first
data sensing and processing system that captures first data from a first set
of sensors deployed to monitor aspects of an industrial
machine associated with at least one moving part of the machine, the first
data covering a set of lines of resolution and a frequency
range. This system may include a second data sensing and processing system
that captures and streams a second set of data from a
second set of sensors deployed to monitor aspects of the industrial machine
associated with at least one moving part of the machine,
the second data covering a plurality of lines of resolution that includes the
set of lines of resolution and a plurality of frequencies
that includes the frequency range. The system may enable (1) selecting a
portion of the second data that corresponds to the set of
lines of resolution and the frequency range of the first data; and (2)
processing the selected portion of the second data with the first
data sensing and processing system.
[0303] Methods and systems described herein for industrial machine sensor data
streaming, collection, processing, and storage
may be configured to operate and integrate with existing data collection,
processing and storage systems and may include a method
for automatically processing a portion of a stream of sensed data. The sensed
data received from a first set of sensors is deployed to
monitor aspects of an industrial machine associated with at least one moving
part of the machine in response to an electronic data
structure that facilitates extracting a subset of the stream of sensed data
that corresponds to a set of sensed data received from a
second set of sensors deployed to monitor the aspects of the industrial
machine associated with the at least one moving part of the
machine. The set of sensed data is constrained to a frequency range. The
stream of sensed data includes a range of frequencies that
exceeds the frequency range of the set of sensed data. The processing
comprising executing data methodologies on a portion of the
stream of sensed data that is constrained to the frequency range of the set of
sensed data. The data methodologies are configured to
process the set of sensed data.
[0304] Methods and systems described herein for industrial machine sensor data
streaming, collection, processing, and storage
may be configured to operate and integrate with existing data collection,
processing and storage systems and may include a method
for receiving first data from sensors deployed to monitor aspects of an
industrial machine associated with at least one moving part
of the machine. This method may further include: (1) detecting at least one of
a frequency range and lines of resolution represented
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by the first data; and (2) receiving a stream of data from sensors deployed to
monitor the aspects of the industrial machine associated
with the at least one moving part of the machine. The stream of data includes
a plurality of frequency ranges and a plurality of lines
of resolution that exceeds the frequency range and the lines of resolution
represented by the first data; extracting a set of data from
the stream of data that corresponds to at least one of the frequency range and
the lines of resolution represented by the first data;
and processing the extracted set of data with a data processing method that is
configured to process data within the frequency range
and within the lines of resolution of the first data.
[0305] The methods and systems disclosed herein may include, connect to, or be
integrated with a data acquisition instrument and
in the many embodiments, Figure 22 shows methods and systems 5000 that
includes a data acquisition (DAQ) streaming instrument
5002 also known as an SDAQ. In embodiments, output from sensors 82 may be of
various types including vibration, temperature,
pressure, ultrasound and so on. In my many examples, one of the sensors may be
used. In further examples, many of the sensors
may be used and their signals may be used individually or in predetermined
combinations and/or at predetermined intervals,
circumstances, setups, and the like.
[0306] In embodiments, the output signals from the sensors 82 may be fed into
instrument inputs 5020, 5022, 5024 of the DAQ
instrument 5002 and may be configured with additional streaming capabilities
5028. By way of these many examples, the output
signals from the sensors 82, or more as applicable, may be conditioned as an
analog signal before digitization with respect to at least
scaling and filtering. The signals may then be digitized by an analog to
digital converter 5030. The signals received from all
relevant channels (i.e., one or more channels are switched on manually, by
alarm, by route, and the like) may be simultaneously
sampled at a predetermined rate sufficient to perform the maximum desired
frequency analysis that may be adjusted and readjusted
as needed or otherwise held constant to ensure compatibility or conformance
with other relevant datasets. In embodiments, the
signals are sampled for a relatively long time and gap-free as one continuous
stream so as to enable further post-processing at lower
sampling rates with sufficient individual sampling.
[0307] In embodiments, data may be streamed from a collection of points and
then the next set of data may be collected from
additional points according to a prescribed sequence, route, path, or the
like. In many examples, the sensors 82 or more may be
moved to the next location according to the prescribed sequence, route, pre-
arranged configurations, or the like. In certain examples,
not all of the sensor 82 may move and therefore some may remain fixed in place
and used for detection of reference phase or the
like.
[0308] In embodiments, a multiplex (mux) 5032 may be used to switch to the
next collection of points, to a mixture of the two
methods or collection patterns that may be combined, other predetermined
routes, and the like. The multiplexer 5032 may be
stackable so as to be laddered and effectively accept more channels than the
DAQ instrument 5002 provides. In examples, the DAQ
instrument 5002 may provide eight channels while the multiplexer 5032 may be
stacked to supply 32 channels. Further variations
are possible with one more multiplexers. In embodiments, the multiplexer 5032
may be fed into the DAQ instrument 5002 through
an instrument input 5034. In embodiments, the DAQ instrument 5002 may include
a controller 5038 that may take the form of an
onboard controller, a PC, other connected devices, network based services, and
combinations thereof.
[0309] In embodiments, the sequence and panel conditions used to govern the
data collection process may be obtained from the
multimedia probe (MMP) and probe control, sequence and analytical (PCSA)
information store 5040. In embodiments, the PCSA
information store 5040 may be onboard the DAQ instrument 5002. In embodiments,
contents of the PCSA information store 5040
may be obtained through a cloud network facility, from other DAQ instruments,
from other connected devices, from the machine
being sensed, other relevant sources, and combinations thereof. In
embodiments, the PCSA information store 5040 may include
such items as the hierarchical structural relationships of the machine, e.g.,
a machine contains predetermined pieces of equipment,
each of which may contain one or more shafts and each of those shafts may have
multiple associated bearings. Each of those types
of bearings may be monitored by specific types of transducers or probes,
according to one or more specific prescribed sequences
(paths, routes, and the like) and with one or more specific panel conditions
that may be set on the one or more DAQ instruments
5002. By way of this example, the panel conditions may include hardware
specific switch settings or other collection parameters.
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In many examples, collection parameters include but are not limited to a
sampling rate, AC/DC coupling, voltage range and gain,
integration, high and low pass filtering, anti-aliasing filtering, ICPTM
transducers and other integrated-circuit piezoelectric
transducers, 4-20 mA loop sensors, and the like. In embodiments, the PCSA
information store 5040 may also include machinery
specific features that may be important for proper analysis such as gear teeth
for a gear, number blades in a pump impeller, number
of motor rotor bars, bearing specific parameters necessary for calculating
bearing frequencies, revolution per minutes information
of all rotating elements and multiples of those RPM ranges, and the like.
Information in the information store may also be used to
extract streamed data 5050 for permanent storage.
[0310] Based on directions from the DAQ API software 5052, digitized waveforms
may be uploaded using DAQ driver services
5054 of a driver onboard the DAQ instrument 5002. In embodiments, data may
then be fed into a raw data server 5058 which may
store the stream data 5050 in a stream data repository 5060. In embodiments,
this data storage area is typically meant for storage
until the data is copied off of the DAQ instrument 5002 and verified. The DAQ
API 5052 may also direct the local data control
application 5062 to extract and process the recently obtained stream data 5050
and convert it to the same or lower sampling rates of
sufficient length to effect one or more desired resolutions. By way of these
examples, this data may be converted to spectra,
averaged, and processed in a variety of ways and stored, at least temporarily,
as extracted/processed (EP) data 5064. It will be
appreciated in light of the disclosure that legacy data may require its own
sampling rates and resolution to ensure compatibility and
often this sampling rate may not be integer proportional to the acquired
sampling rate. It will also be appreciated in light of the
disclosure that this may be especially relevant for order-sampled data whose
sampling frequency is related directly to an external
frequency (typically the running speed of the machine or its local
componentry) rather than the more-standard sampling rates
employed by the internal crystals, clock functions, or the like of the DAQ
instrument (e.g., values of Fmax of 100, 200, 500, 1K,
2K, 5K, 10K, 20K, and so on).
[0311] In embodiments, the extract/process (EP) align module 5068 of the local
data control application 5062 may be able to
fractionally adjust the sampling rates to these non-integer ratio rates
satisfying an important requirement for making data compatible
with legacy systems. In embodiments, fractional rates may also be converted to
integer ratio rates more readily because the length
of the data to be processed may be adjustable. It will be appreciated in light
of the disclosure that if the data was not streamed and
just stored as spectra with the standard or predetermined Fmax, it may be
impossible in certain situations to convert it retroactively
and accurately to the order-sampled data. It will also be appreciated in light
of the disclosure that internal identification issues may
also need to be reconciled. In many examples, stream data may be converted to
the proper sampling rate and resolution as described
and stored (albeit temporarily) in an EP legacy data repository 5070 to ensure
compatibility with legacy data.
[0312] To support legacy data identification issues, a user input module 5072
is shown in many embodiments should there be no
automated process (whether partially or wholly) for identification
translation. In such examples, one or more legacy systems (i.e.,
pre-existing data acquisition) may be characterized in that the data to be
imported is in a fully standardized format such as a
MimosaTM format, and other similar formats. Moreover, sufficient indentation
of the legacy data and/or the one or more machines
from which the legacy data was produced may be required in the completion of
an identification mapping table 5074 to associate
and link a portion of the legacy data to a portion of the newly acquired
stream data 5050. In many examples, the end user and/or
legacy vendor may be able to supply sufficient information to complete at
least a portion of a functioning identification (ID) mapping
table 5074 and therefore may provide the necessary database schema for the raw
data of the legacy system to be used for comparison,
analysis, and manipulation of newly streamed data 5050.
[0313] In embodiments, the local data control application 5062 may also direct
streaming data as well as extracted/processed (EP)
data to a cloud network facility 5080 via wired or wireless transmission. From
the cloud network facility 5080 other devices may
access, receive, and maintain data including the data from a master raw data
server (MRDS) 5082. The movement, distribution,
storage, and retrieval of data remote to the DAQ instrument 5002 may be
coordinated by the cloud data management services
(CDMS) 5084.
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[0314] Figure 23 shows additional methods and systems that include the DAQ
instrument 5002 accessing related cloud based
services. In embodiments, the DAQ API 5052 may control the data collection
process as well as its sequence. By way of these
examples, the DAQ API 5052 may provide the capability for editing processes,
viewing plots of the data, controlling the processing
of that data, viewing the output data in all its myriad forms, analyzing this
data including expert analysis, and communicating with
external devices via the local data control application 5062 and with the CDMS
5084 via the cloud network facility 5080. In
embodiments, the DAQ API 5052 may also govern the movement of data, its
filtering, as well as many other housekeeping functions.
[0315] In embodiments, an expert analysis module 5100 may generate reports
5102 that may use machine or measurement point
specific information from the PCSA information store 5040 to analyze the
stream data 5050 using a stream data analyzer module
5104 and the local data control application 5062 with the extract/process (EP)
align module 5068. In embodiments, the expert
analysis module 5100 may generate new alarms or ingest alarm settings into an
alarms module 5108 that is relevant to the stream
data 5050. In embodiments, the stream data analyzer module 5104 may provide a
manual or automated mechanism for extracting
meaningful information from the stream data 5050 in a variety of plotting and
report formats. In embodiments, a supervisory control
of the expert analysis module 5100 is provided by the DAQ API 5052. In further
examples, the expert analysis module 5100 may
be supplied (wholly or partially) via the cloud network facility 5080. In many
examples, the expert analysis module 5100 via the
cloud may be used rather than a locally-deployed expert analysis module 5100
for various reasons such as using the most up-to-date
software version, more processing capability, a bigger volume of historical
data to reference, and so on. In many examples, it may
be important that the expert analysis module 5100 be available when an
internet connection cannot be established so having this
redundancy may be crucial for seamless and time efficient operation. Toward
that end, many of the modular software applications
and databases available to the DAQ instrument 5002 where applicable may be
implemented with system component redundancy to
provide operational robustness to provide connectivity to cloud services when
needed but also operate successfully in isolated
scenarios where connectivity is not available and sometime not available
purposefully to increase security and the like.
[0316] In embodiments, the DAQ instrument acquisition may require a real time
operating system (RTOS) for the hardware
especially for streamed gap-free data that is acquired by a PC. In some
instances, the requirement for a RTOS may result in (or may
require) expensive custom hardware and software capable of running such a
system. In many embodiments, such expensive custom
hardware and software may be avoided and an RTOS may be effectively and
sufficiently implemented using a standard WindowsTM
operating systems or similar environments including the system interrupts in
the procedural flow of a dedicated application included
in such operating systems.
[0317] The methods and systems disclosed herein may include, connect to, or be
integrated with one or more DAQ instruments
and in the many embodiments, Figure 24 shows methods and systems that include
the DAQ instrument 5002 (also known as a
streaming DAQ or an SDAQ). In embodiments, the DAQ instrument 5002 may
effectively and sufficiently implement an RTOS
using standard windows operating system (or other similar personal computing
systems) that may include a software driver
configured with a First In, First Out (FIFO) memory area 5152. The FIFO memory
area 5152 may be maintained and hold
information for a sufficient amount of time to handle a worst-case interrupt
that it may face from the local operating system to
effectively provide the RTOS. In many examples, configurations on a local
personal computer or connected device may be
maintained to minimize operating system interrupts. To support this, the
configurations may be maintained, controlled, or adjusted
to eliminate (or be isolated from) any exposure to extreme environments where
operating system interrupts may become an issue.
In embodiments, the DAQ instrument 5002 may produce a notification, alarm,
message, or the like to notify a user when any gap
errors are detected. In these many examples, such errors may be shown to be
rare and even if they occur, the data may be adjusted
knowing when they occurred should such a situation arise.
[0318] In embodiments, the DAQ instrument 5002 may maintain a sufficiently
large FIFO memory area 5152 that may buffer the
incoming data so as to be not affected by operating system interrupts when
acquiring data. It will be appreciated in light of the
disclosure that the predetermined size of the FIFO memory area 5152 may be
based on operating system interrupts that may include
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Windows system and application functions such as the writing of data to Disk
or SSD, plotting, GUI interactions and standard
Windows tasks, low-level driver tasks such as servicing the DAQ hardware and
retrieving the data in bursts, and the like.
[0319] In embodiments, the computer, controller, connected device or the like
that may be included in the DAQ instrument 5002
may be configured to acquire data from the one or more hardware devices over a
USB port, firewire, ethernet, or the like. In
embodiments, the DAQ driver services 5054 may be configured to have data
delivered to it periodically so as to facilitate providing
a channel specific FIFO memory buffer that may be configured to not miss data,
i.e. it is gap-free. In embodiments, the DAQ driver
services 5054 may be configured so as to maintain an even larger (than the
device) channel specific FIFO area 5152 that it fills with
new data obtained from the device. In embodiments, the DAQ driver services
5054 may be configured to employ a further process
in that the raw data server 5058 may take data from the FIFO 5152and may write
it as a contiguous stream to non-volatile storage
areas such as the stream data repository 5060 that may be configured as one or
more disk drives, SSDs, or the like. In embodiments,
the FIFO 5152 may be configured to include a starting and stopping marker or
pointer to mark where the latest most current stream
was written. By way of these examples, a FIFO end marker 5154 may be
configured to mark the end of the most current data until
it reaches the end of the spooler and then wraps around constantly cycling
around. In these examples, there is always one megabyte
(or other configured capacities) of the most current data available in the
FIFO 5152 once the spooler fills up. It will be appreciated
in light of the disclosure that further configurations of the FIFO memory area
may be employed. In embodiments, the DAQ driver
services 5054 may be configured to use the DAQ API 5052 to pipe the most
recent data to a high-level application for processing,
graphing and analysis purposes. In some examples, it is not required that this
data be gap-free but even in these instances, it is
helpful to identify and mark the gaps in the data. Moreover, these data
updates may be configured to be frequent enough so that the
user would perceive the data as live. In the many embodiments, the raw data is
flushed to non-volatile storage without a gap at least
for the prescribed amount of time and examples of the prescribed amount of
time may be about thirty seconds to over four hours.
It will be appreciated in light of the disclosure that many pieces of
equipment and their components may contribute to the relative
needed duration of the stream of gap-free data and those durations may be over
four hours when relatively low speeds are present
in large numbers, when non-periodic transient activity is occurring on a
relatively long time frame, when duty cycle only permits
operation in relevant ranges for restricted durations and the like.
[0320] With reference to Figure 23, the stream data analyzer module 5104 may
provide for the manual or extraction of information
from the data stream in a variety of plotting and report formats. In
embodiments, resampling, filtering (including anti-aliasing),
transfer functions, spectrum analysis, enveloping, averaging, peak detection
functionality, as well as a host of other signal processing
tools, may be available for the analyst to analyze the stream data and to
generate a very large array of snapshots. It will be
appreciated in light of the disclosure that much larger arrays of snapshots
are created than ever would have been possible by
scheduling the collection of snapshots beforehand, i.e. during the initial
data acquisition for the measurement point in question.
[0321] Figure 25 depicts a display 5200 whose viewable content 5202 may be
accessed locally or remotely, wholly or partially.
In many embodiments, the display 5200 may be part of the DAQ instrument 5002,
may be part of the PC or connected device 5038
that may be part of the DAQ instrument 5002, or its viewable content 5202 may
be viewable from associated network connected
displays. In further examples, the viewable content 5202 of the display 5200
or portions thereof may be ported to one or more
relevant network addresses. In the many embodiments, the viewable content 5202
may include a screen 5204 that shows, for
example, an approximately two-minute data stream 5208 may be collected at a
sampling rate of 25.6 kHz for four channels 5220,
5222, 5224, 5228, simultaneously. By way of these examples and in these
configurations, the length of the data may be
approximately 3.1 megabytes. It will be appreciated in light of the disclosure
that the data stream (including each of its four channels
or as many as applicable) may be replayed in some aspects like a magnetic tape
recording (i.e., like a reel-to-reel or a cassette) with
all of the controls normally associated such playback such as forward 5230,
fast forward, backward 5232, fast rewind, step back,
step forward, advance to time point, retreat to time point, beginning 5234,
end5238, play 5240, stop 5242, and the like. Additionally,
the playback of the data stream may further be configured to set a width of
the data stream to be shown as a contiguous subset of
the entire stream. In the example with a two-minute data stream, the entire
two minutes may be selected by the select all button
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5244, or some subset thereof is selected with the controls on the screen 5204
or that may be placed on the screen 5204 by configuring
the display 5200 and the DAQ instrument 5002. In this example, the process
selected data button 5250 on the screen 5204 may be
selected to commit to a selection of the data stream.
[0322] Figure 26 depicts the many embodiments that include a screen 5204 on
the display 5200 displaying results of selecting all
of the data for this example. In embodiments, the screen 5204 in Figure 26 may
provide the same or similar playback capabilities
of what is depicted on the screen 5204 shown in Figure 25 but additionally
includes resampling capabilities, waveform displays,
and spectrum displays. It will be appreciated in light of the disclosure that
this functionality may permit the user to choose in many
situations any Fmax less than that supported by the original streaming
sampling rate. In embodiments, any section of any size may
be selected and further processing, analytics, and tools for looking at and
dissecting the data may be provided. In embodiments, the
screen 5250 may include four windows 5252, 5254, 5258, 5260 that show the
stream data from the four channels 5220, 5222, 5224,
5228 of Figure 25. In embodiments, the screen 5250 may also include offset and
overlap controls 5262, resampling controls 5264,
and the like.
[0323] In many examples, any one of many transfer functions may be established
between any two channels such as the two
channels 5280, 5282 that may be shown on a screen 5284 shown on the display
5200, as shown in Figure 27. The selection of the
two channels 5280, 5282 on the screen 5284 may permit the user to depict the
output of the transfer function on any of the screens
including screen 5284 and screen 5204.
[0324] In embodiments, Figure 28 shows a high-resolution spectrum screen 5300
on the display 5200 with a waveform view 5302,
full cursor control 5304 and a peak extraction view 5308. In these examples,
the peak extraction view 5308 may be configured with
a resolved configuration 5310 that may be configured to provide enhanced
amplitude and frequency accuracy and may use spectral
sideband energy distribution. The peak extraction view 5308 may also be
configured with averaging 5312, phase and cursor vector
information 5314, and the like.
[0325] In embodiments, Figure 29 shows an enveloping screen 5350 on the
display 5200 with a waveform view 5352, and a
spectral format view 5354. The views 5352, 5354 on the enveloping screen 5350
may display modulation from the signal in both
waveform and spectral formats. In embodiments, Figure 30 shows a relative
phase screen 5380 on the display 5200 with four phase
views 5382, 5384, 5388, 5390. The four phase views 5382, 5384, 5388, 5390
relate to the on spectrum the enveloping screen 5350
that may display modulation from the signal in waveform format in view 5352
and spectral format in view 5354. In embodiments,
the reference channel control 5392 may be selected to use channel four as a
reference channel to determine relative phase between
each of the channels.
[0326] It will be appreciated in light of the disclosure that the sampling
rates of vibration data of up to 100 kHz (or higher in some
scenarios) may be utilized for non-vibration sensors as well. In doing so, it
will further be appreciated in light of the disclosure that
stream data in such durations at these sampling rates may uncover new patterns
to be analyzed due in no small part that many of
these types of sensors have not been utilized in this manner. It will also be
appreciated in light of the disclosure that different sensors
used in machinery condition monitoring may provide measurements more akin to
static levels rather than fast-acting dynamic
signals. In some cases, faster response time transducers may have to be used
prior to achieving the faster sampling rates.
[0327] In many embodiments, sensors may have a relatively static output such
as temperature, pressure, or flow but may still be
analyzed with dynamic signal processing system and methodologies as disclosed
herein. It will be appreciated in light of the
disclosure that the time scale, in many examples, may be slowed down. In many
examples, a collection of temperature readings
collected approximately every minute for over two weeks may be analyzed for
their variation solely or in collaboration or in fusion
with other relevant sensors. By way of these examples, the direct current
level or average level may be omitted from all the readings
(e.g., by subtraction) and the resulting delta measurements may be processed
(e.g., through a Fourier transform). From these
examples, resulting spectral lines may correlate to specific machinery
behavior or other symptoms present in industrial system
processes. In further examples, other techniques include enveloping that may
look for modulation, wavelets that may look for
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spectral patterns that last only for a short time (i.e., bursts), cross-
channel analysis to look for correlations with other sensors
including vibration, and the like.
[0328] Figure 31 shows a DAQ instrument 5400 that may be integrated with one
or more analog sensors 5402 and endpoint nodes
5404 to provide a streaming sensor 5410 or smart sensors that may take in
analog signals and then process and digitize them, and
then transmit them to one or more external monitoring systems 5412 in the many
embodiments that may be connected to, interfacing
with, or integrated with the methods and systems disclosed herein. The
monitoring system 5412 may include a streaming hub server
5420 that may communicate with the cloud data management services (CDMS) 5084.
In embodiments, the CDMS 5084 may
contact, use, and integrate with cloud data 5430 and cloud services 5432 that
may be accessible through one or more cloud network
facilities 5080. In embodiments, the steaming hub server 5420 may connect with
another streaming sensor 5440 that may include
a DAQ instrument 5442, an endpoint node 5444, and the one or more analog
sensors such as analog sensor 5448. The steaming
hub server 5420 may connect with other streaming sensors such as the streaming
sensor 5460 that may include a DAQ instrument
5462, an endpoint node 5464, and the one or more analog sensors such as analog
sensor 5468.
[0329] In embodiments, there may be additional streaming hub servers such as
the steaming hub server 5480 that may connect
with other streaming sensors such as the streaming sensor 5490 that may
include a DAQ instrument 5492, an endpoint node 5494,
and the one or more analog sensors such as analog sensor 5498. In embodiments,
the steaming hub server 5480 may also connect
with other streaming sensors such as the streaming sensor 5500 that may
include a DAQ instrument 5502, an endpoint node 5504,
and the one or more analog sensors such as analog sensor 5508. In embodiments,
the transmission may include averaged overall
levels and in other examples may include dynamic signal sampled at a
prescribed and/or fixed rate. In embodiments, the streaming
sensors 5410, 5440, 5460, 5490, 5500 may be configured to acquire analog
signals and then apply signal conditioning to those
analog signals including coupling, averaging, integrating, differentiating,
scaling, filtering of various kinds, and the like. The
streaming sensors 5410, 5440, 5460, 5490, 5500 may be configured to digitize
the analog signals at an acceptable rate and resolution
(number of bits) and further processing the digitized signal when required.
The streaming sensors 5410, 5440, 5460, 5490, 5500
may be configured to transmit the digitized signals at pre-determined,
adjustable, and re-adjustable rates. In embodiments, the
streaming sensors 5410, 5440,5460, 5490, 5500 are configured to acquire,
digitize, process, and transmit data at a sufficient effective
rate so that a relatively consistent stream of data may be maintained for a
suitable amount of time so that a large number of effective
analyses may be shown to be possible. In the many embodiments, there would be
no gaps in the data stream and the length of data
should be relatively long, ideally for an unlimited amount of time, although
practical considerations typically require ending the
stream. It will be appreciated in light of the disclosure that this long
duration data stream with effectively no gap in the stream is in
contrast to the more commonly used burst collection where data is collected
for a relatively short period of time (i.e., a short burst
of collection), followed by a pause, and then perhaps another burst collection
and so on. In the commonly used collections of data
collected over noncontiguous bursts, data would be collected at a slow rate
for low frequency analysis and high frequency for high
frequency analysis. In many embodiments of the present disclosure, the
streaming data is in contrast (i) being collected once, (ii)
being collected at the highest useful and possible sampling rate, and (iii)
being collected for a long enough time that low frequency
analysis may be performed as well as high frequency. To facilitate the
collection of the streaming data, enough storage memory
must be available on the one or more streaming sensors such as the streaming
sensors 5410, 5440, 5460, 5490, 5500 so that new
data may be off-loaded externally to another system before the memory
overflows. In embodiments, data in this memory would be
stored into and accessed from in FIFO mode (First-In, First-Out). In these
examples, the memory with a FIFO area may be a dual
port so that the sensor controller may write to one part of it while the
external system reads from a different part. In embodiments,
data flow traffic may be managed by semaphore logic.
[0330] It will be appreciated in light of the disclosure that vibration
transducers that are larger in mass will have a lower linear
frequency response range because the natural resonance of the probe is
inversely related to the square root of the mass and will be
lowered. Toward that end, a resonant response is inherently non-linear and so
a transducer with a lower natural frequency will have
a narrower linear passband frequency response. It will also be appreciated in
light of the disclosure that above the natural frequency
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the amplitude response of the sensor will taper off to negligible levels
rendering it even more unusable. With that in mind, high
frequency accelerometers, for this reason, tend to be quite small in mass of
the order of half of a gram. It will also be appreciated
in light of the disclosure that adding the required signal processing and
digitizing electronics required for streaming may, in certain
situations, render the sensors incapable in many instances of measuring high-
frequency activity.
[0331] In embodiments, streaming hubs such as the streaming hubs 5420, 5480
may effectively move the electronics required for
streaming to an external hub via cable. It will be appreciated in light of the
disclosure that the streaming hubs may be located
virtually next to the streaming sensors or up to a distance supported by the
electronic driving capability of the hub. In instances
where an internet cache protocol (ICP) is used, the distance supported by the
electronic driving capability of the hub would be
anywhere from 100 to 1000 feet (30.5 to 305 meters) based on desired frequency
response, cable capacitance and the like. In
embodiments, the streaming hubs may be positioned in a location convenient for
receiving power as well as connecting to a network
(be it LAN or WAN). In embodiments, other power options would include solar,
thermal as well as energy harvesting. Transfer
between the streaming sensors and any external systems may be wireless or
wired and may include such standard communication
technologies as 802.11 and 900 MHz wireless systems, Ethernet, USB, firewire
and so on.
[0332] With reference to Figure 22, the many examples of the DAQ instrument
5002 include embodiments where data that may
be uploaded from the local data control application 5062 to the master raw
data server (MRDS) 5082. In embodiments, information
in the multimedia probe (MMP) and probe control, sequence and analytical
(PCSA) information store 5040 may also be downloaded
from the MRDS 5082 down to the DAQ instrument 5002. Further details of the
MRDS 5082 are shown in Figure 32 including
embodiments where data may be transferred to the MRDS 5082 from the DAQ
instrument 5002 via a wired or wireless network, or
through connection to one or more portable media, drive, other network
connections, or the like. In embodiments, the DAQ
instrument 5002 may be configured to be portable and may be carried on one or
more predetermined routes to assess predefined
points of measurement. In these many examples, the operating system that may
be included in the MRDS 5082 may be WindowsTM,
LinuxTM, or MacOSTM operating systems or other similar operating systems and
in these arrangements, the operating system,
modules for the operating system, and other needed libraries, data storage,
and the like may be accessible wholly or partially through
access to the cloud network facility 5080. In embodiments, the MRDS 5082 may
reside directly on the DAQ instrument 5002
especially in on-line system examples. In embodiments, the DAQ instrument 5002
may be linked on an intra-network in a facility
but may otherwise but behind a firewall. In further examples, the DAQ
instrument 5002 may be linked to the cloud network facility
5080. In the various embodiments, one of the computers or mobile computing
devices may be effectively designated the MRDS
5082 to which all of the other computing devices may feed it data such as one
of the MRDS 6104, as depicted in Figures 41 and 42.
In the many examples where the DAQ instrument 5002 may be deployed and
configured to receive stream data in a swarm
environment, one or more of the DAQ instruments 5002 may be effectively
designated the MRDS 5082 to which all of the other
computing devices may feed it data. In the many examples where the DAQ
instrument 5002 may be deployed and configured to
receive stream data in an environment where the methods and systems disclosed
herein are intelligently assigning, controlling,
adjusting, and re-adjusting data pools, computing resources, network bandwidth
for local data collection, and the like one or more
of the DAQ instruments 5002 may be effectively designated the MRDS 5082 to
which all of the other computing devices may feed
it data.
[0333] With further reference to Figure 32, new raw streaming data, data that
have been through extract, process, and align
processes (EP data), and the like may be uploaded to one or more master raw
data servers as needed or as scaled to in various
environments. In embodiments, a master raw data server (MRDS) 5700 may connect
to and receive data from other master raw
data servers such as the MRDS 5082. The MRDS 5700 may include a data
distribution manager module 5702. In embodiments,
the new raw streaming data may be stored in the new stream data repository
5704. In many instances, like raw data streams stored
on the DAQ instrument 5002, the new stream data repository 5704 and new
extract and process data repository 5708 may be
similarly configured as a temporary storage area.
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[0334] In embodiments, the MRDS 5700 may include a stream data analyzer module
5710 with an extract and process alignment
module. The analyzer module 5710 may be shown to be a more robust data
analyzer and extractor than may be typically found on
portable streaming DAQ instruments although it may be deployed on the DAQ
instrument 5002 as well. In embodiments, the
analyzer module 5710 takes streaming data and instantiates it at a specific
sampling rate and resolution similar to the local data
control module 5062 on the DAQ instrument 5002. The specific sampling rate and
resolution of the analyzer module 5710 may be
based on either user input 5712 or automated extractions from a multimedia
probe (MMP) and the probe control, sequence and
analytical (PCSA) information store 5714 and/or an identification mapping
table 5718, which may require the user input 5712 if
there is incomplete information regarding various forms of legacy data similar
to as was detailed with the DAQ instrument 5002.
In embodiments, legacy data may be processed with the analyzer module 5710 and
may be stored in one or more temporary holding
areas such as a new legacy data repository 5720. One or more temporary areas
may be configured to hold data until it is copied to
an archive and verified. The analyzer 5710 module may also facilitate in-depth
analysis by providing many varying types of signal
processing tools including but not limited to filtering, Fourier transforms,
weighting, resampling, envelope demodulation, wavelets,
two-channel analysis, and the like. From this analysis, many different types
of plots and mini-reports 5724 may be generated from
a reports and plots module 5724. In embodiments, data is sent to the
processing, analysis, reports, and archiving (PARA) server
5730 upon user initiation or in an automated fashion especially for on-line
systems.
[0335] In embodiments (Figures 33-34), a processing, analysis, reports, and
archiving (PARA) server 5750 may connect to and
receive data from other PARA servers such as the PARA server 5730. With
reference to Figure 33, the PARA server 5730 may
provide data to a supervisory module 5752 on the PARA server 5750 that may be
configured to provide at least one of processing,
analysis, reporting, archiving, supervisory, and similar functionalities. The
supervisory module 5752 may also contain extract,
process align functionality and the like. In embodiments, incoming streaming
data may first be stored in a raw data stream archive
5760 after being properly validated. Based on the analytical requirements
derived from a multimedia probe (MMP) and probe
control, sequence and analytical (PCSA) information store 5762 as well user
settings, data may be extracted, analyzed, and stored
in an extract and process (EP) raw data archive 5764. In embodiments, various
reports from a reports module 5768 are generated
from the supervisory module 5752. The various reports from the reports module
5768 include trend plots of various smart bands,
overalls along with statistical patterns, and the like. In embodiments, the
reports module 5768 may also be configured to compare
incoming data to historical data. By way of these examples, the reports module
5768 may search for and analyze adverse trends,
sudden changes, machinery defect patterns, and the like. In embodiments, the
PARA server 5750 may include an expert analysis
module 5770 from which reports generated and analysis may be conducted. Upon
completion, archived data may be fed to a local
master server (LMS) 5772 via a server module 5774 that may connect to the
local area network. In embodiments, archived data
may also be fed to the LMS 5772 via a cloud data management server (CDMS) 5778
through a server application for a cloud
network facility 5780. In embodiments, the supervisory module 5752 on the PARA
server 5750 may be configured to provide at
least one of processing, analysis, reporting, archiving, supervisory, and
similar functionalities from which alarms may be generated,
rated, stored, modifying, reassigned, and the like with an alarm generator
module 5782.
[0336] Figure 34 depicts various embodiments that include a processing,
analysis, reports, and archiving (PARA) server 5800 and
its connection to a local area network (LAN) 5802. In embodiments, one or more
DAQ instruments such as the DAQ instrument
5002 may receive and process analog data from one or more analog sensors 5711
that may be fed into the DAQ instrument 5002.
As discussed herein, the DAQ instrument 5002 may create a digital stream of
data based on the ingested analog data from the one
or more analog sensors. The digital stream from the DAQ instrument 5002 may be
uploaded to the MRDS 5082 and from there, it
may be sent to the PARA server 5800 where multiple terminals such as terminal
5810 5812, 5814 may each interface with it or the
MRDS 5082 and view the data and/or analysis reports. In embodiments, the PARA
server 5800 may communicate with a network
data server 5820 that may include a local master server (LMS) 5822. In these
examples, the LMS 5822 may be configured as an
optional storage area for archived data. The LMS 5822 may also be configured
as an external driver that may be connected to a PC
or other computing device that may run the LMS 5822 or the LMS 5822 may be
directly run by the PARA server 5800 where the
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LMS 5822 may be configured to operate and coexist with the PARA server 5800.
The LMS 5822 may connect with a raw data
stream archive 5824, an extra and process (EP) raw data archive 5828, and a
multimedia probe (MMP) and probe control, sequence
and analytical (PCSA) information store 5830. In embodiments, a cloud data
management server (CDMS) 5832 may also connect
to the LAN 5802 and may also support the archiving of data.
[0337] In embodiments, portable connected devices 5850 such a tablet 5852 and
a smart phone 5854 may connect the CDMS 5832
using web APIs 5860 and 5862, respectively, as depicted in Figure 35. The APIs
5860, 5862 may be configured to execute in a
browser and may permit access via a cloud network facility 5780 of all (or
some of) the functions previously discussed as accessible
through the PARA Server 5800. In embodiments, computing devices of a user 5880
such as computing devices 5882, 5884, 5888
may also access the cloud network facility 5780 via a browser or other
connection in order to receive the same functionality. In
embodiments, thin-client apps which do not require any other device drivers
and may be facilitated by web services supported by
cloud services 5890 and cloud data 5892. In many examples, the thin-client
apps may be developed and reconfigured using, for
example, the visual high-level LabVIEWTM programming language with NXGTM Web-
based virtual interface subroutines. In
embodiments, thin client apps may provide high-level graphing functions such
as those supported by LabVIEWTM tools. In
embodiments, the LabVIEWTM tools may generate JSCRIPTTm code and JAVATM code
that may be edited post-compilation. The
NXGTM tools may generate Web VI' s that may not require any specialized driver
and only some RESTfulTm services which may be
readily installed from any browser. It will be appreciated in light of the
disclosure that because various applications may be run
inside a browser, the applications may be run on any operating system, be it
WindowsTM, LinuxTM, and AndroidTM operating systems
especially for personal devices, mobile devices, portable connected devices,
and the like.
[0338] In embodiments, the CDMS 5832 is depicted in greater detail in Figure
36. In embodiments, the CDMS 5832 may provide
all of the data storage and services that the PARA Server 5800 (Figure 34) may
provide. In contrast, all of the API' s may be web
API' s which may run in a browser and all other apps may run on the PARA
Server 5800 or the DAQ instrument 5002 may typically
be WindowsTM, LinuxTM or other similar operating systems. In embodiments, the
CDMS 5832 includes at least one of or
combinations of the following functions. The CDMS 5832 may include a cloud GUI
5900 that may be configured to provide access
to all data, plots including trend, waveform, spectra, envelope, transfer
function, logs of measurement events, analysis including
expert, utilities, and the like. In embodiments, the CDMS 5832 may include a
cloud data exchange 5902 configured to facilitate the
transfer of data to and from the cloud network facility 5780. In embodiments,
the CDMS 5832 may include a cloud plots/trends
module 5904 that may be configured to show all plots via web apps including
trend, waveform, spectra, envelope, transfer function,
and the like. In embodiments, the CDMS 5832 may include a cloud reporter 5908
that may be configured to provide all analysis
reports, logs, expert analysis, trend plots, statistical information, and the
like. In embodiments, the CDMS 5832 may include a
cloud alarm module 5910. Alarms from the cloud alarm module 5910 may be
generated to various devices 5920 via email, texts,
or other messaging mechanisms. From the various modules, data may be stored in
new data 5914. The various devices 5920 may
include a terminal 5922, portable connected device 5924, or a tablet 5928. The
alarms from the cloud alarm module are designed
to be interactive so that the end user may acknowledge alarms in order to
avoid receiving redundant alarms and also to see significant
context-sensitive data from the alarm points that may include spectra,
waveform statistical info, and the like.
[0339] In embodiments, a relational database server (RDS) 5930 may be used to
access all of the information from a multimedia
probe (MMP) and probe control, sequence and analytical (PCSA) information
store 5932. As with the PARA server 5800 (Figure
36), information from the information store 5932 may be used with an extra,
process (EP) and align module 5934, a data exchange
5938 and the expert system 5940. In embodiments, a raw data stream archive
5942 and extract and process raw data archive 5944
may also be used by the EP align 5934, the data exchange 5938 and the expert
system 5940 as with the PARA server 5800. In
embodiments, new stream raw data 5950, new extract and process raw data 5952,
and new data 5954 (essentially all other raw data
such as overalls, smart bands, stats, and data from the information store
5932) are directed by the CDMS 5832.
[0340] In embodiments, the streaming data may be linked with the RDS 5930 and
the MMP and PCSA information store 5932
using a technical data management streaming (TDMS) file format. In
embodiments, the information store 5932 may include tables
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for recording at least portions of all measurement events. By way of these
examples, a measurement event may be any single data
capture, a stream, a snapshot, an averaged level, or an overall level. Each of
the measurement events in addition to point
identification information may also have a date and time stamp. In
embodiments, a link may be made between the streaming data,
the measurement event, and the tables in the information store 5932 using the
TDMS format. By way of these examples, the link
may be created by storing a unique measurement point identification codes with
a file structure having the TDMS format by
including and assigning TDMS properties. In embodiments, a file with the TDMS
format may allow for three levels of hierarchy.
By way of these examples, the three levels of hierarchy may be root, group,
and channel. It will be appreciated in light of the
disclosure that the MimosaTM database schema may be, in theory, unlimited.
With that said, there are advantages to limited TDMS
hierarchies. In the many examples, the following properties may be proposed
for adding to the TDMS Stream structure while using
a Mimosa Compatible database schema.
[0341] Root Level:
[0342] Global ID 1: Text String (This could be a unique ID obtained from the
web.)
[0343] Global ID 2: Text String (This could be an additional ID obtained from
the web.)
[0344] Company Name: Text String
[0345] Company ID: Text String
[0346] Company Segment ID: 4-byte Integer
[0347] Company Segment ID: 4-byte Integer
[0348] Site Name: Text String
[0349] Site Segment ID: 4-byte Integer
[0350] Site Asset ID: 4-byte Integer
[0351] Route Name: Text String
[0352] Version Number: Text String
[0353] Group Level:
[0354] Section 1 Name: Text String
[0355] Section 1 Segment ID: 4-byte Integer
[0356] Section 1 Asset ID: 4-byte Integer
[0357] Section 2 Name: Text String
[0358] Section 2 Segment ID: 4-byte Integer
[0359] Section 2 Asset ID: 4-byte Integer
[0360] Machine Name: Text String
[0361] Machine Segment ID: 4-byte Integer
[0362] Machine Asset ID: 4-byte Integer
[0363] Equipment Name: Text String
[0364] Equipment Segment ID: 4-byte Integer
[0365] Equipment Asset ID: 4-byte Integer
[0366] Shaft Name: Text String
[0367] Shaft Segment ID: 4-byte Integer
[0368] Shaft Asset ID: 4-byte Integer
[0369] Bearing Name: Text String
[0370] Bearing Segment ID: 4-byte Integer
[0371] Bearing Asset ID: 4-byte Integer
[0372] Probe Name: Text String
[0373] Probe Segment ID: 4-byte Integer
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[0374] Probe Asset ID: 4-byte Integer
[0375] Channel Level:
[0376] Channel #: 4-byte Integer
[0377] Direction: 4-byte Integer (in certain examples may be text)
[0378] Data Type: 4-byte Integer
[0379] Reserved Name 1: Text String
[0380] Reserved Segment ID 1: 4-byte Integer
[0381] Reserved Name 2: Text String
[0382] Reserved Segment ID 2: 4-byte Integer
[0383] Reserved Name 3: Text String
[0384] Reserved Segment ID 3: 4-byte Integer
[0385] In embodiments, the file with the TDMS format may automatically use
property or asset information and may make an
index file out of the specific property and asset information to facilitate
database searches. It will be appreciated in light of the
disclosure that the TDMS format may offer a compromise for storing voluminous
streams of data because it may be optimized for
storing binary streams of data but may also include some minimal database
structure making many standard SQL operations feasible.
It will also be appreciated in light of the disclosure that the TDMS format
and functionality discussed herein may not be as efficient
as a full-fledged SQL relational database, the TDMS format, however, may take
advantages of both worlds in that it may balance
between the class or format of writing and storing large streams of binary
data efficiently and the class or format of a fully relational
database which facilitates searching, sorting and data retrieval. In
embodiments, an optimum solution may be found such that
metadata required for analytical purposes and extracting prescribed lists with
panel conditions for stream collection may be stored
in the RDS 5930 by establishing a link between the two database methodologies.
By way of these examples, relatively large analog
data streams may be stored predominantly as binary storage in the raw data
stream archive 5942 for rapid stream loading but with
inherent relational SQL type hooks, formats, conventions, or the like. The
files with the TDMS format may also be configured to
incorporate DIAdemTM reporting capability of LabVIEWTM software so as to
provide a further mechanism to facilitate conveniently
and rapidly accessing the analog or the streaming data.
[0386] The methods and systems disclosed herein may include, connect to, or be
integrated with a virtual data acquisition
instrument and in the many embodiments, Figure 37 shows methods and systems
that include a virtual streaming data acquisition
(DAQ) instrument 6000 also known as a virtual DAQ instrument, a VRDS, or a
VSDAQ. In contrast to the DAQ instrument 5002
(Figure 22), the virtual DAQ instrument 6000 may be configured so to only
include one native application. In the many examples,
the one permitted one native application may be the DAQ driver module 6002
that may manage all communications with the DAQ
Device 6004 that may include streaming capabilities. In embodiments, other
applications, if any, may be configured as thin client
web applications such as RESTfulTm web services. The one native application or
other applications or services may be accessible
through the DAQ Web API 6010. The DAQ Web API 6010 may run in or be accessible
through various web browsers.
[0387] In embodiments, storage of streaming data, as well as the extraction
and processing of streaming data into extract and
process data, may be handled primarily by the DAQ driver services 6012 under
the direction of the DAQ Web API 6010. In
embodiments, the output from sensors of various types including vibration,
temperature, pressure, ultrasound and so on may be fed
into the instrument inputs of the DAQ device 6004. In embodiments, the signals
from the output sensors may be signal conditioned
with respect to scaling and filtering and digitized with an analog to digital
converter. In embodiments, the signals from the output
sensors may be signals from all relevant channels simultaneously sampled at a
rate sufficient to perform the maximum desired
frequency analysis. In embodiments, the signals from the output sensors may be
sampled for a relatively long time, gap-free as one
continuous stream so as to enable a wide array of further post-processing at
lower sampling rates with sufficient samples. In further
examples, streaming frequency may be adjusted (and readjusted) to record
streaming data at non-evenly spaced recording. For
temperature data, pressure data, and other similar data that may be relatively
slow, varying delta times between samples may further
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improve quality of the data. By way of the above examples, data may be
streamed from a collection of points and then the next set
of data may be collected from additional points according to a prescribed
sequence, route, path, or the like. In the many examples,
the portable sensors may be moved to the next location according to the
prescribed sequence but not necessarily all of them as some
may be used for reference phase or otherwise. In further examples, a
multiplexer 6020 may be used to switch to the next collection
of points or a mixture of the two methods may be combined.
[0388] In embodiments, the sequence and panel conditions that may be used to
govern the data collection process using the virtual
DAQ instrument 6000 may be obtained from the MMP PCSA information store 6022.
The MMP PCSA information store 6022
may include such items as the hierarchical structural relationships of the
machine, e.g., a machine contains pieces of equipment in
which each piece of equipment contains shafts and each shaft is associated
with bearings, which may be monitored by specific types
of transducers or probes according to a specific prescribed sequence (routes,
path, etc.) with specific panel conditions. By way of
these examples, the panel conditions may include hardware specific switch
settings or other collection parameters such as sampling
rate, AC/DC coupling, voltage range and gain, integration, high and low pass
filtering, anti-aliasing filtering, ICPTM transducers and
other integrated-circuit piezoelectric transducers, 4-20 mA loop sensors, and
the like. The information store 6022 includes other
information that may be stored in what would be machinery specific features
that would be important for proper analysis including
the number of gear teeth for a gear, the number of blades in a pump impeller,
the number of motor rotor bars, bearing specific
parameters necessary for calculating bearing frequencies, lx rotating speed
(e.g., RPMs) of all rotating elements, and the like.
[0389] Upon direction of the DAQ Web API 6010 software, digitized waveforms
may be uploaded using the DAQ driver services
6012 of the virtual DAQ instrument 6000. In embodiments, data may then be fed
into an RLN data and control server 6030 that
may store the stream data into a network stream data repository 6032. Unlike
the DAQ instrument 5002, the server 6030 may run
from within the DAQ driver module 6002. It will be appreciated in light of the
disclosure that a separate application may require
drivers for running in the native operating system and for this instrument
only the instrument driver may run natively. In many
examples, all other applications may be configured to be browser based. As
such, a relevant network variable may be very similar
to a LabVIEWTM shared or network stream variable which may be designed to be
accessed over one or more networks or via web
applications.
[0390] In embodiments, the DAQ Web API 6010 may also direct the local data
control application 6034 to extract and process the
recently obtained streaming data and, in turn, convert it to the same or lower
sampling rates of sufficient length to provide the
desired resolution. This data may be converted to spectra, then averaged and
processed in a variety of ways and stored as
extracted/processed (EP) data 6040. The EP data repository 6040 but this
repository may, in certain embodiments, only be meant
for temporary storage. It will be appreciated in light of the disclosure that
legacy data may require its own sampling rates and
resolution and often this sampling rate may not be integer proportional to the
acquired sampling rate especially for order-sampled
data whose sampling frequency is related directly to an external frequency,
which is typically the running speed of the machine or
its internal componentry, rather than the more-standard sampling rates
produced by the internal crystals, clock functions, and the
like of the (e.g., values of Fmax of 100, 200, 500, 1K, 2K, 5K, 10K, 20K and
so on) of the DAQ instrument 5002, 6000. In
embodiments, the EP (extract/process) align component of the local data
control application 6034 is able to fractionally adjust the
sampling rate to the non-integer ratio rates that may be more applicable to
legacy data sets and therefore driving compatibility with
legacy systems. In embodiments, the fractional rates may be converted to
integer ratio rates more readily because the length of the
data to be processed (or at least that portion of the greater stream of data)
is adjustable because of the depth and content of the
original acquired streaming data by the DAQ instrument 5002, 6000. It will be
appreciated in light of the disclosure that if the data
was not streamed and just stored as traditional snap-shots of spectra with the
standard values of Fmax, it may very well be impossible
to convert retroactively and accurately the acquired data to the order-sampled
data. In embodiments, the stream data may be
converted, especially for legacy data purposes, to the proper sampling rate
and resolution as described and stored in the EP legacy
data repository 6042. To support legacy data identification scenarios, a user
input 6044 may be included should there be no
automated process for identification translation. In embodiments, one such
automated process for identification translation may
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include importation of data from a legacy system that may contain fully
standardized format such as MimosaTM format and sufficient
identification information to complete an ID Mapping Table 6048. In further
examples, the end user, a legacy data vendor, a legacy
data storage facility, or the like may be able to supply enough info to
complete (or sufficiently complete) relevant portions of the
ID Mapping Table 6048 to provide, in turn, the database schema for the raw
data of the legacy system so it may be readily ingested,
saved, and use for analytics in the current systems disclosed herein.
[0391] Figure 38 depicts further embodiments and details of the virtual DAQ
Instrument 6000. In these examples, the DAQ Web
API 6010 may control the data collection process as well as its sequence. The
DAQ Web API 6010 may provide the capability for
editing this process, viewing plots of the data, controlling the processing of
that data and viewing the output in all its myriad forms,
analyzing this data including the expert analysis, communicating with external
devices via the DAQ driver module 6002, as well as
communicating with and transferring both streaming data and EP data to one or
more cloud network facilities 5080 whenever
possible. In embodiments, the virtual DAQ instrument itself and the DAQ Web
API 6010 may run independently of access to cloud
network facilities 5080 when local demands may require or simply results in no
outside connectivity such use throughout a
proprietary industrial setting. In embodiments, the DAQ Web API 6010 may also
govern the movement of data, its filtering as well
as many other housekeeping functions.
[0392] The virtual DAQ Instrument 6000 may also include an expert analysis
module 6052. In embodiments, the expert analysis
module 6052 may be a web application or other suitable modules that may
generate reports 6054 that may use machine or
measurement point specific information from the MMP PCSA information store
6022 to analyze stream data 6058 using the stream
data analyzer module 6050. In embodiments, supervisory control of the expert
analysis module 6052 may be provided by the DAQ
Web API 6010. In embodiments, the expert analysis may also be supplied (or
supplemented) via the expert system module 5940
that may be resident on one or more cloud network facilities that are
accessible via the CDMS 5832. In many examples, expert
analysis via the cloud may be preferred over local systems such the expert
analysis module 6052 for various reasons such as the
availability and use of the most up-to-date software version, more processing
capability, a bigger volume of historical data to
reference and the like. It will be appreciated in light of the disclosure that
it may be important to offer expert analysis when an
internet connection cannot be established so as to provide a redundancy, when
needed, for seamless and time efficient operation. In
embodiments, this redundancy may be extended to all of the discussed modular
software applications and databases where applicable
so each module discussed herein may be configured to provide redundancy to
continue operation in the absence of an internet
connection.
[0393] Figure 39 depicts further embodiments and details of many virtual DAQ
instruments existing in an online system and
connecting through network endpoints through a central DAQ instrument to one
or more cloud network facilities. In embodiments,
a master DAQ instrument with network endpoint 6060 is provided along with
additional DAQ instruments such as a DAQ instrument
with network endpoint 6062, a DAQ instrument with network endpoint 6064, and a
DAQ instrument with network endpoint 6068.
The master DAQ instrument with network endpoint 6060 may connect with the
other DAQ instruments with network endpoints
6062, 6064, 6068 over a local area network (LAN) 6070. It will be appreciated
that each of the instruments 6060, 6062, 6064, 6068
may include personal computer, connected device, or the like that include
WindowsTM, LinuxTM or other suitable operating systems
to facilitate, among other things, ease of connection of devices utilizing
many wired and wireless network options such as Ethernet,
wireless 802.11g, 900 MHz wireless (e.g., for better penetration of walls,
enclosures and other structural barriers commonly
encountered in an industrial setting) as well as a myriad of others permitting
use of off-the-shelf communication hardware when
needed.
[0394] Figure 40 depicts further embodiments and details of many functional
components of an endpoint that may be used in the
various settings, environments, and network connectivity settings. The
endpoint includes endpoint hardware modules 6080. In
embodiments, the endpoint hardware modules 6080 may include one or more
multiplexers 6082, a DAQ instrument 6084 as well
as a computer 6088, computing device, PC, or the like that may include the
multiplexers, DAQ instruments, and computers,
connected devices and the like disclosed herein. The endpoint software modules
6090 include a data collector application (DCA)
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6092 and a raw data server (RDS) 6094. In embodiments, DCA 6092 may be similar
to the DAQ API 5052 (Figure 22) and may
be configured to be responsible for obtaining stream data from the DAQ device
6084 and storing it locally according to a prescribed
sequence or upon user directives. In the many examples, the prescribed
sequence or user directives may be a LabVIEWTm software
app that may control and read data from the DAQ instruments. For cloud based
online systems, the stored data in many embodiments
may be network accessible. In many examples, LabVIEWTm tools may be used to
accomplish this with a shared variable or network
stream (or subsets of shared variables). Shared variables and the affiliated
network streams may be network objects that may be
optimized for sharing data over the network. In many embodiments, the DCA 6092
may be configured with a graphic user interface
that may be configured to collect data as efficiently and fast as possible and
push it to the shared variable and its affiliated network
stream. In embodiments, the endpoint raw data server 6094 may be configured to
read raw data from the single-process shared
variable and may place it with a master network stream. In embodiments, a raw
stream of data from portable systems may be stored
locally and temporarily until the raw stream of data is pushed to the MRDS
5082 (Figure 22). It will be appreciated in light of the
disclosure that on-line system instruments on a network either local or
remote, LAN or WAN are termed endpoints and for portable
data collector applications that may or may not be wirelessly connected to one
or more cloud network facilities, then the endpoint
term may be omitted as described to describe an instrument may not require
network connectivity.
[0395] Figures 41 and 42 depict further embodiments and details of multiple
endpoints with their respective software blocks with
at least one of the devices configured as master blocks. Each of the blocks
may include a data collector application (DCA) 7000
and a raw data server (RDS) 7002. In embodiments, each of the blocks may also
include a master raw data server module (MRDS)
7004, a master data collection and analysis module (MDCA) 7008, and a
supervisory and control interface module (SCI) 7010. The
MRDS 7004 may be configured to read network stream data (at a minimum) from
the other endpoints and may forward it up to one
or more cloud network facilities via the CDMS 5832 including the cloud
services 5890 and the cloud data 5892. In embodiments,
the CDMS 5832 may be configured to store the data and provides web, data, and
processing services. In these examples, this may
be implemented with a LabVIEWTM application that may be configured to read
data from the network streams or shared variables
from all of the local endpoints, writes them to the local host PC, local
computing device, connected device, or the like, as both a
network stream and file with TDMSTm formatting. In embodiments, the CDMS 5832
may also be configured to then post this data
to the appropriate buckets using the LabVIEW or similar software that may be
supported by 53TM web service from the AWSTM
(Amazon Web Services) on the AmazonTM web server, or the like and may
effectively serve as a back-end server. In the many
examples, different criteria may be enabled or may be set up for when to post
data, to create and adjust schedules, to create and
adjust event triggering including a new data event, a buffer full message, one
or more alarms messages, and the like.
[0396] In embodiments, the MDCA 7008 may be configured to provide automated as
well as user-directed analyses of the raw
data that may include tracking and annotating specific occurrence and in doing
so, noting where reports may be generated and
alarms may be noted. In embodiments, the SCI 7010 may be an application
configured to provide remote control of the system
from the cloud as well as the ability to generate status and alarms. In
embodiments, the SCI 7010 may be configured to connect to,
interface with, or be integrated into a supervisory control and data
acquisition (SCADA) control system. In embodiments, the SCI
7010 may be configured as a LabVIEWTM application that may provide remote
control and status alerts that may be provided to any
remote device that may connect to one or more of the cloud network facilities
5870.
[0397] In embodiments, the equipment that is being monitored may include RFID
tags that may provide vital machinery analysis
background information. The RFID tags may be associated with the entire
machine or associated with the individual componentry
and may be substituted when certain parts of the machine are replaced, repair,
or rebuilt. The RFID tags may provide permanent
information relevant to the lifetime of the unit or may also be re-flashed to
update with at least portion of new information. In many
embodiments, the DAQ instruments 5002 disclosed herein may interrogate the one
or RFID chips to learn of the machine, its
componentry, its service history, and the hierarchical structure of how
everything is connected including drive diagrams, wire
diagrams, and hydraulic layouts. In embodiments, some of the information that
may be retrieved from the RFID tags includes
manufacturer, machinery type, model, serial number, model number,
manufacturing date, installation date, lots numbers, and the
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like. By way of these examples, machinery type may include the use of a
MimosaTM format table including information about one
or more of the following motors, gearboxes, fans, and compressors. The
machinery type may also include the number of bearings,
their type, their positioning, and their identification numbers. The
information relevant to the one or more fans includes fan type,
number of blades, number of vanes, and number belts. It will be appreciated in
light of the disclosure that other machines and their
componentry may be similarly arranged hierarchically with relevant information
all of which may be available through interrogation
of one or more RFID chips associated with the one or more machines.
[0398] Industrial components such as pumps, compressors, air conditioning
units, mixers, agitators, motors, and engines may be
play critical roles in the operation of equipment in a variety of environments
including as part of manufacturing equipment in
industrial environments such as factories, gas handling systems mining
operations, automotive systems and the like.
[0399] There are a wide variety of pumps such as a variety of positive
displacement pumps, velocity pumps, and impulse pumps.
Velocity or centrifugal pumps typically comprise an impeller with curved
blades which, when an impeller is immersed in a fluid,
such as water or a gas, causes the fluid or gas to rotate in the same
rotational direction as the impeller. As the fluid or gas rotates,
centrifugal force causes it to move to the outer diameter of the pump, e.g.
the pump housing, where it can be collected and further
processed. The removal of the fluid or gas from the outer circumference may
result in lower pressure at a pump input orifice causing
new fluid or gas to be drawn into the pump.
[0400] Positive displacement pumps may comprise reciprocating pumps,
progressive cavity pumps, gear or screw pumps, such as
reciprocating pumps typically comprise a piston which alternately creates
suction which opens an inlet valve and draws a liquid or
gas into a cylinder and pressure which closes the inlet valve and forces the
liquid or gas present out of the cylinder through an outlet
valve. This method of pumping may result in periodic waves of pressurized
liquid or gas being introduced into the downstream
system.
[0401] Some automotive vehicles such as cars and trucks may use a water
cooling system to keep the engine from overheating. In
some automobiles, a centrifugal water pump, driven by a belt associated with a
drive shaft of the vehicle, is used to force a mixture
of water and coolant through the engine to maintain an acceptable engine
temperature. Overheating of the engine may be highly
destructive to the engine and yet it may be difficult or costly to access a
water pump installed in a vehicle.
[0402] In embodiments, a vehicle water pump may be equipped with a plurality
of sensors for measuring attributes associated
with the water pump such as temperature of bearings or pump housing, vibration
of a drive shaft associated with the pump, liquid
leakage and the like. These sensors may be connected either directly to a
monitoring device or through an intermediary device using
a mix of wired and wireless connection techniques. A monitoring device may
have access to detection values corresponding to the
sensors where the detection values correspond directly to the sensor output or
a processed version of the data output such as a
digitized or sampled version of the sensor output, and/or a virtual sensor or
modeled value correlated from other sensed values. The
monitoring device may access and process the detection values using methods
discussed elsewhere herein to evaluate the health of
the water pump and various components of the water pump prone to wear and
failure, e.g. bearings or sets of bearings, drive shafts,
motors, and the like. The monitoring device may process the detection values
to identify a torsion of the drive shaft of the pump.
The identified torsion may then be evaluated relative to expected torsion
based on the specific geometry of the water pump and how
it is installed in the vehicle. Unexpected torsion may put undue stress on the
drive shaft and may be a sign of deteriorating health of
the pump. The monitoring device may process the detection values to identify
unexpected vibrations in the shaft or unexpected
temperature values or temperature changes in the bearings or in the housing in
proximity to the bearings. In some embodiments, the
sensors may include multiple temperature sensors positioned around the water
pump to identify hot spots among the bearings or
across the pump housing which might indicated potential bearing failure. The
monitoring device may process the detection values
associated with water sensors to identify liquid leakage near the pump which
may indicate a bad seal. The detection values may be
jointly analyzed to provide insight into the health of the pump.
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[0403] In an illustrative example, detection values associated with a vehicle
water pump may show a sudden increase in vibration
at a higher frequency than the operational rotation of the pump with a
corresponding localized increase of temperature associated
with a specific phase in the pump cycle. Together these may indicate a
localized bearing failure.
[0404] Production lines may also include one or more pumps for moving a
variety of material including acidic or corrosive
materials, flammable materials, minerals, fluids comprising particulates of
varying sizes, high viscosity fluids, variable viscosity
fluids, or high-density fluids. Production line pumps may be designed to
specifically meet the needs of the production line including
pump composition to handle the various material types, torque needed to move
the fluid at the desired speed or with the desired
pressure. Because these production lines may be continuous process lines, it
may be desirable to perform proactive maintenance
rather than wait for a component to fail. Variations in pump speed and
pressure may have the potential to negatively impact the
final product and the ability to identify issues in the final product may lag
the actual component deterioration by an unacceptably
long period.
[0405] In embodiments, an industrial pump may be equipped with a plurality of
sensors for measuring attributes associated with
the pump such as temperature of bearings or pump housing, vibration of a drive
shaft associated with the pump, vibration of input
or output lines, pressure, flow rate, fluid particulate measures, vibrations
of the pump housing and the like. These sensors may be
connected either directly to a monitoring device or through an intermediary
device using a mix of wired and wireless connection
techniques. A monitoring device may have access to detection values
corresponding to the sensors where the detection values
correspond directly to the sensor output of a processed version of the data
output such as a digitized or sampled version of the sensor
output. The monitoring device may access and process the detection values
using methods discussed elsewhere herein to evaluate
the health of the pump overall, evaluate the health of pump components,
predict potential down line issues arising from atypical
pump performance or changes in fluid being pumped. The monitoring device may
process the detection values to identify torsion
on the drive shaft of the pump. The identified torsion may then be evaluated
relative to expected torsion based on the specific
geometry of the pump and how it is installed in the equipment relative to
other components on the assembly line. Unexpected torsion
may put undue stress on the drive shaft and may be a sign of deteriorating
health of the pump. Vibration of the inlet and outlet pipes
may also be evaluated for unexpected or resonant vibrations which may be used
to drive process controls to avoid certain pump
frequencies. Changes in vibration may also be due to changes in fluid
composition or density amplifying or dampening vibrations
as certain frequencies. The monitoring device may process the detection values
to identify unexpected vibrations in the shaft,
unexpected temperature values or temperature changes in the bearings or in the
housing in proximity to the bearings. In some
embodiments, the sensors may include multiple temperature sensors positioned
around the pump to identify hot spots among the
bearings or across the pump housing which might indicated potential bearing
failure. For some pumps, when the fluid being pumped
is corrosive or contains large amounts of particulate, there may be damage to
the interior components of the pump in contact with
the fluid due to cumulative exposure to the fluid. This may be reflected in
unanticipated variations in output pressure. Additionally
or alternatively, if a gear in a gear pump begins to corrode and no longer
forces all the trapped fluid out this may result in increased
pump speed, fluid cavitation, and/or unexpected vibrations in the output pipe.
[0406] Compressors increase the pressure of a gas by decreasing the volume
occupied by the gas or increasing the amount of the
gas in a confined volume. There may be positive-displacement compressors that
utilize the motion of pistons or rotary screws to
move the gas into a pressurized holding chamber. There are dynamic
displacement gas compressors that use centrifugal force to
accelerate the gas into a stationary compressor where the kinetic energy is
converted to pressure. Compressors may be used to
compress various gases for use on an assembly line. Compressed air may power
pneumatic equipment on an assembly line. In the
oil and gas industry flash gas compressors may be used to compress gas so that
is leaves a hydrocarbon liquid when it enters a lower
pressure environment. Compressors may be used to restore pressure in gas and
oil pipelines, to mix fluids of interest, and/or to
transfer or transport fluids of interest. Compressors may be used to enable
the underground storage of natural gas.
[0407] Like pumps, compressors may be equipped with a plurality of sensors for
measuring attributes associated with the
compressor such as temperature of bearings or compressor housing. vibration of
a drive shaft, transmission, gear box and the like
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associated with the compressor, vessel pressure, flow rate, and the like.
These sensors may be connected either directly to a
monitoring device or through an intermediary device using a mix of wired and
wireless connection techniques. A monitoring device
may have access to detection values corresponding to the sensors where the
detection values correspond directly to the sensor output
of a processed version of the data output such as a digitized or sampled
version of the sensor output. The monitoring device may
access and process the detection values using methods described elsewhere
herein to evaluate the health of the compressor overall,
evaluate the health of compressor components and/or predict potential down
line issues arising from atypical compressor
performance. The monitoring device may process the detection values to
identify torsion on a drive shaft of the compressor. The
identified torsion may then be evaluated relative to expected torsion based on
the specific geometry of the compressor and how it is
installed in the equipment relative to other components and pieces of
equipment. Unexpected torsion may put undue stress on the
drive shaft and may be a sign of deteriorating health of the Compressor.
Vibration of the inlet and outlet pipes may also be evaluated
for unexpected or resonant vibrations which may be used to drive process
controls to avoid certain compressor frequencies. The
monitoring device may process the detection values to identify unexpected
vibrations in the shaft, unexpected temperature values
or temperature changes in the bearings or in the housing in proximity to the
bearings. In some embodiments, the sensors may include
multiple temperature sensors positioned around the compressor to identify hot
spots among the bearings or across the compressor
housing which might indicate potential bearing failure. In some embodiments,
sensors may monitor the pressure in a vessel storing
the compressed gas. Changes in the pressure or rate of pressure change may be
indicative of problems with the compressor.
[0408] Agitators and mixers are used in a variety of industrial environments.
Agitators may be used to mix together different
components such as liquids, solids or gases. Agitators may be used to promote
a more homogenous mixture of component materials.
Agitators may be used to promote a chemical reaction by increasing exposure
between different component materials and adding
energy to the system. Agitators may be used to promote heat transfer to
facilitate uniform heating or cooling of a material.
[0409] Mixers and agitators are used in such diverse industries as chemical
production, food production, pharmaceutical
production. There are paint and coating mixers, adhesive and sealant mixers,
oil and gas mixers, water treatment mixers, wastewater
treatment mixers and the like.
[0410] Agitators may comprise equipment that rotates or agitates an entire
tank or vessel in which the materials to be mixed are
located, such as a concrete mixer. Effective agitations may be influenced by
the number and shape of baffles in the interior of the
tank. Agitation by rotation of the tank or vessel may be influenced by the
axis of rotation relative to the shape of the tank, direction
of rotation and external forces such as gravity acting on the material in the
tank. Factors affecting the efficacy of material agitation
or mixing by agitation of the tank or vessel may include axes of rotation,
amplitude and frequency of vibration along different axes.
These factors may be selected based on the types of materials being selected,
their relative viscosities, specific gravities, particulate
count, any shear thinning or shear thickening anticipated for the component
materials or mixture, flow rates of material entering or
exiting the vessel or tank, direction and location of flows of material
entering of exiting the vessel, and the like.
[0411] Agitators, large tank mixers, portable tank mixers, tote tank mixers,
drum mixers, and mounted mixers (with various mount
types) may comprise a propeller or other mechanical device such as a blade,
vane, or stator inserted into a tank of materials to be
mixed and rotating a propeller or otherwise moving a mechanical device. These
may include airfoil impellers, fixed pitch blade
impellers, variable pitch blade impellers, anti-ragging impellers, fixed
radial blade impellers, marine-type propellers, collapsible
airfoil impellers, collapsible pitched blade impellers, collapsible radial
blade impellers, and variable pitch impellers. Agitators may
be mounted such that the mechanical agitation is centered in the tank.
Agitators may be mounted such that they are angled in a tank
or are vertically or horizontally offset from the center of the vessel. The
agitators may enter the tank from the above, below or the
side of the tank. There may be a plurality of agitators in a single tank to
achieve uniform mixing throughout the tank or container
of chemicals.
[0412] Agitators may include the strategic flow or introduction of component
materials into the vessel including the location and
direction of entry, rate of entry, pressure of entry, viscosity of material,
specific gravity of the material, and the like.
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[0413] Successful agitation of mixing of materials may occur with a
combination of techniques such as one or more propellers in
a baffled tank where components are being introduced at different locations
and at different rates.
[0414] In embodiments, an industrial mixer or agitator may be equipped with a
plurality of sensors for measuring attributes
associated with the industrial mixer such as temperature of bearings or tank
housing, vibration of drive shafts associated with a
propeller or other mechanical device such as a blade, vane or stator,
vibration of input or output lines, pressure, flow rate, fluid
particulate measures, vibrations of the tank housing and the like. These
sensors may be connected either directly to a monitoring
device or through an intermediary device using a mix of wired and wireless
connection techniques. A monitoring device may have
access to detection values corresponding to the sensors where the detection
values correspond directly to the sensor output of a
processed version of the data, output such as a digitized or sampled version
of the sensor output, fusion of data from multiple
sensors, and the like. The monitoring device may access and process the
detection values using methods discussed elsewhere herein
to evaluate the health of the agitator or mixer overall, evaluate the health
of agitator or mixer components, predict potential down
line issues arising from atypical performance or changes in composition of
material being agitated. For example, the monitoring
device may process the detection values to identify torsion on the drive shaft
of an agitating impeller. The identified torsion may
then be evaluated relative to expected torsion based on the specific geometry
of the agitator and how it is installed in the equipment
relative to other components and/or pieces of equipment. Unexpected torsion
may put undue stress on the drive shaft and may be a
sign of deteriorating health of the agitator. Vibration of inflow and outflow
pipes may be monitored for unexpected or resonant
vibrations which may be used to drive process controls to avoid certain
agitation frequencies. Inflow and outflow pipes may also be
monitored for unexpected flow rates, unexpected particulate content, and the
like. Changes in vibration may also be due to changes
in fluid composition or density amplifying or dampening vibrations as certain
frequencies. The monitoring device may distribute
sensors to collect detection values which may be used to identify unexpected
vibrations in the shaft, unexpected temperature values
or temperature changes in the bearings or in the housing in proximity to the
bearings. For some agitators, when the fluid being
agitated is corrosive or contains large amounts of particulate, there may be
damage to the interior components of the agitator (e.g.
baffles, propellers, blades, and the like) which are in contact with the
materials due to cumulative exposure to the materials.
[0415] HVAC, Air-conditioning systems and the like may use a combination of
compressors and fans to cool and circulate air in
industrial environments. Similar to the discussion of compressors and
agitators these systems may include a number of rotating
components whose failure or reduced performance might negatively impact the
working environment and potentially degrade
product quality. A monitoring device may be used to monitor sensors measuring
various aspects of the one or more rotating
components, the venting system, environmental conditions, and the like.
Components of the HVAC/air-conditioning systems may
include fan motors, drive shafts, bearings, compressors and the like. The
monitoring device may access and process the detection
values corresponding to the sensor outputs according to methods discussed
elsewhere herein to evaluate the overall health of the
air-conditioning unit, HVAC system, and like as well as components of these
systems, identify operational states, predict potential
issues arising from atypical performance, and the like. Evaluation techniques
may include bearing analysis, torsional analysis of
drive shafts, rotors and stators, peak value detection, and the like. The
monitoring device may process the detection values to identify
issues such as torsion on a drive shaft, potential bearing failures, and the
like.
[0416] Assembly lines conveyors may comprise a number of moving and rotating
components as part of a system for moving
material through a manufacturing process. These assembly lines conveyors may
operate over a wide range of speeds. These
conveyances may also vibrate at a variety of frequencies as they convey
material horizontally to facilitate screening, grading, laning
for packaging, spreading, dewatering, feeding product into the next in-line
process, and the like.
[0417] Conveyance systems may include engines or motors, one or more drive
shafts turning rollers or bearings along which a
conveyor belt may move. A vibrating conveyor may include springs and a
plurality of vibrators which vibrate the conveyor forward
in a sinusoidal manner.
[0418] In embodiments, conveyors and vibrating conveyors may be equipped with
a plurality of sensors for measuring attributes
associated with the conveyor such as temperature of benrin ac vihratinn rlf
drive chnftS, vibrations of rollers along which the
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conveyor travels, velocity and speed associated with the conveyor, and the
like. The monitoring device may access and process the
detection values using methods discussed elsewhere herein to evaluate the
overall health of the conveyor as well as components of
the conveyor, predict potential issues arising from atypical performance, and
the like. Techniques for evaluating the conveyors may
include bearing analysis, torsional analysis, phase detection/phase lock loops
to align detection values from different parts of the
conveyor, frequency transformations and frequency analysis, peak value
detection, and the like. The monitoring device may process
the detection values to identify torsion on a drive shaft, potential bearing
failures, uneven conveyance and like.
[0419] In an illustrative example, a paper-mill conveyance system may comprise
a mesh onto which the paper slurry is coated.
The mesh transports the slurry as liquid evaporates and the paper dries. The
paper may then be wound onto a core until the roll
reaches diameters of up to three meters. The transport speeds of the paper-
mill range from traditional equipment operating at 14-48
meters /min to new, high-speed equipment operating at close to 2000
meters/min. For slower machines, the paper may be winding
onto the roll at 14 meters/m which, towards the end of the roll having a
diameter of approximately three meters would indicate that
the take-up roll may be rotating at speeds on the order of a couple of
rotations a minute. Vibrations in the web conveyance or torsion
across the take-up roller may result in damage to the paper, skewing of the
paper on the web or skewed rolls which may result in
equipment downtime or product that is lower in quality or unusable.
Additionally, equipment failure may result in costly machine
shutdowns and loss of product. Therefore, the ability to predict problems and
provide preventative maintenance and the like may be
useful.
[0420] Monitoring truck engines and steering systems to facilitate timely
maintenance and avoid unexpected breakdowns may be
important. Health of the combustion chamber, rotating crankshafts, bearings
and the like may be monitored using a monitoring
device structured to interpret detection values received from a plurality of
sensors measuring a variety of characteristics associated
with engine components including temperature, torsion, vibration, and the
like. As discussed above, the monitoring device may
process the detection values to identify engine bearing health, torsional
vibrations on a crankshaft/drive shaft, unexpected vibrations
in the combustion chambers, overheating of different components and the like.
Processing may be done locally or data collected
across a number of vehicles and jointly analyzed. The monitoring device may
process detection values associated with the engine,
combustion chambers column, and the like. Sensors may monitor temperature,
vibration, torsion, acoustics and the like to identify
issues. A monitoring device or system may use techniques such as peak
detection, bearing analysis, torsion analysis, phase detection,
PLL, band pass filtering, to identify potential issues with the steering
system and bearing and torsion analysis to identify potential
issues with rotating components on the engine. This identification of
potential issues may be used to schedule timely maintenance,
reduce operation prior to maintenance and influence future component design.
[0421] Drilling machines and screwdrivers in the oil and gas industries may be
subjected to significant stresses. Because they are
frequently situated in remote locations, an unexpected breakdown may result in
extended down time due to the lead-time associated
with bringing in replacement components. The health of a drilling machine or
screwdriver and associated rotating crankshafts,
bearings and the like may be monitored using a monitoring device structured to
interpret detection values received from a plurality
of sensors measuring a variety of characteristics associated with the drilling
machine or screwdriver including temperature, torsion,
vibration, rotational speed, vertical speed, acceleration, image sensors, and
the like. As discussed above, the monitoring device may
process the detection values to identify equipment health, torsional
vibrations on a crankshaft/drive shaft, unexpected vibrations in
the component, overheating of different components and the like. Processing
may be done locally or data collected across a number
of machines and jointly analyzed. The monitoring device may jointly process
detection values, equipment maintenance records,
product records historical data, and the like to identify correlations between
detection values, current and future states of the
component, anticipated lifetime of the component or piece of equipment, and
the like. Sensors may monitor temperature, vibration,
torsion, acoustics and the like to identify issues such as unanticipated
torsion in the drill shaft, slippage in the gears, overheating
and the like. A monitoring device or system may use techniques such as peak
detection, bearing analysis, torsion analysis, phase
detection, PLL, band pass filtering, to identify potential issues. This
identification of potential issues may be used to schedule timely
maintenance, order new or replacement components, reduce operation prior to
maintenance and influence future component design.
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[0422] Similarly, it may be desirable to monitor the health of gearboxes
operating in an oil and gas field. A monitoring device
may be structured to interpret detection values received from a plurality of
sensors measuring a variety of characteristics associated
with the gearbox such as temperature, vibration, and the like. The monitoring
device may process the detection values to identify
gear and gearbox health and anticipated life. Processing may be done locally
or data collected across a number of gearboxes and
jointly analyzed. The monitoring device may jointly process detection values,
equipment maintenance records, product records
historical data, and the like to identify correlations between detection
values, current and future states of the gearbox, anticipated
lifetime of the gearbox and associated components, and the like. A monitoring
device or system may use techniques such as peak
detection, bearing analysis, torsion analysis, phase detection, PLL, band pass
filtering, to identify potential issues. This identification
of potential issues may be used to schedule timely maintenance, order new or
replacement components, reduce operation prior to
maintenance and influence future equipment design.
[0423] Refining tanks in the oil and gas industries may be subjected to
significant stresses due to the chemical reactions occurring
inside. Because a breach in a tank could result in the release of potentially
toxic chemicals it may be beneficial to monitor the
condition of the refining tank and associated components. Monitoring a
refining tank to collect a variety of ongoing data may be
used to predict equipment wear, component wear, unexpected stress and the
like. Given predictions about equipment health, such
as the status of a refining tank, may be used to schedule timely maintenance,
order new or replacement components, reduce
operation prior to maintenance and influence future component design. Similar
to the discussion above, a refining tank may be
monitored using a monitoring device structured to interpret detection values
received from a plurality of sensors measuring a variety
of characteristics associated with the refining tank such as temperature,
vibration, internal and external pressure, the presence of
liquid or gas at seams and ports, and the like. The monitoring device may
process the detection values to identify equipment health,
unexpected vibrations in the tank, overheating of the tank or uneven heating
across the tank and the like. Processing may be done
locally or data collected across a number of tanks and jointly analyzed. The
monitoring device may jointly process detection values,
equipment maintenance records, product records historical data, and the like
to identify correlations between detection values,
current and future states of the tank, anticipated lifetime of the tank and
associated components, and the like. A monitoring device
or system may use techniques such as peak detection, bearing analysis, torsion
analysis, phase detection, PLL, band pass filtering,
to identify potential issues.
[0424] Similarly, it may be desirable to monitor the health of centrifuges
operating in an oil and gas refinery. A monitoring device
may be structured to interpret detection values received from a plurality of
sensors measuring a variety of characteristics associated
with the centrifuge such as temperature, vibration, pressure, and the like.
The monitoring device may process the detection values
to identify equipment health, unexpected vibrations in the centrifuge,
overheating, pressure across the centrifuge, and the like.
Processing may be done locally or data collected across a number of
centrifuges and jointly analyzed. The monitoring device may
jointly process detection values, equipment maintenance records, product
records historical data, and the like to identify correlations
between detection values, current and future states of the centrifuge,
anticipated lifetime of the centrifuge and associated
components, and the like. A monitoring device or system may use techniques
such as peak detection, bearing analysis, torsion
analysis, phase detection, PLL, band pass filtering, to identify potential
issues. This identification of potential issues may be used
to schedule timely maintenance, order new or replacement components, reduce
operation prior to maintenance and influence future
equipment design.
[0425] In embodiments, information about the health or other status or state
information of or regarding a component or piece of
industrial equipment may be obtained by monitoring the condition of various
components throughout a process. Monitoring may
include monitoring the amplitude of a sensor signal measuring attributes such
as temperature, humidity, acceleration, displacement
and the like. An embodiment of a data monitoring device 8100 is shown in
Figure 43 and may include a plurality of sensors 8106
communicatively coupled to a controller 8102. The controller 8102 may include
a data acquisition circuit 8104, a data analysis
circuit 8108, a multiplexor (MUX) control circuit 8114, and a response circuit
8110. The data acquisition circuit 8104 may include
a multiplexor (MUX) 8112 where the inputs correspond to a subset of the
detection values. The multiplexor control circuit 8114
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may be structured to provide adaptive scheduling of the logical control of the
MUX and the correspondence of MUX input and
detected values based on a subset of the plurality of detection values and/or
a command from the response circuit 8110 and/or the
output of the data analysis circuit 8108. The data analysis circuit 8108 may
comprise one or more of a peak detection circuit, a phase
differential circuit, a phase lock loop circuit, a bandpass filter circuit, a
frequency transformation circuit, a frequency analysis circuit,
a torsional analysis circuit, a bearing analysis circuit, an overload
detection circuit, a sensor fault detection circuit, a vibrational
resonance circuit for the identification of unfavorable interaction among
machines or components, a distortion identification circuit
for the identification of unfavorable distortions such as deflections shapes
upon operation, overloading of weight, excessive forces,
stress and strain-based effects, and the like. The data analysis circuit 8108
may output a component health status as a result of the
analysis.
[0426] The data analysis circuit 8108 may determine a state, condition, or
status of a component, part, sub-system, or the like of
a machine, device, system or item of equipment (collectively referred to
herein as a component health status) based on a maximum
value of a MUX output for a given input or a rate of change of the value of a
MUC output for a given input. The data analysis circuit
8108 may determine a component health status based on a time integration of
the value of a MUX for a given input. The data
analysis circuit 8108 may determine a component health status based on phase
differential of MUX output relative to an on-board
time or another sensor. The data analysis circuit 8108 may determine a
component health status based a relationship of value, phase,
phase differential and rate of change for MUX outputs corresponding to one or
more input detection values. The data analysis circuit
8108 may determine a component health status based on process stage or
component specification or component anticipated state.
[0427] The multiplexor control circuit 8114 may adapt the scheduling of the
logical control of the multiplexor based on a
component health status, an anticipated component health status, the type of
component, the type of equipment being measured, an
anticipated state of the equipment, a process stage (different
parameters/sensor values may be important at different stages in a
process. The multiplexor control circuit 8114 may adapt the scheduling of the
logical control of the multiplexor based on a selected
sequence selected by a user or a remote monitoring application, on the basis
of a user request for a specific value. The multiplexor
control circuit 8114 may adapt the scheduling of the logical control of the
multiplexor based on the basis of a storage profile or plan
(such as based on type and availability of storage elements and parameters as
described elsewhere in this disclosure and in the
documents incorporated herein by reference), network conditions or
availability (also as described elsewhere in this disclosure and
in the documents incorporated herein by reference), or value or cost of
component or equipment.
[0428] The plurality of sensors 8106 may be wired to ports on the data
acquisition circuit 8104. The plurality of sensors 8106 may
be wirelessly connected to the data acquisition circuit 8104. The data
acquisition circuit 8104 may be able to access detection values
corresponding to the output of at least one of the plurality of sensors 8106
where the sensors 8106 may be capturing data on different
operational aspects of a piece of equipment or an operating component.
[0429] The selection of the plurality of sensors 8106 for a data monitoring
device 8100 designed for a specific component or piece
of equipment may depend on a variety of considerations such as accessibility
for installing new sensors, incorporation of sensors in
the initial design, anticipated operational and failure conditions, resolution
desired at various positions in a process or plant,
reliability of the sensors, and the like. The impact of a failure, time
response of a failure (e.g. warning time and/or off-nominal
modes occurring before failure), likelihood of failure, and/or sensitivity
required and/or difficulty to detection failure conditions
may drive the extent to which a component or piece of equipment is monitored
with more sensors and/or higher capability sensors
being dedicated to systems where unexpected or undetected failure would be
costly or have severe consequences.
[0430] Depending on the type of equipment, the component being measured, the
environment in which the equipment is operating
and the like, sensors 8106 may comprise one or more of, without limitation, a
vibration sensor, a thermometer, a hygrometer, a
voltage sensor and/or a current sensor (for the component and/or other sensors
measuring the component), an accelerometer, a
velocity detector, a light or electromagnetic sensor (e.g., determining
temperature, composition and/or spectral analysis, and/or
object position or movement), an image sensor, a structured light sensor, a
laser-based image sensor, a thermal imager, an acoustic
wave sensor, a displacement sensor, a turbidity meter, a viscosity meter. a
axial load sensor, a radial load sensor, a tri-axial sensor,
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an accelerometer, a speedometer, a tachometer, a fluid pressure meter, an air
flow meter, a horsepower meter, a flow rate meter, a
fluid particle detector, an optical (laser) particle counter, an ultrasonic
sensor, an acoustical sensor, a heat flux sensor, a galvanic
sensor, a magnetometer, a pH sensor, and the like, including, without
limitation, any of the sensors described throughout this
disclosure and the documents incorporated by reference.
[0431] The sensors 8106 may provide a stream of data over time that has a
phase component, such as relating to acceleration or
vibration, allowing for the evaluation of phase or frequency analysis of
different operational aspects of a piece of equipment or an
operating component. The sensors 8106 may provide a stream of data that is not
conventionally phase-based, such as temperature,
humidity, load, and the like. The sensors 8106 may provide a continuous or
near continuous stream of data over time, periodic
readings, event-driven readings, and/or readings according to a selected
interval or schedule.
[0432] The sensors 8106 may monitor components such as bearings, sets of
bearings, motors, drive shafts, pistons, pumps,
conveyors, vibrating conveyors, compressors, drills and the like in vehicles,
oil and gas equipment in the field, in assembly line
components, and the like.
[0433] In embodiments, as illustrated in Figure 43, the sensors 8106 may be
part of the data monitoring device 8100, referred to
herein in some cases as a data collector, which in some cases may comprise a
mobile or portable data collector. In embodiments, as
illustrated in Figures 44 and 45, one or more external sensors 8126, which are
not explicitly part of a monitoring device 8120 but
rather are new, previously attached to or integrated into the equipment or
component, may be opportunistically connected to or
accessed by the monitoring device 8120. The monitoring device 8120 may include
a controller 8122. The controller 8122 may
include a data acquisition circuit 8104, a data analysis circuit 8108, a
multiplexor (MUX) control circuit 8114, and a response circuit
8110. The data acquisition circuit 8104 may comprise a multiplexor (MUX) 8112
where the inputs correspond to a subset of the
detection values. The multiplexor control circuit 8114 may be structured to
provide the logical control of the MUX and the
correspondence of MUX input and detected values based on a subset of the
plurality of detection values and/or a command from
the response circuit 8110 and/or the output of the data analysis circuit 8108.
The data analysis circuit 8108 may comprise one or
more of a peak detection circuit, a phase differential circuit, a phase lock
loop circuit, a bandpass filter circuit, a frequency
transformation circuit, a frequency analysis circuit, a torsional analysis
circuit, a bearing analysis circuit, an overload detection
circuit, vibrational resonance circuit for the identification of unfavorable
interaction among machines or components, a distortion
identification circuit for the identification of unfavorable distortions such
as deflections shapes upon operation ,stress and strain-
based effects, and the like.
[0434] The one or more external sensors 8126 may be directly connected to the
one or more input ports 8128 on the data acquisition
circuit 8124 of the controller 8122 or may be accessed by the data acquisition
circuit 8104 wirelessly, such as by a reader,
interrogator, or other wireless connection, such as over a short-distance
wireless protocol. In embodiments as shown in Figure 45,
a data acquisition circuit 8124 may further comprise a wireless communication
circuit 8130. The data acquisition circuit 8124 may
use the wireless communication circuit 8130 to access detection values
corresponding to the one or more external sensors 8126
wirelessly or via a separate source or some combination of these methods.
[0435] In embodiments, as illustrated in Figure 46, the controller 8134 may
further comprise a data storage circuit 8136. The data
storage circuit 8136 may be structured to store one or more of sensor
specifications, component specifications, anticipated state
information, detected values, multiplexor output, component models, and the
like. The data storage circuit 8116 may provide
specifications and anticipated state information to the data analysis circuit
8108.
[0436] In embodiments, the response circuit 8110 may initiate a variety of
actions based on the sensor status provided by the data
analysis circuit 8108. The response circuit 8110 may adjust a sensor scaling
value (e.g. from 100mV/gram to 10 mV/gram). The
response circuit 8110 may select an alternate sensor from a plurality
available. The response circuit 8110 may acquire data from a
plurality of sensors of different ranges. The response circuit 8110 may
recommend an alternate sensor. The response circuit 8110
may issue an alarm or an alert.
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[0437] In embodiments, the response circuit 8110 may cause the data
acquisition circuit 8104 (which may comprise a multiplexor
(MUX) 8112) to enable or disable the processing of detection values
corresponding to certain sensors based on the component
status. This may include switching to sensors having different response rates,
sensitivity, ranges, and the like; accessing new sensors
or types of sensors, accessing data from multiple sensors, and the like.
Switching may be undertaken based on a model, a set of
rules, or the like. In embodiments, switching may be under control of a
machine learning system, such that switching is controlled
based on one or more metrics of success, combined with input data, over a set
of trials, which may occur under supervision of a
human supervisor or under control of an automated system. Switching may
involve switching from one input port to another (such
as to switch from one sensor to another). Switching may involve altering the
multiplexing of data, such as combining different
streams under different circumstances. Switching may involve activating a
system to obtain additional data, such as moving a mobile
system (such as a robotic or drone system), to a location where different or
additional data is available (such as positioning an image
sensor for a different view or positioning a sonar sensor for a different
direction of collection) or to a location where different sensors
can be accessed (such as moving a collector to connect up to a sensor that is
disposed at a location in an environment by a wired or
wireless connection). This switching may be implemented by directing changes
to the multiplexor (MUX) control circuit 8114.
[0438] In embodiments, the response circuit 8110 may make recommendations for
the replacement of certain sensors in the future
with sensors having different response rates, sensitivity, ranges, and the
like. The response circuit 8110 may recommend design
alterations for future embodiments of the component, the piece of equipment,
the operating conditions, the process, and the like.
[0439] In embodiments, the response circuit 8110 may recommend maintenance at
an upcoming process stop or initiate a
maintenance call where the maintenance may include the replacement of the
sensor with the same or an alternate type of sensor
having a different response rate, sensitivity, range and the like. In
embodiments, the response circuit 8110 may implement or
recommend process changes ¨ for example to lower the utilization of a
component that is near a maintenance interval, operating
off-nominally, or failed for purpose but still at least partially operational,
to change the operating speed of a component (such as to
put it in a lower-demand mode), to initiate amelioration of an issue (such as
to signal for additional lubrication of a roller bearing
set, or to signal for an alignment process for a system that is out of
balance), and the like.
[0440] In embodiments, the data analysis circuit 8108 and/or the response
circuit 8110 may periodically store certain detection
values and/or the output of the multiplexers and/or the data corresponding to
the logic control of the MUX in the data storage circuit
8136 to enable the tracking of component performance over time. In
embodiments, based on sensor status, as described elsewhere
herein recently measured sensor data and related operating conditions such as
RPMs, component loads, temperatures, pressures,
vibrations or other sensor data of the types described throughout this
disclosure in the data storage circuit 8116 to enable the backing
out of overloaded/failed sensor data. The signal evaluation circuit 8108 may
store data at a higher data rate for greater granularity
in future processing, the ability to reprocess at different sampling rates,
and/or to enable diagnosing or post-processing of system
information where operational data of interest is flagged, and the like.
[0441] In embodiments as shown in Figures 47 and 48 and 49 and 50, a data
monitoring system 8138 8160 may include at least
one data monitoring device 8140. The at least one data monitoring device 8140
may include sensors 8106 and a controller 8142
comprising a data acquisition circuit 8104, a data analysis circuit 8108, a
data storage circuit 8136, and a communication circuit
8146 to allow data and analysis to be transmitted to a monitoring application
8150 on a remote server 8148.
[0442] The data analysis circuit 8108 may include at least an overload
detection circuit and/or a sensor fault detection circuit. The
data analysis circuit 8108 may periodically share data with the communication
circuit 8146 for transmittal to the remote server 8148
to enable the tracking of component and equipment performance over time and
under varying conditions by a monitoring application
8150. Based on the sensor status, the data analysis circuit 8108 and/or
response circuit 8110 may share data with the communication
circuit 8146 for transmittal to the remote server 8148 based on the fit of
data relative to one or more criteria. Data may include
recent sensor data and additional data such as RPMS, component loads,
temperatures, pressures, vibrations, and the like for
transmittal. The data analysis circuit 8108 may share data at a higher data
rate for transmittal to enable greater granularity in
processing on the remote server.
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[0443] In embodiments as shown in Figure 47, the communication circuit 8146
may communicated data directly to a remote server
8148. In embodiments as shown in Figure 48, the communication circuit 8146 may
communicate data to an intermediate computer
8152 which may include a processor 8154 running an operating system 8156 and a
data storage circuit 8158.
[0444] In embodiments as illustrated in Figures 49 and 50, a data collection
system 8160 may have a plurality of monitoring
devices 8140 collecting data on multiple components in a single piece of
equipment, collecting data on the same component across
a plurality of pieces of equipment, (both the same and different types of
equipment) in the same facility as well as collecting data
from monitoring devices in multiple facilities. A monitoring application 8150
on a remote server 8148 may receive and store one
or more of detection values, timing signals and data coming from a plurality
of the various monitoring devices 8140.
[0445] In embodiments as shown in Figure 49, the communication circuit 8146
may communicated data directly to a remote server
8148. In embodiments as shown in Figure 50, the communication circuit 8146 may
communicate data to an intermediate computer
8152 which may include a processor 8154 running an operating system 8156 and a
data storage circuit 8158. There may be an
individual intermediate computer 8152 associated with each monitoring device
8140 or an individual intermediate computer 8152
may be associated with a plurality of monitoring devices 8140 where the
intermediate computer 8152 may collect data from a
plurality of data monitoring devices and send the cumulative data to the
remote server 8148. Communication to the remote server
8148 may be streaming, batch (e.g. when a connection is available) or
opportunistic.
[0446] The monitoring application 8150 may select subsets of the detection
values to jointly analyzed. Subsets for analysis may
be selected based on a single type of sensor, component or a single type of
equipment in which a component is operating. Subsets
for analysis may be selected or grouped based on common operating conditions
such as size of load, operational condition (e.g.
intermittent, continuous), operating speed or tachometer, common ambient
environmental conditions such as humidity, temperature,
air or fluid particulate, and the like. Subsets for analysis may be selected
based on the effects of other nearby equipment such as
nearby machines rotating at similar frequencies, nearby equipment producing
electromagnetic fields, nearby equipment producing
heat, nearby equipment inducing movement or vibration, nearby equipment
emitting vapors, chemicals or particulates, or other
potentially interfering or intervening effects.
[0447] In embodiments, the monitoring application 8150 may analyze the
selected subset. In an illustrative example, data from a
single sensor may be analyzed over different time periods such as one
operating cycle, several operating cycles, a month, a year,
the life of the component or the like. Data from multiple sensors of a common
type measuring a common component type may also
be analyzed over different time periods. Trends in the data such as changing
rates of change associated with start-up or different
points in the process may be identified. Correlation of trends and values for
different sensors may be analyzed to identify those
parameters whose short-term analysis might provide the best prediction
regarding expected sensor performance. This information
may be transmitted back to the monitoring device to update sensor models,
sensor selection, sensor range, sensor scaling, sensor
sampling frequency, types of data collected and analyzed locally or to
influence the design of future monitoring devices.
[0448] In embodiments, the monitoring application 8150 may have access to
equipment specifications, equipment geometry,
component specifications, component materials, anticipated state information
for a plurality of sensors, operational history,
historical detection values, sensor life models and the like for use analyzing
the selected subset using rule-based or model-based
analysis. The monitoring application 8150 may provide recommendations
regarding sensor selection, additional data to collect, data
to store with sensor data. The monitoring application 8150 may provide
recommendations regarding scheduling repairs and/or
maintenance. The monitoring application 8150 may provide recommendations
regarding replacing a sensor. The replacement sensor
may match the sensor being replaced or the replacement sensor may have a
different range, sensitivity, sampling frequency and the
like.
[0449] In embodiments, the monitoring application 8150 may include a remote
learning circuit structured to analyze sensor status
data (e.g. sensor overload, sensor failure) together with data from other
sensors, failure data on components being monitored,
equipment being monitored, product being produced, and the like. The remote
learning system may identify correlations between
sensor overload and data from other sensors.
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[0450] 1. A monitoring system for data collection in an industrial
environment, the monitoring system comprising:
a data acquisition circuit structured to interpret a plurality of detection
values, each of the plurality of detection values corresponding
to input received from at least one of a plurality of input sensors;
a multiplexor (MUX) having inputs corresponding to a subset of the detection
values;
a MUX control circuit structured to interpret a subset of the plurality of
detection values and provide the logical control of the MUX
and the correspondence of MUX input and detected values as a result, wherein
the logic control of the MUX comprises adaptive
scheduling of the select lines;
a data analysis circuit structured to receive an output from the MUX and data
corresponding to the logic control of the MUX resulting
in a component health status; and
an analysis response circuit to perform at least one operation in response to
the component health status, wherein the plurality of
sensors includes at least two sensors selected from the group consisting of a
temperature sensor, a load sensor, a vibration sensor,
an acoustic wave sensor, a heat flux sensor, an infrared sensor, an
accelerometer, a tri-axial vibration sensor and a tachometer.
[0451] 2. The monitoring system of claim 1, wherein at least one of the
plurality of detection values may correspond to a fusion
of two or more input sensors representing a virtual sensor.
[0452] 3. The monitoring system of claim 1, wherein the system further
comprises a data storage circuit structured for storing at
least one of component specifications and anticipated component state
information and buffering a subset of the plurality of detection
values for a predetermined length of time.
[0453] 4. The monitoring system of claim 1, wherein the system further
comprises a data storage circuit structured for storing at
least one of component specifications and anticipated component state
information and buffering the output of the multiplexor and
data corresponding to the logic control of the MUX for a predetermined length
of time.
[0454] 5. The monitoring system of claim 1, wherein the data analysis circuit
comprises at least one of a peak detection circuit, a
phase detection circuit, a bandpass filter circuit, a frequency transformation
circuit, a frequency analysis circuit, a phase lock loop
circuit, a torsional analysis circuit, and a bearing analysis circuit.
[0455] 6. The monitoring system of claim 3, wherein the at least one operation
further comprises storing additional data in the
data storage circuit.
[0456] 7. The monitoring system of claim 1, wherein the at least one operation
comprises at least one of enabling or disabling one
or more portions of the multiplexer circuit.
[0457] 8. The monitoring system of claim 1, wherein the at least one operation
comprises causing the multiplexor control circuit
to alter the logical control of the MUX and the correspondence of MUX input
and detected values.
[0458] 9. A monitoring system for data collection in an industrial
environment, the monitoring system comprising:
a data acquisition circuit structured to interpret a plurality of detection
values, each of the plurality of detection values corresponding
to input received from at least one of a plurality of input sensors;
at least two multiplexors (MUX), each having inputs corresponding to a subset
of the detection values and each providing a data
stream as output;
a MUX control circuit structured to interpret a subset of the plurality of
detection values and provide the logical control of the at
least two MUX and control of the correspondence of MUX input and detected
values as a result, wherein the logic control of the
MUX comprises adaptive scheduling of the select lines;
a data analysis circuit structured to receive the data stream from at least
one of the at least two MUX and data corresponding to the
logic control of the MUX resulting in a component health status; and
an analysis response circuit to perform at least one operation in response to
the component health status, wherein the plurality of
sensors includes at least two sensors selected from the group consisting of a
temperature sensor, a load sensor, a vibration sensor,
an acoustic wave sensor, a heat flux sensor, an infrared sensor, an
accelerometer, a tri-axial vibration sensor and a tachometer.
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[0459] 10. The monitoring system of claim 9, wherein at least one of the
plurality of detection values may correspond to a fusion
of two or more input sensors representing a virtual sensor.
[0460] 11. The monitoring system of claim 9, wherein the system further
comprises a data storage circuit structured for storing at
least one of component specifications and anticipated component state
information and buffering a subset of the plurality of detection
values for a predetermined length of time.
[0461] 12. The monitoring system of claim 1, wherein the system further
comprises a data storage circuit structured for storing at
least one of component specifications and anticipated component state
information and buffering the output of at least one of the at
least two multiplexors and associated data corresponding to the logic control
of the at least one of the at least two multiplexors for
a predetermined length of time.
[0462] 13. The monitoring system of claim 9, wherein the data analysis circuit
comprises at least one of a peak detection circuit,
a phase detection circuit, a bandpass filter circuit, a frequency
transformation circuit, a frequency analysis circuit, a phase lock loop
circuit, a torsional analysis circuit, and a bearing analysis circuit.
[0463] 14. The monitoring system of claim 11, wherein the at least one
operation further comprises storing additional data in the
data storage circuit.
[0464] 15. The monitoring system of claim 9, wherein the at least one
operation comprises at least one of enabling or disabling
one or more portions of the multiplexer circuit.
[0465] 16. The monitoring system of claim 9, wherein the at least one
operation comprises causing the multiplexor control circuit
to alter the logical control of the MUX and the correspondence of MUX input
and detected values.
[0466] 17. The monitoring system of claim 9, wherein the control of the
correspondence of the multiplexor input and the detected
values further comprises controlling the connection of the output of a first
multiplexor to an input of a second multiplexor.
[0467] 18. The monitoring system of claim 9, wherein the control of the
correspondence of the multiplexor input and the detected
values further comprises powering down at least a portion of one of the at
least two multiplexors.
[0468] 19. A system for data collection in an industrial environment, the
system comprising:
a monitoring device comprising:
a data acquisition circuit structured to interpret a plurality of a data
acquisition circuit structured to interpret a plurality of detection
values, each of the plurality of detection values corresponding to input
received from at least one of a plurality of input sensors;
at least two multiplexors (MUX), each having inputs corresponding to a subset
of the detection values;
a MUX control circuit structured to interpret a subset of the plurality of
detection values and provide the logical control of the at
least two MUX and control of the correspondence of MUX input and detected
values as a result, wherein the logic control of the
MUX comprises adaptive scheduling of the select lines;
a data analysis circuit structured to receive an output from at least one of
the at least two MUX and data corresponding to the logic
control of the MUX resulting in a component health status;
a communication circuit structured to communicate the output of the MUX and
the adaptive control schedule to a remote server;
and
a monitoring application on the remote server structured to:
receive the stream of MUX output and the adaptive control schedule;
analyze the stream of received MUX output; and
recommend an action.
[0469] 20. A system for data collection in an industrial environment, the
system comprising:
a plurality of monitoring devices comprising:
a data acquisition circuit structured to interpret a plurality of a data
acquisition circuit structured to interpret a plurality of detection
values, each of the plurality of detection values corresponding to input
received from at least one of a plurality of input sensors;
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at least two multiplexors (MUX), each having inputs corresponding to a subset
of the detection values;
a MUX control circuit structured to interpret a subset of the plurality of
detection values and provide the logical control of the at
least two MUX and control of the correspondence of MUX input and detected
values as a result, wherein the logic control of the
MUX comprises adaptive scheduling of the select lines;
a data analysis circuit structured to receive a data stream from at least one
of the at least two MUX and data corresponding to the
logic control of the MUX resulting in a component health status;
a communication circuit structured to communicate the output of the MUX and
the adaptive control schedule to a remote server;
and
a monitoring application on the remote server structured to:
receive the data stream of MUX output and the adaptive control schedule;
analyze the data stream of received MUX output; and
recommend an action.
[0470] 21. A system for data collection in an industrial environment, the
system comprising a plurality of monitoring devices,
each monitoring device comprising:
a data acquisition circuit structured to interpret a plurality of detection
values, each of the plurality of detection values corresponding
to input received from at least one of a plurality of input sensors;
at least one multiplexors (MUX) having inputs corresponding to a subset of the
detection values and each providing a data stream
as output;
a MUX control circuit structured to interpret a subset of the plurality of
detection values and provide the logical control of the at
least one MUX and control of the correspondence of MUX input and detected
values as a result, wherein the logic control of the
MUX comprises adaptive scheduling of the select lines;
a data analysis circuit structured to receive the data stream from at least
one of the at least two MUX and data corresponding to the
logic control of the MUX resulting in a component health status;
a communication circuit structured to communicate the output of the MUX and
the adaptive control schedule to an intermediate
computer;
a processor on the intermediate computer comprising an operating system, the
processor structured to access a data storage circuit
on the intermediate computer and communicate the output of the MUX and the
adaptive control schedule to a remote server; and
a monitoring application on the remote server structured to:
receive the stream of MUX output and the adaptive control schedule;
analyze the stream of received MUX output; and
recommend an action.
[0471] 22. A system for data collection comprising a plurality of monitoring
systems for data collection from a piece of equipment
in an industrial environment, each monitoring system comprising:
a data acquisition circuit structured to interpret a plurality of detection
values, each of the plurality of detection values corresponding
to input received from at least one of a plurality of input sensors;
at least two multiplexors (MUX), each having inputs corresponding to a subset
of the detection values;
a MUX control circuit structured to interpret a subset of the plurality of
detection values and provide the logical control of the at
least two MUX and control of the correspondence of MUX input and detected
values as a result, wherein the logic control of the
MUX comprises adaptive scheduling of the select lines;
a data analysis circuit structured to receive an output from at least one of
the at least two MUX and data corresponding to the logic
control of the MUX resulting in a component health status;
a communication circuit structured to communicate the output of the MUX and
the adaptive control schedule to a remote server;
and
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a monitoring application on the remote server structured to:
receive, for at least two of the plurality of the monitoring devices, the data
stream from at least one of the MUX and the adaptive
control schedule;
jointly analyze the data streams received from at least two monitoring
devices; and
recommend an action.
[0472] 23. A testing system, wherein the testing system is in communication
with a plurality of analog and digital input sensors,
the monitoring device comprising:
a data acquisition circuit structured to interpret a plurality of detection
values, each of the plurality of detection values corresponding
to at least one of the input sensors;
a multiplexor (MUX) having inputs corresponding to a subset of the detection
values;
a MUX control circuit structured to interpret a subset of the plurality of
detection values and provide the logical control of the MUX
and control of the correspondence of MUX input and detected values as a
result, wherein the logic control of the MUX comprises
adaptive scheduling of the select lines; and
a user interface enabled to accept scheduling input for select lines and
display output of MUX and select line data.
[0473] In embodiments, information about the health or other status or state
information of or regarding a component or piece of
industrial equipment may be obtained by looking at both the amplitude and
phase or timing of data signals relative to related data
signals, timers, reference signals or data measurements. An embodiment of a
data monitoring device 8500 is shown in Figure 51
and may include a plurality of sensors 8506 communicatively coupled to a
controller 8502. The controller 8502 may include a data
acquisition circuit 8504, a signal evaluation circuit 8508 and a response
circuit 8510. The plurality of sensors 8506 may be wired to
ports on the data acquisition circuit 8504 or wirelessly in communication with
the data acquisition circuit 8504. The plurality of
sensors 8506 may be wirelessly connected to the data acquisition circuit 8504.
The data acquisition circuit 8504 may be able to
access detection values corresponding to the output of at least one of the
plurality of sensors 8506 where the sensors 8506 may be
capturing data on different operational aspects of a piece of equipment or an
operating component.
[0474] The selection of the plurality of sensors 8506 for a data monitoring
device 8500 designed for a specific component or piece
of equipment may depend on a variety of considerations such as accessibility
for installing new sensors, incorporation of sensors in
the initial design, anticipated operational and failure conditions,
reliability of the sensors, and the like. The impact of failure may
drive the extent to which a component or piece of equipment is monitored with
more sensors and/or higher capability sensors being
dedicated to systems where unexpected or undetected failure would be costly or
have severe consequences.
[0475] Depending on the type of equipment, the component being measured, the
environment in which the equipment is operating
and the like, sensors 8506 may comprise one or more of, without limitation, a
vibration sensor, a thermometer, a hygrometer, a
voltage sensor, a current sensor, an accelerometer, a velocity detector, a
light or electromagnetic sensor (e.g., determining
temperature, composition and/or spectral analysis, and/or object position or
movement), an image sensor, a structured light sensor,
a laser-based image sensor, an acoustic wave sensor, a displacement sensor, a
turbidity meter, a viscosity meter, a load sensor, a tri-
axial sensor, an accelerometer, a tachometer, a fluid pressure meter, an air
flow meter, a horsepower meter, a flow rate meter, a fluid
particle detector, an acoustical sensor, a pH sensor, and the like, including,
without limitation, any of the sensors described
throughout this disclosure and the documents incorporated by reference.
[0476] The sensors 8506 may provide a stream of data over time that has a
phase component, such as relating to acceleration or
vibration, allowing for the evaluation of phase or frequency analysis of
different operational aspects of a piece of equipment or an
operating component. The sensors 8506 may provide a stream of data that is not
conventionally phase-based, such as temperature,
humidity, load, and the like. The sensors 8506 may provide a continuous or
near continuous stream of data over time, periodic
readings, event-driven readings, and/or readings according to a selected
interval or schedule.
[0477] In embodiments, as illustrated in Figure 51, the sensors 8506 may be
part of the data monitoring device 8500, referred to
herein in some cases as a data collector, which in some rnQPQ ninv rnninriQe i
ninhile nr oortable data collector. In embodiments, as
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illustrated in Figures 52 and 53, sensors 8518, either new or previously
attached to or integrated into the equipment or component,
may be opportunistically connected to or accessed by a monitoring device 8512.
The sensors 8518 may be directly connected to
input ports 8520 on the data acquisition circuit 8516 of a controller 8514 or
may be accessed by the data acquisition circuit 8516
wirelessly, such as by a reader, interrogator, or other wireless connection,
such as over a short-distance wireless protocol. In
embodiments, a data acquisition circuit 8516 may access detection values
corresponding to the sensors 8518 wirelessly or via a
separate source or some combination of these methods. In embodiments, the data
acquisition circuit 8504 may include a wireless
communications circuit 8522 able to wirelessly receive data opportunistically
from sensors 8518 in the vicinity and route the data
to the input ports 8520 on the data acquisition circuit 8516.
[0478] In an embodiment as illustrated in Figures 54 and 55, the signal
evaluation circuit 8538 may then process the detection
values to obtain information about the component or piece of equipment being
monitored. Information extracted by the signal
evaluation circuit 8538 may comprise rotational speed, vibrational data
including amplitudes, frequencies, phase, and/or acoustical
data, and/or non-phase sensor data such as temperature, humidity, image data,
and the like.
[0479] The signal evaluation circuit 8538 may include one or more components
such as a phase detection circuit 8528 to determine
a phase difference between two time-based signals, a phase lock loop circuit
8530 to adjust the relative phase of a signal such that
it is aligned with a second signal, timer or reference signal, and/or a band
pass filter circuit 8532 which may be used to separate out
signals occurring at different frequencies. An example band pass filter
circuit 8532 includes any filtering operations understood in
the art, including at least a low-pass filter, a high-pass filter, and/or a
band pass filter ¨ for example to exclude or reduce frequencies
that are not of interest for a particular determination, and/or to enhance the
signal for frequencies of interest. Additionally, or
alternatively, a band pass filter circuit 8532 includes one or more notch
filters or other filtering mechanism to narrow ranges of
frequencies (e.g., frequencies from a known source of noise). This may be used
to filter out dominant frequency signals such as the
overall rotation, and may help enable the evaluation of low amplitude signals
at frequencies associated with torsion, bearing failure
and the like.
[0480] In embodiments, understanding the relative differences may be enabled
by a phase detection circuit 8528 to determine a
phase difference between two signals. It may be of value to understand a
relative phase offset, if any, between signals such as when
a periodic vibration occurs relative to a relative rotation of a piece of
equipment. In embodiments, there may be value in
understanding where in a cycle shaft vibrations occur relative to a motor
control input to better balance the control of the motor.
This may be particularly true for systems and components that are operating at
relative slow RPMs. Understanding of the phase
difference between two signals or between those signals and a timer may enable
establishing a relationship between a signal value
and where it occurs in a process or rotation. Understanding relative phase
differences may help in evaluating the relationship between
different components of a system such as in the creation of a vibrational
model for an Operational Deflection Shape (ODS).
[0481] In embodiments, a phase lock loop circuit 8530 may adjust one or more
signals so that their phases are aligned, either to
one another, to a time signal or to a reference signal. Once a signal is phase
locked it may be possible to extract a low amplitude
signal that is on top of a carrier signal, such as a small amplitude vibration
due to a bearing defect which may be thought of as riding
on top of a larger rotational vibration, such as due to the turning of a shaft
that is borne by the bearing. In some embodiments, the
phase difference may be determined between timing indicated by a timer that is
on-board the monitoring device and the timing of
streamed detection values corresponding to a sensor. In some embodiments, the
phase difference may be determined between two
sets of detection values. The two sets of detection values may correspond to
differences in location between two sensors, different
types of sensors, sensors of different resolution and the like.
[0482] The signal evaluation circuit 8538 may perform frequency analysis using
techniques such as a digital Fast Fourier transform
(FFT), Laplace transform, Z-transform, wavelet transform, other frequency
domain transform, or other digital or analog signal
analysis techniques, including, without limitation, complex analysis,
including complex phase evolution analysis. An overall
rotational speed or tachometer may be derived from data from sensors such as
rotational velocity meters, accelerometers,
displacement meters and the like. Additional frequencies of interest may also
be identified. These may include frequencies near the
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overall rotational speed as well as frequencies higher than that of the
rotational speed. These may include frequencies that are
nonsynchronous with an overall rotational speed. Signals observed at
frequencies that are multiples of the rotational speed may be
due to bearing induced vibrations or other behaviors or situations involving
bearings. In some instances, these frequencies may be
in the range of one times the rotational speed, two times the rotational
speed, three times the rotational speed, and the like, up to
3.15 to 15 times the rotational speed, or higher. In some embodiments, the
signal evaluation circuit 8538 may select RC components
for a band pass filter circuit 8532 based on overall rotational speed to
create a band pass filter circuit 8532 to remove signals at
expected frequencies such as the overall rotational speed, to facilitate
identification of small amplitude signals at other frequencies.
In embodiments, variable components may be selected, such that adjustments may
be made in keeping with changes in the rotational
speed, so that the band pass filter may be a variable band pass filter. This
may occur under control of automatically self-adjusting
circuit elements, or under control of a processor, including automated control
based on a model of the circuit behavior, where a
rotational speed indicator or other data is provided as a basis for control.
[0483] In embodiments, rather than performing frequency analysis, the signal
evaluation circuit 8538 may utilize the time-based
detection values to perform transitory signal analysis. These may include
identifying abrupt changes in signal amplitude including
changes where the change in amplitude exceeds a predetermined value or exists
for a certain duration. In embodiments, the time-
based sensor data may be aligned with a timer or reference signal allowing the
time-based sensor data to be aligned with, for
example, a time or location in a cycle. Additional processing to look at
frequency changes over time may include the use of Short-
Time Fourier Transforms (STFT) or a wavelet transform.
[0484] In embodiments, frequency-based techniques and time-based techniques
may be combined, such as using time-based
techniques to determine discrete time periods during which given operational
modes or states are occurring and using frequency-
based techniques to determine behavior within one or more of the discrete time
periods.
[0485] In embodiments, the signal evaluation circuit may utilize demodulation
techniques for signals obtained from equipment
running at slow speeds such as paper and pulp machines, mining equipment, and
the like. A signal evaluation circuit employing a
demodulation technique may comprise a band-pass filter circuit, a rectifier
circuit, and/or a low pass circuit prior to transforming
the data to the frequency domain.
[0486] The response circuit 8510 may further comprise evaluating the results
of the signal evaluation circuit 8538 and, based on
certain criteria, initiating an action. Criteria may include a predetermined
maximum or minimum value for a detection value from a
specific sensor, a value of a sensor's corresponding detection value over
time, a change in value, a rate of change in value, and/or
an accumulated value (e.g., a time spent above/below a threshold value, a
weighted time spent above/below one or more threshold
values, and/or an area of the detected value above/below one or more threshold
values). The criteria may include a sensor's detection
values at certain frequencies or phases where the frequencies or phases may be
based on the equipment geometry, equipment control
schemes, system input, historical data, current operating conditions, and/or
an anticipated response. The criteria may comprise
combinations of data from different sensors such as relative values, relative
changes in value, relative rates of change in value,
relative values over time, and the like. The relative criteria may change with
other data or information such as process stage, type
of product being processed, type of equipment, ambient temperature and
humidity, external vibrations from other equipment, and
the like. The relative criteria may include level of synchronicity with an
overall rotational speed, such as to differentiate between
vibration induced by bearings and vibrations resulting from the equipment
design. In embodiments, the criteria may be reflected in
one or more calculated statistics or metrics (including ones generated by
further calculations on multiple criteria or statistics), which
in turn may be used for processing (such as on board a data collector or by an
external system), such as to be provided as an input
to one or more of the machine learning capabilities described in this
disclosure, to a control system (which may be on board a data
collector or remote, such as to control selection of data inputs, multiplexing
of sensor data, storage, or the like), or as a data element
that is an input to another system, such as a data stream or data package that
may be available to a data marketplace, a SCADA
system, a remote control system, a maintenance system, an analytic system, or
other system.
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[0487] In an illustrative and non-limiting example, an alert may be issued if
the vibrational amplitude and/or frequency exceeds
a predetermined maximum value, if there is a change or rate of change that
exceeds a predetermined acceptable range, and/or if an
accumulated value based on vibrational amplitude and/or frequency exceeds a
threshold. Certain embodiments are described herein
as detected values exceeding thresholds or predetermined values, but detected
values may also fall below thresholds or
predetermined values ¨ for example where an amount of change in the detected
value is expected to occur, but detected values
indicate that the change may not have occurred. For example, and without
limitation, vibrational data may indicate system agitation
levels, properly operating equipment, or the like, and vibrational data below
amplitude and/or frequency thresholds may be an
indication of a process that is not operating according to expectations.
Except where the context clearly indicates otherwise, any
description herein describing a determination of a value above a threshold
and/or exceeding a predetermined or expected value is
understood to include determination of a value below a threshold and/or
falling below a predetermined or expected value.
[0488] The predetermined acceptable range may be based on anticipated system
response or vibration based on the equipment
geometry and control scheme such as number of bearings, relative rotational
speed, influx of power to the system at a certain
frequency, and the like. The predetermined acceptable range may also be based
on long term analysis of detection values across a
plurality of similar equipment and components and correlation of data with
equipment failure. Based on vibration phase information,
a physical location of a problem may be identified. Based on the vibration
phase information system design flaws, off-nominal
operation, and/or component or process failures may be identified. In some
embodiments, an alert may be issued based on changes
or rates of change in the data over time such as increasing amplitude or
shifts in the frequencies or phases at which a vibration
occurs. In some embodiments, an alert may be issued based on accumulated
values such as time spent over a threshold, weighted
time spent over one or more thresholds, and/or an area of a curve of the
detected value over one or more thresholds. In embodiments,
an alert may be issued based on a combination of data from different sensors
such as relative changes in value, or relative rates of
change in amplitude, frequency of phase in addition to values of non-phase
sensors such as temperature, humidity and the like. For
example, an increase in temperature and energy at certain frequencies may
indicate a hot bearing that is starting to fail. In
embodiments, the relative criteria for an alarm may change with other data or
information such as process stage, type of product
being processed on equipment, ambient temperature and humidity, external
vibrations from other equipment and the like.
[0489] In embodiments, response circuit 8510 may cause the data acquisition
circuit 8504 to enable or disable the processing of
detection values corresponding to certain sensors based on the some of the
criteria discussed above. This may include switching to
sensors having different response rates, sensitivity, ranges, and the like;
accessing new sensors or types of sensors, and the like.
Switching may be undertaken based on a model, a set of rules, or the like. In
embodiments, switching may be under control of a
machine learning system, such that switching is controlled based on one or
more metrics of success, combined with input data, over
a set of trials, which may occur under supervision of a human supervisor or
under control of an automated system. Switching may
involve switching from one input port to another (such as to switch from one
sensor to another). Switching may involve altering the
multiplexing of data, such as combining different streams under different
circumstances. Switching may involve activating a system
to obtain additional data, such as moving a mobile system (such as a robotic
or drone system), to a location where different or
additional data is available (such as positioning an image sensor for a
different view or positioning a sonar sensor for a different
direction of collection) or to a location where different sensors can be
accessed (such as moving a collector to connect up to a sensor
that is disposed at a location in an environment by a wired or wireless
connection). The response circuit 8510 may make
recommendations for the replacement of certain sensors in the future with
sensors having different response rates, sensitivity, ranges,
and the like. The response circuit 8510 may recommend design alterations for
future embodiments of the component, the piece of
equipment, the operating conditions, the process, and the like.
[0490] In embodiments, the response circuit 8510 may recommend maintenance at
an upcoming process stop or initiate a
maintenance call. The response circuit 8510 may recommend changes in process
or operating parameters to remotely balance the
piece of equipment. In embodiments, the response circuit 8510 may implement or
recommend process changes ¨ for example to
lower the utilization of a component that is near a maintenance interval,
operatinn off-nominally, or failed for purpose but still at
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least partially operational, to change the operating speed of a component
(such as to put it in a lower-demand mode), to initiate
amelioration of an issue (such as to signal for additional lubrication of a
roller bearing set, or to signal for an alignment process for
a system that is out of balance), and the like.
[0491] In embodiments, as shown in Figure 56, the data monitoring device 8540
may further comprise a data storage circuit 8542,
memory, and the like. The signal evaluation circuit 8538 may periodically
store certain detection values to enable the tracking of
component performance over time.
[0492] In embodiments, based on relevant operating conditions and/or failure
modes which may occur in as sensor values approach
one or more criteria, the signal evaluation circuit 8538 may store data in the
data storage circuit 8542 based on the fit of data relative
to one or more criteria, such as those described throughout this disclosure.
Based on one sensor input meeting or approaching
specified criteria or range, the signal evaluation circuit 8538 may store
additional data such as RPMs, component loads,
temperatures, pressures, vibrations or other sensor data of the types
described throughout this disclosure. The signal evaluation
circuit 8508 may store data at a higher data rate for greater granularity in
future processing, the ability to reprocess at different
sampling rates, and/or to enable diagnosing or post-processing of system
information where operational data of interest is flagged,
and the like.
[0493] In embodiments as shown in Figure 57, a data monitoring system 8546 may
comprise at least one data monitoring device
8548. The at least one data monitoring device 8548 comprising sensors 8506, a
controller 8550 comprising a data acquisition circuit
8504, a signal evaluation circuit 8538, a data storage circuit 8542, and a
communications circuit 8552 to allow data and analysis to
be transmitted to a monitoring application 8556 on a remote server 8554. The
signal evaluation circuit 8538 may comprise at least
one of a phase detection circuit 8528, a phase lock loop circuit 8530, and/or
a band pass circuit 8532. The signal evaluation circuit
8538 may periodically share data with the communication circuit 8552 for
transmittal to the remote server 8554 to enable the
tracking of component and equipment performance over time and under varying
conditions by a monitoring application 8556.
Because relevant operating conditions and/or failure modes may occur as sensor
values approach one or more criteria, the signal
evaluation circuit 8538 may share data with the communication circuit 8552 for
transmittal to the remote server 8554 based on the
fit of data relative to one or more criteria. Based on one sensor input
meeting or approaching specified criteria or range, the signal
evaluation circuit 8538 may share additional data such as RPMs, component
loads, temperatures, pressures, vibrations, and the like
for transmittal. The signal evaluation circuit 8538 may share data at a higher
data rate for transmittal to enable greater granularity
in processing on the remote server.
[0494] In embodiments as illustrated in Figure 58, a data collection system
may have a plurality of monitoring devices 8548
collecting data on multiple components in a single piece of equipment,
collecting data on the same component across a plurality of
pieces of equipment (both the same and different types of equipment) in the
same facility, as well as collecting data from monitoring
devices in multiple facilities. A monitoring application on a remote server
may receive and store the data coming from a plurality
of the various monitoring devices. The monitoring application may then select
subsets of data which may be jointly analyzed.
Subsets of monitoring data may be selected based on data from a single type of
component or data from a single type of equipment
in which the component is operating. Monitoring data may be selected or
grouped based on common operating conditions such as
size of load, operational condition (e.g. intermittent, continuous), operating
speed or tachometer, common ambient environmental
conditions such as humidity, temperature, air or fluid particulate, and the
like. Monitoring data may be selected based on the effects
of other nearby equipment, such as nearby machines rotating at similar
frequencies, nearby equipment producing electromagnetic
fields, nearby equipment producing heat, nearby equipment inducing movement or
vibration, nearby equipment emitting vapors,
chemicals or particulates, or other potentially interfering or intervening
effects.
[0495] The monitoring application may then analyze the selected data set. For
example, data from a single component may be
analyzed over different time periods such as one operating cycle, several
operating cycles, a month, a year, or the like. Data from
multiple components of the same type may also be analyzed over different time
periods. Trends in the data such as changes in
frequency or amplitude may be correlated with failure and maintenance records
associated with the same component or piece of
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equipment. Trends in the data such as changing rates of change associated with
start-up or different points in the process may be
identified. Additional data may be introduced into the analysis such as output
product quality, output quantity (such as per unit of
time), indicated success or failure of a process, and the like. Correlation of
trends and values for different types of data may be
analyzed to identify those parameters whose short-term analysis might provide
the best prediction regarding expected performance.
This information may be transmitted back to the monitoring device to update
types of data collected and analyzed locally or to
influence the design of future monitoring devices.
[0496] In an illustrative and non-limiting example, the monitoring device may
be used to collect and process sensor data to measure
mechanical torque. The monitoring device may be in communication with or
include a high resolution, high speed vibration sensor
to collect data over an extended period of time, enough to measure multiple
cycles of rotation. For gear driven equipment, the
sampling resolution should be such that the number of samples taken per cycle
is at least equal to the number of gear teeth driving
the component. It will be understood that a lower sampling resolution may also
be utilized, which may result in a lower confidence
determination and/or taking data over a longer period of time to develop
sufficient statistical confidence. This data may then be used
in the generation of a phase reference (relative probe) or tachometer signal
for a piece of equipment. This phase reference may be
used to align phase data such as vibrational data or acceleration data from
multiple sensors located at different positions on a
component or on different components within a system. This information may
facilitate the determination of torque for different
components or the generation of an Operational Deflection Shape (ODS),
indicating the extent of mechanical deflection of one or
more components during an operational mode, which in turn may be used to
measure mechanical torque in the component.
[0497] The higher resolution data stream may provide additional data for the
detection of transitory signals in low speed operations.
The identification of transitory signals may enable the identification of
defects in a piece of equipment or component
[0498] In an illustrative and non-limiting example, the monitoring device may
be used to identify mechanical jitter for use in
failure prediction models. The monitoring device may begin acquiring data when
the piece of equipment starts up through ramping
up to operating speed and then during operation. Once at operating speed, it
is anticipated that the torsional jitter should be minimal
and changes in torsion during this phase may be indicative of cracks, bearing
faults and the like. Additionally, known torsions may
be removed from the signal to facilitate in the identification of
unanticipated torsions resulting from system design flaws or
component wear. Having phase information associated with the data collected at
operating speed may facilitate identification of a
location of vibration and potential component wear. Relative phase information
for a plurality of sensors located throughout a
machine may facilitate the evaluation of torsion as it is propagated through a
piece of equipment.
[0499] 1. A system for data collection in an industrial environment, the
system comprising:
a data acquisition circuit structured to interpret a plurality of detection
values from a plurality of input sensors communicatively
coupled to the data acquisition circuit, each of the plurality of detection
values corresponding to at least one of the input sensors;
a signal evaluation circuit structured to obtain at least one of a vibration
amplitude, a vibration frequency and a vibration phase
location corresponding to at least one of the input sensors in response to the
plurality of detection values; and
a response circuit structured to perform at least one operation in response to
at the at least one of the vibration amplitude, the
vibration frequency and the vibration phase location.
[0500] 2. The system of claim 1, wherein the signal evaluation circuit
comprises a phase detection circuit.
[0501] 3. The system of claim 2, wherein the signal evaluation circuit further
comprises at least one of a phase lock loop circuit
and a band pass filter.
[0502] 4. The system of claim 3, wherein the plurality of input sensors
includes at least two input sensors providing phase
information and at least one input sensor providing non-phase sensor
information, the signal evaluation circuit further structured to
align the phase information provided by the at least two of the input sensors.
[0503] 5. The system of claim 1, wherein the at least one operation is further
in response to at least one of: a change in magnitude
of the vibration amplitude; a change in frequency or phase of vibration; a
rate of change in at least one of vibration amplitude,
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vibration frequency and vibration phase; a relative change in value between at
least two of vibration amplitude, vibration frequency
and vibration phase; and a relative rate of change between at least two of
vibration amplitude, vibration frequency and vibration
phase.
[0504] 6. The system of claim 1, further comprising an alert circuit, wherein
the at least one operation comprises providing an
alert.
[0505] 7. The system of claim 6, wherein the alert may be one of haptic,
audible and visual.
[0506] 8. The system of claim 1, further comprising a data storage circuit,
wherein at least one or the vibration amplitude, vibration
frequency and vibration phase is stored periodically to create a vibration
history.
[0507] 9. The system of claim 8 wherein the at least one operation comprises
storing additional data in the data storage circuit.
[0508] 10. The system of claim 9, wherein the storing additional data in the
data storage circuit is further in response to at least
one of: a change in magnitude of the vibration amplitude; a change in
frequency or phase of vibration; a rate of change in the
vibration amplitude, frequency or phase; a relative change in value between at
least two of vibration amplitude, frequency and phase;
and a relative rate of change between at least two of vibration amplitude,
frequency and phase.
[0509] 11. The system of claim 1, further comprising at least one a
multiplexing (MUX) circuit whereby alternative combinations
of detection values may be selected based on at least one of user input, a
detected state and a selected operating parameter for a
machine, each of the plurality of detection values corresponding to at least
one of the input sensors.
[0510] 12. The system of claim 11, wherein the at least one operation
comprises enabling or disabling the connection of one or
more portions of the multiplexing circuit.
[0511] 13. The system of claim 11, further comprising a MUX control circuit
structured to interpret a subset of the plurality of
detection values and provide the logical control of the MUX and the
correspondence of MUX input and detected values as a result,
wherein the logic control of the MUX comprises adaptive scheduling of the
select lines;
[0512] 14. A method of monitoring a component, the method comprising:
receiving time-based data from at least one sensor;
phase-locking the received data with a reference signal;
transforming the received time-based data to frequency data;
filtering the frequency data to remove tachometer frequencies;
identifying low amplitude signals occurring at high frequencies; and
activating an alarm if a low amplitude signal exceeds a threshold.
[0513] 15. A system for data collection, processing, and utilization of
signals in an industrial environment comprising:
a plurality of monitoring devices, each monitoring device comprising:
a data acquisition circuit structured to interpret a plurality of detection
values from a plurality of input sensors communicatively
coupled to the data acquisition circuit, each of the plurality of detection
values corresponding to at least one of the input sensors;
a signal evaluation circuit structured to obtain at least one of vibration
amplitude, vibration frequency and a vibration phase location
corresponding to at least one of the input sensors in response to the
corresponding at least one of the plurality of detection values;
a data storage facility for storing a subset of the plurality of detection
values;
a communication circuit structured to communicate at least one selected
detection value to a remote server; and
a monitoring application on the remote server structured to:
receive the at least one selected detection value;
jointly analyze a subset of the detection values received from the plurality
of monitoring devices; and
recommend an action.
[0514] 16. The system of claim 15, wherein, for each monitoring device, the
plurality of input sensors includes at least one input
sensor providing phase information and at least one input sensor providing non-
phase input sensor information and wherein joint
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analysis comprises using the phase information from the plurality of
monitoring devices to align the information from the plurality
of monitoring devices.
[0515] 17. The system of claim 15 wherein the subset of detection values is
selected based on data associated with a detection
value comprising at least one: common type of component, common type of
equipment, and common operating conditions.
[0516] 18. The system of claim 17, the system further structured to subset
detection values based on one of anticipated life of a
component associated with detection values, type of the equipment associated
with detection values, and operational conditions
under which detection values were measured.
[0517] 19. The system of claim 15, wherein the analysis of the subset of
detection values comprises feeding a neural net with the
subset of detection values and supplemental information to learn to recognize
various operating states, health states, life expectancies
and fault states utilizing deep learning techniques.
[0518] 20. The system of claim 17, wherein the supplemental information
comprises one of component specification, component
performance, equipment specification, equipment performance, maintenance
records, repair records and an anticipated state model.
[0519] 21. A monitoring system for data collection in an industrial
environment, the monitoring system comprising:
a data acquisition circuit structured to interpret a plurality of detection
values from a plurality of input sensors communicatively
coupled to the data acquisition circuit, each of the plurality of detection
values corresponding to at least one of the input sensors;
a signal evaluation circuit structured to obtain at least one of vibration
amplitude, vibration frequency and vibration phase location
corresponding to at least one of the input sensors in response to the
corresponding at least one of a plurality of detection values;
a multiplexing circuit whereby alternative combinations of the detection
values may be selected based on at least one of user input,
a detected state and a selected operating parameter for a machine, each of the
plurality of detection values corresponding to at least
one of the input sensors; and
a response circuit structured to perform at least one operation in response to
at the at least one of the vibration amplitude, vibration
frequency and vibration phase location.
[0520] 22. A monitoring system for data collection in a piece of equipment,
the monitoring system comprising:
a data acquisition circuit structured to interpret a plurality of detection
values from a plurality of input sensors communicatively
coupled to the data acquisition circuit, each of the plurality of detection
values corresponding to at least one of the input sensors;
a timer circuit structured to generate a timing signal based on a first
detected value of the plurality of detection values;
a signal evaluation circuit structured to obtain at least one of vibration
amplitude, vibration frequency and vibration phase location
corresponding to a second detected value comprising:
a phase detection circuit structured to determine a relative phase difference
between a second detection value of the plurality of
detection values and the timing signal; and
a response circuit structured to perform at least one operation in response to
at the at least one of the vibration amplitude, vibration
frequency and vibration phase location.
[0521] 23. A system for bearing analysis in an industrial environment, the
system comprising:
a data acquisition circuit structured to interpret a plurality of detection
values from a plurality of input sensors communicatively
coupled to the data acquisition circuit, each of the plurality of detection
values corresponding to at least one of the input sensors;
a data storage for storing specifications and anticipated state information
for a plurality of bearing types and buffering the plurality
of detection values for a predetermined length of time;
a timer circuit structured to generate a timing signal based on a first
detected value of the plurality of detection values;
a bearing analysis circuit structured to analyze buffered detection values
relative to specifications and anticipated state information
resulting in a life prediction comprising:
a phase detection circuit structured to determine a relative phase difference
between a second detection value of the plurality of
detection values and the timing signal; and
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a signal evaluation circuit structured to obtain at least one of vibration
amplitude, vibration frequency and vibration phase location
corresponding to a second detected value: and
a response circuit structured to perform at least one operation in response to
at the at least one of the vibration amplitude, vibration
frequency and vibration phase location.
[0522] 24. A motor monitoring system, the motor monitoring system comprising:
a data acquisition circuit structured to interpret a plurality of detection
values from a plurality of input sensors communicatively
coupled to the data acquisition circuit, each of the plurality of detection
values corresponding to at least one of the input sensors;
a data storage circuit structured to store specifications, system geometry,
and anticipated state information for the motor and motor
components, store historical motor performance and buffer the plurality of
detection values for a predetermined length of time;
a timer circuit structured to generate a timing signal based on a first
detected value of the plurality of detection values;
a motor analysis circuit structured to analyze buffered detection values
relative to specifications and anticipated state information
resulting in a motor performance parameter comprising:
a phase detection circuit structured to determine a relative phase difference
between a second detection value of the plurality of
detection values and the timing signal; and
a signal evaluation circuit structured to obtain at least one of vibration
amplitude, vibration frequency and vibration phase location
corresponding to a second detected value and analyze the at least one of
vibration amplitude, vibration frequency and vibration
phase location relative to buffered detection values, specifications and
anticipated state information resulting in a motor performance
parameter; and
a response circuit structured to perform at least one operation in response to
at the at least one of vibration amplitude, vibration
frequency and vibration phase location and motor performance parameter.
[0523] 25. A system for estimating a vehicle steering system performance
parameter, the device comprising:
a data acquisition circuit structured to interpret a plurality of detection
values from a plurality of input sensors communicatively
coupled to the data acquisition circuit, each of the plurality of detection
values corresponding to at least one of the input sensors;
a data storage circuit structured to store specifications, system geometry,
and anticipated state information for the vehicle steering
system, the rack, the pinion, and the steering column, store historical
steering system performance and buffer the plurality of
detection values for a predetermined length of time;
a timer circuit structured to generate a timing signal based on a first
detected value of the plurality of detection values;
a steering system analysis circuit structured to analyze buffered detection
values relative to specifications and anticipated state
information resulting in a steering system performance parameter comprising:
a phase detection circuit structured to determine a relative phase difference
between a second detection value of the plurality of
detection values and the timing signal; and
a signal evaluation circuit structured to obtain at least one of vibration
amplitude, vibration frequency and vibration phase location
corresponding to a second detected value and analyze the at least one of
vibration amplitude, vibration frequency and vibration
phase location relative to buffered detection values, specifications and
anticipated state information resulting in a steering system
performance parameter; and
a response circuit structured to perform at least one operation in response to
at the at least one of vibration amplitude, vibration
frequency and vibration phase location and the steering system performance
parameter.
[0524] 26. A system for estimating a pump performance parameter, the system
comprising:
a data acquisition circuit structured to interpret a plurality of detection
values from a plurality of input sensors communicatively
coupled to the data acquisition circuit, each of the plurality of detection
values corresponding to at least one of the input sensors;
a data storage circuit structured to store specifications, system geometry,
and anticipated state information for the pump and pump
components associated with the detection values, store historical pump
performance and buffer the plurality of detection values for
a predetermined length of time;
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a timer circuit structured to generate a timing signal based on a first
detected value of the plurality of detection values;
a pump analysis circuit structured to analyze buffered detection values
relative to specifications and anticipated state information
resulting in a pump performance parameter comprising:
a phase detection circuit structured to determine a relative phase difference
between a second detection value of the plurality of
detection values and the timing signal; and
a signal evaluation circuit structured to obtain at least one of vibration
amplitude, vibration frequency and vibration phase location
corresponding to a second detected value and analyze the at least one of
vibration amplitude, vibration frequency and vibration
phase location relative to buffered detection values, specifications and
anticipated state information resulting in a pump performance
parameter; and
a response circuit structured to perform at least one operation in response to
at the at least one of vibration amplitude, vibration
frequency and vibration phase location and the pump performance parameter.
[0525] 27. The system of claim 26, wherein the pump is a water pump in a car.
[0526] 28. The system of claim 26, wherein the pump is a mineral pump.
[0527] 29. A system for estimating a drill performance parameter for a
drilling machine, the system comprising:
a data acquisition circuit structured to interpret a plurality of detection
values from a plurality of input sensors communicatively
coupled to the data acquisition circuit, each of the plurality of detection
values corresponding to at least one of the input sensors;
a data storage circuit structured to store specifications, system geometry,
and anticipated state information for the drill and drill
components associated with the detection values, store historical drill
performance and buffer the plurality of detection values for a
predetermined length of time;
a timer circuit structured to generate a timing signal based on a first
detected value of the plurality of detection values;
a drill analysis circuit structured to analyze buffered detection values
relative to specifications and anticipated state information
resulting in a drill performance parameter comprising:
a phase detection circuit structured to determine a relative phase difference
between a second detection value of the plurality of
detection values and the timing signal; and
a signal evaluation circuit structured to obtain at least one of vibration
amplitude, vibration frequency and vibration phase location
corresponding to a second detected value and analyze the at least one of
vibration amplitude, vibration frequency and vibration
phase location relative to buffered detection values, specifications and
anticipated state information resulting in a drill performance
parameter; and
a response circuit structured to perform at least one operation in response to
at the at least one of vibration amplitude, vibration
frequency and vibration phase location and the drill performance parameter.
[0528] 30. The system of claim 29, wherein the drilling machine is one of an
oil drilling machine and a gas drilling machine.
[0529] 31. A system for estimating a conveyor health parameter, the system
comprising:
a data acquisition circuit structured to interpret a plurality of detection
values from a plurality of input sensors communicatively
coupled to the data acquisition circuit, each of the plurality of detection
values corresponding to at least one of the input sensors;
a data storage circuit structured to store specifications, system geometry,
and anticipated state information for a conveyor and
conveyor components associated with the detection values, store historical
conveyor performance and buffer the plurality of
detection values for a predetermined length of time;
a timer circuit structured to generate a timing signal based on a first
detected value of the plurality of detection values;
a conveyor analysis circuit structured to analyze buffered detection values
relative to specifications and anticipated state information
resulting in a conveyor performance parameter comprising:
a phase detection circuit structured to determine a relative phase difference
between a second detection value of the plurality of
detection values and the timing signal; and
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a signal evaluation circuit structured to obtain at least one of vibration
amplitude, vibration frequency and vibration phase location
corresponding to a second detected value and analyze the at least one of
vibration amplitude, vibration frequency and vibration
phase location relative to buffered detection values, specifications and
anticipated state information resulting in a conveyor
performance parameter; and
a response circuit structured to perform at least one operation in response to
at the at least one of vibration amplitude, vibration
frequency and vibration phase location and the conveyor performance parameter.
[0530] 32. A system for estimating an agitator health parameter, the system
comprising:
a data acquisition circuit structured to interpret a plurality of detection
values from a plurality of input sensors communicatively
coupled to the data acquisition circuit, each of the plurality of detection
values corresponding to at least one of the input sensors;
a data storage circuit structured to store specifications, system geometry,
and anticipated state information for an agitator and agitator
components associated with the detection values, store historical agitator
performance and buffer the plurality of detection values
for a predetermined length of time;
a timer circuit structured to generate a timing signal based on a first
detected value of the plurality of detection values;
an agitator analysis circuit structured to analyze buffered detection values
relative to specifications and anticipated state information
resulting in an agitator performance parameter comprising:
a phase detection circuit structured to determine a relative phase difference
between a second detection value of the plurality of
detection values and the timing signal; and
a signal evaluation circuit structured to obtain at least one of vibration
amplitude, vibration frequency and vibration phase location
corresponding to a second detected value and analyze the at least one of
vibration amplitude, vibration frequency and vibration
phase location relative to buffered detection values, specifications and
anticipated state information resulting in an agitator
performance parameter; and
a response circuit structured to perform at least one operation in response to
at the at least one of vibration amplitude, vibration
frequency and vibration phase location and the agitator performance parameter.
[0531] 33. The system of claim 32 where the agitator is one of a rotating tank
mixer, a large tank mixer, a portable tank mixers, a
tote tank mixer, a drum mixer, a mounted mixer and a propeller mixer.
[0532] 34. A system for estimating a compressor health parameter, the system
comprising:
a data acquisition circuit structured to interpret a plurality of detection
values from a plurality of input sensors communicatively
coupled to the data acquisition circuit, each of the plurality of detection
values corresponding to at least one of the input sensors;
a data storage circuit structured to store specifications, system geometry,
and anticipated state information for a compressor and
compressor components associated with the detection values, store historical
compressor performance and buffer the plurality of
detection values for a predetermined length of time;
a timer circuit structured to generate a timing signal based on a first
detected value of the plurality of detection values;
a compressor analysis circuit structured to analyze buffered detection values
relative to specifications and anticipated state
information resulting in a compressor performance parameter comprising:
a phase detection circuit structured to determine a relative phase difference
between a second detection value of the plurality of
detection values and the timing signal; and
a signal evaluation circuit structured to obtain at least one of vibration
amplitude, vibration frequency and vibration phase location
corresponding to a second detected value and analyze the at least one of
vibration amplitude, vibration frequency and vibration
phase location relative to buffered detection values, specifications and
anticipated state information resulting in a compressor
performance parameter; and
a response circuit structured to perform at least one operation in response to
at the at least one of vibration amplitude, vibration
frequency and vibration phase location and the compressor performance
parameter.
[0533] 35. A system for estimating an air conditioner health parameter, the
system comprising:
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a data acquisition circuit structured to interpret a plurality of detection
values from a plurality of input sensors communicatively
coupled to the data acquisition circuit, each of the plurality of detection
values corresponding to at least one of the input sensors;
a data storage circuit structured to store specifications, system geometry,
and anticipated state information for an air conditioner and
air conditioner components associated with the detection values, store
historical air conditioner performance and buffer the plurality
of detection values for a predetermined length of time;
a timer circuit structured to generate a timing signal based on a first
detected value of the plurality of detection values;
an air conditioner analysis circuit structured to analyze buffered detection
values relative to specifications and anticipated state
information resulting in an air conditioner performance parameter comprising:
a phase detection circuit structured to determine a relative phase difference
between a second detection value of the plurality of
detection values and the timing signal; and
a signal evaluation circuit structured to obtain at least one of vibration
amplitude, vibration frequency and vibration phase location
corresponding to a second detected value and analyze the at least one of
vibration amplitude, vibration frequency and vibration
phase location relative to buffered detection values, specifications and
anticipated state information resulting in an air conditioner
performance parameter; and
a response circuit structured to perform at least one operation in response to
at the at least one of vibration amplitude, vibration
frequency and vibration phase location and the air conditioner performance
parameter.
[0534] 36. A system for estimating a centrifuge health parameter, the system
comprising:
a data acquisition circuit structured to interpret a plurality of detection
values from a plurality of input sensors communicatively
coupled to the data acquisition circuit, each of the plurality of detection
values corresponding to at least one of the input sensors;
a data storage circuit structured to store specifications, system geometry,
and anticipated state information for a centrifuge and
centrifuge components associated with the detection values, store historical
centrifuge performance and buffer the plurality of
detection values for a predetermined length of time;
a timer circuit structured to generate a timing signal based on a first
detected value of the plurality of detection values;
a centrifuge analysis circuit structured to analyze buffered detection values
relative to specifications and anticipated state
information resulting in a centrifuge performance parameter comprising:
a phase detection circuit structured to determine a relative phase difference
between a second detection value of the plurality of
detection values and the timing signal; and
a signal evaluation circuit structured to obtain at least one of vibration
amplitude, vibration frequency and vibration phase location
corresponding to a second detected value and analyze the at least one of
vibration amplitude, vibration frequency and vibration
phase location relative to buffered detection values, specifications and
anticipated state information resulting in a centrifuge
performance parameter; and
a response circuit structured to perform at least one operation in response to
at the at least one of vibration amplitude, vibration
frequency and vibration phase location and the centrifuge performance
parameter.
[0535] In embodiments, information about the health of a component or piece of
industrial equipment may be obtained by
comparing the values of multiple signals at the same point in a process. This
may be accomplished by aligning a signal relative to
other related data signals, timers, or reference signals. An embodiment of a
data monitoring device 8700 is shown in Figure 59 and
may include a plurality of sensors 8706 communicatively coupled to a
controller 8702. The controller 8702 may include a data
acquisition circuit 8704, a signal evaluation circuit 8708, a data storage
circuit 8716 and an optional response circuit 8710. The
signal evaluation circuit 8708 may comprise a timer circuit 8714 and,
optionally, a phase detection circuit 8712.
[0536] The plurality of sensors 8706 may be wired to ports on the data
acquisition circuit 8704. The plurality of sensors 8706 may
be wirelessly connected to the data acquisition circuit 8704. The data
acquisition circuit 8704 may be able to access detection values
corresponding to the output of at least one of the plurality of sensors 8706
where the sensors 8706 may be capturing data on different
operational aspects of a piece of equipment or an operating component.
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[0537] The selection of the plurality of sensors 8706 for a data monitoring
device 8700 designed for a specific component or piece
of equipment may depend on a variety of considerations such as accessibility
for installing new sensors, incorporation of sensors in
the initial design, anticipated operational and failure conditions, resolution
desired at various positions in a process or plant,
reliability of the sensors, and the like. The impact of a failure, time
response of a failure (e.g., warning time and/or off-nominal
modes occurring before failure), likelihood of failure, and/or sensitivity
required and/or difficulty to detect failed conditions may
drive the extent to which a component or piece of equipment is monitored with
more sensors and/or higher capability sensors being
dedicated to systems where unexpected or undetected failure would be costly or
have severe consequences.
[0538] The signal evaluation circuit 8708 may process the detection values to
obtain information about a component or piece of
equipment being monitored. Information extracted by the signal evaluation
circuit 8708 may comprise information regarding what
point or time in a process corresponds with a detection value where the point
in time is based on a timing signal generated by the
timer circuit 8714. The start of the timing signal may be generated by
detecting an edge of a control signal such as a rising edge,
falling edge or both where the control signal may be associated with the start
of a process. The start of the timing signal may be
triggered by an initial movement of a component or piece of equipment. The
start of the timing signal may be triggered by an initial
flow through a pipe or opening or by a flow achieving a predetermined rate.
The start of the timing signal may be triggered by a
state value indicating a process has commenced ¨ for example the state of a
switch, button, data value provided to indicate the
process has commenced, or the like. Information extracted may comprise
information regarding a difference in phase, determined
by the phase detection circuit 8750, between a stream of detection value and
the time signal generated by the timer circuit 8714.
Information extracted may comprise information regarding a difference in phase
between one stream of detection values and a
second stream of detection values where the first stream of detection values
is used as a basis or trigger for a timing signal generated
by the timer circuit.
[0539] Depending on the type of equipment, the component being measured, the
environment in which the equipment is operating
and the like, sensors 8706 may comprise one or more of, without limitation, a
thermometer, a hygrometer, a voltage sensor, a current
sensor, an accelerometer, a velocity detector, a light or electromagnetic
sensor (e.g., determining temperature, composition and/or
spectral analysis, and/or object position or movement), an image sensor, a
displacement sensor, a turbidity meter, a viscosity meter,
a load sensor, a tri-axial sensor, a tachometer, a fluid pressure meter, an
air flow meter, a horsepower meter, a flow rate meter, a
fluid particle detector, an acoustical sensor, a pH sensor, and the like.
[0540] The sensors 8706 may provide a stream of data over time that has a
phase component, such as acceleration or vibration,
allowing for the evaluation of phase or frequency analysis of different
operational aspects of a piece of equipment or an operating
component. The sensors 8706 may provide a stream of data that is not phase
based such as temperature, humidity, load, and the like.
The sensors 8706 may provide a continuous or near continuous stream of data
over time, periodic readings, event-driven readings,
and/or readings according to a selected interval or schedule.
[0541] In embodiments, as illustrated in Figure 59, the sensors 8706 may be
part of the data monitoring device 8700. In
embodiments, as illustrated in Figures 60 and 61, one or more external sensors
8724 which are not explicitly part of a monitoring
device 8718 may be opportunistically connected to or accessed by the
monitoring device 8718. The monitoring device 8718 may
include a controller 8720. The controller 8720 may include a signal evaluation
circuit 8708, a data storage circuit 8716, a data
acquisition circuit 8704 and an optional response circuit 8710. The signal
evaluation circuit 8708 may include a timer circuit 8714
and optionally a phase detection circuit 8712. The data acquisition circuit
8704 may include one or more input ports 8726. The one
or more external sensors 8724 may be directly connected to the one or more
input ports 8726 on the data acquisition circuit 8704 of
the controller 8720. In embodiments as shown in Figure 61, a data acquisition
circuit 8704 may further comprise a wireless
communications circuit 8728. The data acquisition circuit 8704 may use the
wireless communications circuit 8728 to access
detection values corresponding to the one or more external sensors 8724
wirelessly or via a separate source or some combination of
these methods.
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[0542] In embodiments as illustrated in Figure 62, the sensors 8706 may be
part of a data monitoring system 8730 having a data
monitoring device 8720. A data acquisition circuit 8734 may further comprise a
multiplexer circuit 8736 as described elsewhere
herein. Outputs from the multiplexer circuit 8736 may be utilized by the
signal evaluation circuit 8708. The response circuit 8710
may have the ability to turn on and off portions of the multiplexor circuit
8736. The response circuit 8710 may have the ability to
control the control channels of the multiplexor circuit 8736
[0543] The response circuit 8710 may further comprise evaluating the results
of the signal evaluation circuit 8708 and, based on
certain criteria, initiating an action. The criteria may include a sensor's
detection values at certain frequencies or phases relative to
the timer signal where the frequencies or phases of interest may be based on
the equipment geometry, equipment control schemes,
system input, historical data, current operating conditions, and/or an
anticipated response. Criteria may include a predetermined
maximum or minimum value for a detection value from a specific sensor, a
cumulative value of a sensor' s corresponding detection
value overtime, a change in value, a rate of change in value, and/or an
accumulated value (e.g., a time spent above/below a threshold
value, a weighted time spent above/below one or more threshold values, and/or
an area of the detected value above/below one or
more threshold values). The criteria may comprise combinations of data from
different sensors such as relative values, relative
changes in value, relative rates of change in value, relative values over
time, and the like. The relative criteria may change with
other data or information such as process stage, type of product being
processed, type of equipment, ambient temperature and
humidity, external vibrations from other equipment, and the like.
[0544] Certain embodiments are described herein as detected values exceeding
thresholds or predetermined values, but detected
values may also fall below thresholds or predetermined values ¨ for example
where an amount of change in the detected value is
expected to occur, but detected values indicate that the change may not have
occurred. For example, and without limitation,
vibrational data may indicate system agitation levels, properly operating
equipment, or the like, and vibrational data below amplitude
and/or frequency thresholds may be an indication of a process that is not
operating according to expectations. Except where the
context clearly indicates otherwise, any description herein describing a
determination of a value above a threshold and/or exceeding
a predetermined or expected value is understood to include determination of a
value below a threshold and/or falling below a
predetermined or expected value.
[0545] The predetermined acceptable range may be based on anticipated system
response or vibration based on the equipment
geometry and control scheme such as number of bearings, relative rotational
speed, influx of power to the system at a certain
frequency, and the like. The predetermined acceptable range may also be based
on long term analysis of detection values across a
plurality of similar equipment and components and correlation of data with
equipment failure.
[0546] In some embodiments, an alert may be issued based on the some of the
criteria discussed above. In an illustrative example,
an increase in temperature and energy at certain frequencies may indicate a
hot bearing that is starting to fail. In embodiments, the
relative criteria for an alarm may change with other data or information such
as process stage, type of product being processed on
equipment, ambient temperature and humidity, external vibrations from other
equipment and the like. In an illustrative and non-
limiting example, the response circuit 8710 may initiate an alert if a
vibrational amplitude and/or frequency exceeds a predetermined
maximum value, if there is a change or rate of change that exceeds a
predetermined acceptable range, and/or if an accumulated
value based on vibrational amplitude and/or frequency exceeds a threshold.
[0547] In embodiments, response circuit 8710 may cause the data acquisition
circuit 8734 to enable or disable the processing of
detection values corresponding to certain sensors based on the some of the
criteria discussed above. This may include switching to
sensors having different response rates, sensitivity, ranges, and the like;
accessing new sensors or types of sensors, and the like. This
switching may be implemented by changing the control signals for a multiplexor
circuit 8736 and/or by turning on or off certain
input sections of the multiplexor circuit 8736. The response circuit 8710 may
make recommendations for the replacement of certain
sensors in the future with sensors having different response rates,
sensitivity, ranges, and the like. The response circuit 8710 may
recommend design alterations for future embodiments of the component, the
piece of equipment, the operating conditions, the
process, and the like.
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[0548] In embodiments, the response circuit 8710 may recommend maintenance at
an upcoming process stop or initiate a
maintenance call. The response circuit 8710 may recommend changes in process
or operating parameters to remotely balance the
piece of equipment. In embodiments, the response circuit 8710 may implement or
recommend process changes ¨ for example to
lower the utilization of a component that is near a maintenance interval,
operating off-nominally, or failed for purpose but still at
least partially operational. In an illustrative example, vibration phase
information, derived by the phase detection circuit 8712
relative to a timer signal from the timer circuit 8714, may be indicative of a
physical location of a problem. Based on the vibration
phase information, system design flaws, off-nominal operation, and/or
component or process failures may be identified.
[0549] In embodiments, based on relevant operating conditions and/or failure
modes which may occur in as sensor values approach
one or more criteria, the signal evaluation circuit 8708 may store data in the
data storage circuit 8716 based on the fit of data relative
to one or more criteria. Based on one sensor input meeting or approaching
specified criteria or range, the signal evaluation circuit
8708 may store additional data such as RPMS, component loads, temperatures,
pressures, vibrations in the data storage circuit 8716.
The signal evaluation circuit 8708 may store data at a higher data rate for
greater granularity in future processing, the ability to
reprocess at different sampling rates, and/or to enable diagnosing or post-
processing of system information where operational data
of interest is flagged, and the like.
[0550] In embodiments as shown in Figure 63, a data monitoring system 8738 may
include at least one data monitoring device
8740. The at least one data monitoring device 8740 may include sensors 8706 a
data acquisition circuit 8714, a signal evaluation
circuit 8708, a data storage circuit 8742. The signal evaluation circuit 8708
may include at least one of a phase detection circuit
8712 and a timer circuit 8714.
[0551] In embodiments, as shown in Figures 64 and 65, a data monitoring system
8726 may include at least one data monitoring
device 8768. The at least one data monitoring device 8768 may include sensors
8706 and a controller 8730 comprising a data
acquisition circuit 8704, a signal evaluation circuit 8708, a data storage
circuit 8716, and a comunications circuit 8732. The signal
evaluation circuit 8708 may include at least one of a phase detection circuit
8712 and a timer circuit 8714. The communications
circuit 8732 allows data and analysis to be transmitted to a monitoring
application 8752 on a remote server 8750. The signal
evaluation circuit 8708 may include at least one of a phase detection circuit
8712 and a timer circuit 8714. The signal evaluation
circuit 8708 may periodically share data with the communication circuit 8732
for transmittal to the remote server 8750 to enable
the tracking of component and equipment performance over time and under
varying conditions by a monitoring application 8752.
Because relevant operating conditions and/or failure modes may occur as sensor
values approach one or more criteria, the signal
evaluation circuit 8708 may share data with the communication circuit 8732 for
transmittal to the remote server 8750 based on the
fit of data relative to one or more criteria. Based on one sensor input
meeting or approaching specified criteria or range, the signal
evaluation circuit 8708 may share additional data such as RPMS, component
loads, temperatures, pressures, vibrations, and the like
for transmittal. The signal evaluation circuit 8708 may share data at a higher
data rate for transmittal to enable greater granularity
in processing on the remote server.
[0552] In embodiments as shown in Figure 64, the communications circuit 8732
may communicated data directly to a remote
server 8750. In embodiments as shown in Figure 65, the communications circuit
8732 may communicate data to an intermediate
computer 8754 which may include a processor 8756 running an operating system
8758 and a data storage circuit 8760. The
intermediate computer 8754 may collect data from a plurality of data
monitoring devices and send the cumulative data to the remote
server 8750.
[0553] In embodiments as illustrated in Figures 66 and 67, a data collection
system 8762 may have a plurality of monitoring
devices 8744 collecting data on multiple components in a single piece of
equipment, collecting data on the same component across
a plurality of pieces of equipment, (both the same and different types of
equipment) in the same facility as well as collecting data
from monitoring devices in multiple facilities. At least one of the plurality
of data monitoring devices 8744 may include sensors
8706 and a controller 8746 comprising a data acquisition circuit 8704, a
signal evaluation circuit 8708, a data storage circuit 8742,
and a comunications circuit 8764. In embodiments as dinvki in in Ficnire 66 n
nmm11niations circuit 8764 may communicat data
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directly to a remote server 8750. In embodiments as shown in Figure 67, the
communications circuit 8764 may communicate data
to an intermediate computer 8754 which may include a processor 8756 running an
operating system 8758 and a data storage circuit
8760. The intermediate computer 8754 may collect data from a plurality of data
monitoring devices and send the cumulative data
to the remote server 8750.
[0554] In embodiments, a monitoring application 8752 on a remote server 8750
may receive and store one or more of detection
values, timing signals and data coming from a plurality of the various
monitoring devices 8744. The monitoring application 8752
may then select subsets of the detection values, timing signals and data to be
jointly analyzed. Subsets for analysis may be selected
based on a single type of component or a single type of equipment in which a
component is operating. Subsets for analysis may be
selected or grouped based on common operating conditions such as size of load,
operational condition (e.g. intermittent, continuous,
process stage), operating speed or tachometer, common ambient environmental
conditions such as humidity, temperature, air or
fluid particulate, and the like. Subsets for analysis may be selected based on
the effects of other nearby equipment such as nearby
machines rotating at similar frequencies.
[0555] The monitoring application 8752 may then analyze the selected subset.
In an illustrative example, data from a single
component may be analyzed over different time periods such as one operating
cycle, several operating cycles, a month, a year, the
life of the component or the like. Data from multiple components of the same
type may also be analyzed over different time periods.
Trends in the data such as changes in frequency or amplitude may be correlated
with failure and maintenance records associated
with the same or a related component or piece of equipment. Trends in the data
such as changing rates of change associated with
start-up or different points in the process may be identified. Additional data
may be introduced into the analysis such as output
product quality, indicated success or failure of a process, and the like.
Correlation of trends and values for different types of data
may be analyzed to identify those parameters whose short-term analysis might
provide the best prediction regarding expected
performance. This information may be transmitted back to the monitoring device
to update types of data collected and analyzed
locally or to influence the design of future monitoring devices.
[0556] In an illustrative and non-limiting example, a monitoring device 8700
may be used to collect and process sensor data to
measure mechanical torque. The monitoring device 8700 may be in communication
with or include a high resolution, high speed
vibration sensor to collect data over a period of time sufficient to measure
multiple cycles of rotation. For gear driven components,
the sampling resolution of the sensor should be such that the number of
samples taken per cycle is at least equal to the number of
gear teeth driving the component. It will be understood that a lower sampling
resolution may also be utilized, which may result in
a lower confidence determination and/or taking data over a longer period of
time to develop sufficient statistical confidence. This
data may then be used in the generation of a phase reference (relative probe)
or tachometer signal for a piece of equipment. This
phase reference may be used directly or used by the timer circuit 8714 to
generate a timing signal to align phase data such as
vibrational data or acceleration data from multiple sensors located at
different positions on a component or on different components
within a system. This information may facilitate the determination of torque
for different components or the generation of an
Operational Deflection Shape (ODS).
[0557] A higher resolution data stream may also provide additional data for
the detection of transitory signals in low speed
operations. The identification of transitory signals may enable the
identification of defects in a piece of equipment or component
operating a low RPMs.
[0558] In an illustrative and non-limiting example, the monitoring device may
be used to identify mechanical jitter for use in
failure prediction models. The monitoring device may begin acquiring data when
the piece of equipment starts up through ramping
up to operating speed and then during operation. Once at operating speed, it
is anticipated that the torsional jitter should be minimal
or within expected ranges, and changes in torsion during this phase may be
indicative of cracks, bearing faults and the like.
Additionally, known torsions may be removed from the signal to facilitate in
the identification of unanticipated torsions resulting
from system design flaws, component wear, or unexpected process events. Having
phase information associated with the data
collected at operating speed may facilitate identification of a location of
vibration and potential component wear, and/or may be
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further correlated to a type of failure for a component. Relative phase
information for a plurality of sensors located throughout a
machine may facilitate the evaluation of torsion as it is propagated through a
piece of equipment.
[0559] In embodiments, the monitoring application 8752 may have access to
equipment specifications, equipment geometry,
component specifications, component materials, anticipated state information
for a plurality of component types, operational history,
historical detection values, component life models and the like for use
analyzing the selected subset using rule-based or model-
based analysis. In embodiments, the monitoring application 8752 may feed a
neural net with the selected subset to learn to recognize
various operating state, health states (e.g. lifetime predictions) and fault
states utilizing deep learning techniques. In embodiments,
a hybrid of the two techniques (model-based learning and deep learning) may be
used.
[0560] In an illustrative and non-limiting example, component health on
conveyors and lifters in an assembly line may be
monitored using the phase detection and alignment techniques, data monitoring
devices and data collection systems described herein.
[0561] In an illustrative and non-limiting example, component health in water
pumps on industrial vehicles may be monitored
using the phase detection and alignment techniques, data monitoring devices
and data collection systems described herein.
[0562] In an illustrative and non-limiting example, component health in
compressors in gas handling systems may be monitored
using the phase detection and alignment techniques, data monitoring devices
and data collection systems described herein.
[0563] In an illustrative and non-limiting example, component health in
compressors situated out in the gas and oil fields may be
monitored using the phase detection and alignment techniques, data monitoring
devices and data collection systems described herein.
[0564] In an illustrative and non-limiting example, component health in
factory air conditioning units may be evaluated using the
phase detection and alignment techniques, data monitoring devices and data
collection systems described herein.
[0565] In an illustrative and non-limiting example, component health in
factory mineral pumps may be evaluated using the phase
detection and alignment techniques, data monitoring devices and data
collection systems described herein.
[0566] In an illustrative and non-limiting example, component health in
drilling machines and screw drivers situated in the oil and
gas fields may be evaluated using the phase detection and alignment
techniques, data monitoring devices and data collection systems
described herein.
[0567] In an illustrative and non-limiting example, component health of motors
situated in the oil and gas fields may be evaluated
using phase detection and alignment techniques, data monitoring devices and
data collection systems described herein.
[0568] In an illustrative and non-limiting example, the component health of
pumps situated in the oil and gas fields may be
evaluated using the phase detection and alignment techniques, data monitoring
devices and data collection systems described herein.
[0569] In an illustrative and non-limiting example, the component health of
gearboxes situated in the oil and gas fields may be
evaluated using the phase detection and alignment techniques, data monitoring
devices and data collection systems described herein.
[0570] In an illustrative and non-limiting example, the component health of
vibrating conveyors situated in the oil and gas fields
may be evaluated using the phase detection and alignment techniques, data
monitoring devices and data collection systems described
herein.
[0571] In an illustrative and non-limiting example, the component health of
mixers situated in the oil and gas fields may be
evaluated using the phase detection and alignment techniques, data monitoring
devices and data collection systems described herein.
[0572] In an illustrative and non-limiting example, the component health of
centrifuges situated in oil and gas refineries may be
evaluated using the phase detection and alignment techniques, data monitoring
devices and data collection systems described herein.
[0573] In an illustrative and non-limiting example, the component health of
refining tanks situated in oil and gas refineries may
be evaluated using the phase detection and alignment techniques, data
monitoring devices and data collection systems described
herein.
[0574] In an illustrative and non-limiting example, the component health of
rotating tank/mixer agitators to promote chemical
reactions deployed in chemical and pharmaceutical production lines may be
evaluated using the phase detection and alignment
techniques, data monitoring devices and data collection systems described
herein.
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[0575] In an illustrative and non-limiting example, the component health of
mechanical/rotating agitators to promote chemical
reactions deployed in chemical and pharmaceutical production lines may be
evaluated using the phase detection and alignment
techniques, data monitoring devices and data collection systems described
herein.
[0576] In an illustrative and non-limiting example, the component health of
propeller agitators to promote chemical reactions
deployed in chemical and pharmaceutical production lines may be evaluated
using the phase detection and alignment techniques,
data monitoring devices and data collection systems described herein.
[0577] In an illustrative and non-limiting example, the component health of
vehicle steering mechanisms may be evaluated using
the phase detection and alignment techniques, data monitoring devices and data
collection systems described herein.
[0578] In an illustrative and non-limiting example, the component health of
vehicle engines may be evaluated using the phase
detection and alignment techniques, data monitoring devices and data
collection systems described herein.
[0579] 1. A monitoring system for data collection, the monitoring system
comprising:
a data acquisition circuit structured to interpret a plurality of detection
values, each of the plurality of detection values corresponding
to at least one of a plurality of input sensors communicatively coupled to the
data acquisition circuit;
a signal evaluation circuit comprising:
a timer circuit structured to generate at least one timing signal; and
a phase detection circuit structured to determine a relative phase difference
between at least one of the plurality of detection values
and at least one of the timing signals from the timer circuit; and
a response circuit structured to perform at least one operation in response to
the relative phase difference.
[0580] 2. The monitoring system of claim 1, wherein the at least one operation
is further in response to at least one of: a change
in amplitude of at least one of the plurality of detection values; a change in
frequency or relative phase of at least one of the plurality
of detection values; a rate of change in both amplitude and relative phase of
at least one the plurality of detection values; and a
relative rate of change in amplitude and relative phase of at least one the
plurality of detection values.
[0581] 3. The monitoring system of claim 1, wherein the at least one operation
comprises issuing an alert.
[0582] 4. The monitoring system of claim 3, wherein the alert may be one of
haptic, audible and visual.
[0583] 5. The monitoring system of claim 1, further comprising a data storage
circuit, wherein the relative phase difference and at
least one of the detection values and the timing signal are stored.
[0584] 6. The monitoring system of claim 5 wherein the at least one operation
further comprises storing additional data in the data
storage circuit.
[0585] 7. The monitoring system of claim 6, wherein the storing additional
data in the data storage circuit is further in response to
at least one of: a change in the relative phase difference and a relative rate
of change in the relative phase difference.
[0586] 8. The monitoring system of claim 1, wherein the data acquisition
circuit further comprises at least one multiplexer circuit
(MUX) whereby alternative combinations of detection values may be selected
based on at least one of user input and a selected
operating parameter for a machine, wherein each of the plurality of detection
values corresponds to at least one of the input sensors.
[0587] 9. The monitoring system of claim 8, wherein the at least one operation
comprises enabling or disabling one or more
portions of the multiplexer circuit, or altering the multiplexer control
lines.
[0588] 10. The monitoring system of claim 8, wherein the data acquisition
circuit comprises at least two multiplexer circuits and
the at least one operation comprises changing connections between the at least
two multiplexer circuits.
[0589] 11. The monitoring system of claim 8, further comprising a MUX control
circuit structured to interpret a subset of the
plurality of detection values and provide the logical control of the MUX and
the correspondence of MUX input and detected values
as a result, wherein the logic control of the MUX comprises adaptive
scheduling of the select lines.
[0590] 12. A system for data collection, the system comprising:
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a data acquisition circuit structured to interpret a plurality of detection
values, each of the plurality of detection values corresponding
to at least one of a plurality of input sensors communicatively coupled to the
data acquisition circuit;
a signal evaluation circuit comprising:
a timer circuit structured to generate a timing signal based on a first
detected value of the plurality of detection values; and
a phase detection circuit structured to determine a relative phase difference
between a second detection value of the plurality of
detection values and the timing signal; and
a phase response circuit structured to perform at least one operation in
response to the phase difference.
[0591] 13. The system of claim 12, wherein the at least one operation is
further in response to at least one of: a change in amplitude
of at least one of the plurality of detection values; a change in frequency or
relative phase of at least one of the plurality of detection
values; a rate of change in both amplitude and relative phase of at least one
the plurality of detection values; and a relative rate of
change in amplitude and relative phase of at least one the plurality of
detection values.
[0592] 14. The system of claim 12, wherein the at least one operation
comprises issuing an alert.
[0593] 15. The system of claim 14, wherein the alert may be one of haptic,
audible and visual.
[0594] 16. The system of claim 12, further comprising a data storage circuit,
wherein the relative phase difference and at least one
of the detection values and the timing signal are stored.
[0595] 17. The system of claim 16 wherein the at least one operation further
comprises storing additional data in the data storage
circuit.
[0596] 18. The system of claim 17, wherein the storing additional data in the
data storage circuit is further in response to at least
one of: a change in the relative phase difference and a relative rate of
change in the relative phase difference.
[0597] 19. The system of claim 12, wherein the data acquisition circuit
further comprises at least one multiplexer (MUX) circuit
whereby alternative combinations of detection values may be selected based on
at least one of user input and a selected operating
parameter for a machine, wherein each of the plurality of detection values
corresponds to at least one of the input sensors.
[0598] 20. The system of claim 19, wherein the at least one operation
comprises enabling or disabling one or more portions of the
multiplexer circuit, or altering the multiplexer control lines.
[0599] 21. The system of claim 19, wherein the data acquisition circuit
comprises at least two multiplexer circuits and the at least
one operation comprises changing connections between the at least two
multiplexer circuits.
[0600] 22. The monitoring system of claim 19, further comprising a MUX control
circuit structured to interpret a subset of the
plurality of detection values and provide the logical control of the MUX and
the correspondence of MUX input and detected values
as a result, wherein the logic control of the MUX comprises adaptive
scheduling of the select lines.
[0601] 23. A system for data collection, processing, and utilization of
signals in an industrial environment comprising:
a data acquisition circuit structured to interpret a plurality of detection
values, each of the plurality of detection values corresponding
to at least one of a plurality of input sensors communicatively coupled to the
data acquisition circuit;
a signal evaluation circuit comprising:
a timer circuit structured to generate a timing signal based on a first
detected value of the plurality of detection values; and
a phase detection circuit structured to determine a relative phase difference
between a second detection value of the plurality of
detection values and the timing signal;
a data storage facility for storing a subset of the plurality of detection
values and the timing signal;
a communication circuit structured to communicate at least one selected
detection value and the timing signal to a remote server;
and
a monitoring application on the remote server structured to:
receive the at least one selected detection value and the timing signal;
jointly analyze a subset of the detection values received from the plurality
of monitoring devices; and
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recommend an action.
[0602] 24. The system of claim 23, wherein joint analysis comprises using the
timing signal from each of the plurality of
monitoring devices to align the detection values from the plurality of
monitoring devices.
[0603] 25. The system of claim 23 wherein the subset of detection values is
selected based on data associated with a detection
value comprising at least one: common type of component, common type of
equipment, and common operating conditions.
[0604] 26. A monitoring system for data collection in an industrial
environment, the monitoring device comprising:
a data acquisition circuit structured to interpret a plurality of detection
values, each of the plurality of detection values corresponding
to at least one of a plurality of input sensors communicatively coupled to the
data acquisition circuit, the data acquisition circuit
comprising a multiplexer circuit whereby alternative combinations of the
detection values may be selected based on at least one of
user input, a detected state and a selected operating parameter for a machine,
each of the plurality of detection values corresponding
to at least one of the input sensors;
a signal evaluation circuit comprising:
a timer circuit structured to generate a timing signal; and
a phase detection circuit structured to determine a relative phase difference
between at least one of the plurality of detection values
and a signal from the timer circuit; and
a response circuit structured to perform at least one operation in response to
the phase difference.
[0605] 27. A monitoring system for data collection in a piece of equipment,
the monitoring device comprising:
a data acquisition circuit structured to interpret a plurality of detection
values, each of the plurality of detection values corresponding
to at least one of a plurality of input sensors communicatively coupled to the
data acquisition circuit;
a timer circuit structured to generate a timing signal based on a first
detected value of the plurality of detection values;
a signal evaluation circuit structured to obtain at least one of vibration
amplitude, vibration frequency and vibration phase location
corresponding to a second detected value comprising:
a phase detection circuit structured to determine a relative phase difference
between a second detection value of the plurality of
detection values and the timing signal; and
a response circuit structured to perform at least one operation in response to
at the at least one of the vibration amplitude, vibration
frequency and vibration phase location.
[0606] 28. A monitoring system for bearing analysis in an industrial
environment, the monitoring device comprising:
a data acquisition circuit structured to interpret a plurality of detection
values, each of the plurality of detection values corresponding
to at least one of a plurality of input sensors communicatively coupled to the
data acquisition circuit;
a timer circuit structured to generate a timing signal
a data storage for storing specifications and anticipated state information
for a plurality of bearing types and buffering the plurality
of detection values for a predetermined length of time;
a timer circuit structured to generate a timing signal based on a first
detected value of the plurality of detection values;
a bearing analysis circuit structured to analyze buffered detection values
relative to specifications and anticipated state information
resulting in a life prediction comprising:
a phase detection circuit structured to determine a relative phase difference
between a second detection value of the plurality of
detection values and the timing signal;
a signal evaluation circuit structured to obtain at least one of vibration
amplitude, vibration frequency and vibration phase location
corresponding to a second detected value: and
a response circuit structured to perform at least one operation in response to
at the at least one of the vibration amplitude, vibration
frequency and vibration phase location.
[0607] In embodiments, information about the health or other status or state
information of or regarding a component or piece of
industrial equipment may be obtained by monitoring the entiditinn nf vnrinliQ
rnninntlentS throughout a process. Monitoring may
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include monitoring the amplitude of a sensor signal measuring attributes such
as temperature, humidity, acceleration, displacement
and the like. An embodiment of a data monitoring device is shown in Figure 68
and may include a plurality of sensors 9006
communicatively coupled to a controller 9002. The controller 9002, which may
be part of a data collection device, such as a mobile
data collector, or part of a system, such as a network-deployed or cloud-
deployed system, may include a data acquisition circuit
9004, a signal evaluation circuit 9008 and a response circuit 9010. The signal
evaluation circuit 9008 may comprise a peak detection
circuit 9012. Additionally, the signal evaluation circuit 9008 may optionally
comprise one or more of a phase detection circuit 9016,
a bandpass filter circuit 9018, a phase lock loop circuit, a torsional
analysis circuit, a bearing analysis circuit, and the like. The band
pass filter 9018 may be used to filter a stream of detection values such that
values, such as peaks and valleys, are detected only at
or within bands of interest, such as frequencies of interest. The data
acquisition circuit 9004 may include one or more analog to
digital converter circuits 9014. A peak amplitude detected by the peak
detection circuit 9012 may be input into one or more analog
to digital converter circuits 9014 to provide a reference value for scaling
output of the analog to digital converter circuits 9014
appropriately.
[0608] The plurality of sensors 9006 may be wired to ports on the data
acquisition circuit 9004. The plurality of sensors 9006 may
be wirelessly connected to the data acquisition circuit 9004. The data
acquisition circuit 9004 may be able to access detection values
corresponding to the output of at least one of the plurality of sensors 9006
where the sensors 9006 may be capturing data on different
operational aspects of a piece of equipment or an operating component.
[0609] The selection of the plurality of sensors 9006 for a data monitoring
device 9000 designed for a specific component or piece
of equipment may depend on a variety of considerations such as accessibility
for installing new sensors, incorporation of sensors in
the initial design, anticipated operational and failure conditions, resolution
desired at various positions in a process or plant,
reliability of the sensors, power availability, power utilization, storage
utilization, and the like. The impact of a failure, time response
of a failure (e.g. warning time and/or off-optimal modes occurring before
failure), likelihood of failure, extent of impact of failure,
and/or sensitivity required and/or difficulty to detection failure conditions
may drive the extent to which a component or piece of
equipment is monitored with more sensors and/or higher capability sensors
being dedicated to systems where unexpected or
undetected failure would be costly or have severe consequences.
[0610] The signal evaluation circuit 9008 may process the detection values to
obtain information about a component or piece of
equipment being monitored. Information extracted by the signal evaluation
circuit 9008 may comprise information regarding a peak
value of a signal such as a peak temperature, peak acceleration, peak
velocity, peak pressure, peak weight bearing, peak strain, peak
bending, or peak displacement. The peak detection may be done using analog or
digital circuits. In embodiments, the peak detection
circuit 9012 may be able to distinguish between "local" or short term peaks in
a stream of detection values and a "global" or longer
term peak. In embodiments, the peak detection circuit 9012 may be able to
identify peak shapes (not just a single peak value) such
as flat tops, asymptotic approaches, discrete jumps in the peak value or
rapid/steep climbs in peak value, sinusoidal behavior within
ranges and the like. Flat topped peaks may indicate saturation at of a sensor.
Asymptotic approaches to a peak may indicate linear
system behavior. Discrete jumps in value or steep changes in peak value may
indicate quantized or nonlinear behavior of either the
sensor doing the measurement or the behavior of the component. In embodiments,
the system may be able to identify sinusoidal
variations in the peak value within an envelope, such as an envelope
established by line or curve connecting a series of peak values.
It should be noted that references to "peaks" should be understood to
encompass one or more "valleys," representing a series of low
points in measurement, except where context indicates otherwise.
[0611] In embodiments, a peak value may be used as a reference for an analog
to digital conversion circuit 9014.
[0612] In an illustrative and non-limiting example, a temperature probe may
measure the temperature of a gear as it rotates in a
machine. The peak temperature may be detected by a peak detection circuit
9012. The peak temperature may be fed into an analog
to digital converter circuit 9014 to appropriately scale a stream of detection
values corresponding to temperature readings of the
gear as it rotates in a machine. The phase of the stream of detection values
corresponding to temperature relative to an orientation
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of the gear may be determined by the phase detection circuit 9016. Knowing
where in the rotation of the gear a peak temperature is
occurring may allow the identification of a bad gear tooth.
[0613] In some embodiments, two or more sets of detection values may be fused
to create detection values for a virtual sensor. A
peak detection circuit may be used to verify consistency in timing of peak
values between at least one of the two or more sets of
detection values and the detection values for the virtual sensor.
[0614] In embodiments, the signal evaluation circuit 9008 may be able to reset
the peak detection circuit 9012 upon start-up of the
monitoring device, upon edge detection of a control signal of the system being
monitored, based on a user input, after a system error
and the like. In embodiments, the signal evaluation circuit 9008 may discard
an initial portion of the output of the peak detection
circuit 9012 prior to using the peak value as a reference value for an analog
to digital conversion circuit to allow the system to fully
come on line.
[0615] Depending on the type of equipment, the component being measured, the
environment in which the equipment is operating
and the like, sensors 9006 may comprise one or more of, without limitation, a
vibration sensor, a thermometer, a hygrometer, a
voltage sensor, a current sensor, an accelerometer, a velocity detector, a
light or electromagnetic sensor (e.g., determining
temperature, composition and/or spectral analysis, and/or object position or
movement), an image sensor, a structured light sensor,
a laser-based image sensor, an acoustic wave sensor, a displacement sensor, a
turbidity meter, a viscosity meter, a load sensor, a tri-
axial sensor, an accelerometer, a tachometer, a fluid pressure meter, an air
flow meter, a horsepower meter, a flow rate meter, a fluid
particle detector, an acoustical sensor, a pH sensor, and the like, including,
without limitation, any of the sensors described
throughout this disclosure and the documents incorporated by reference.
[0616] The sensors 9006 may provide a stream of data over time that has a
phase component, such as relating to acceleration or
vibration, allowing for the evaluation of phase or frequency analysis of
different operational aspects of a piece of equipment or an
operating component. The sensors 9006 may provide a stream of data that is not
conventionally phase-based, such as temperature,
humidity, load, and the like. The sensors 9006 may provide a continuous or
near continuous stream of data over time, periodic
readings, event-driven readings, and/or readings according to a selected
interval or schedule.
[0617] In embodiments, as illustrated in Figure 68, the sensors 9006 may be
part of the data monitoring device, referred to herein
in some cases as a data collector, which in some cases may comprise a mobile
or portable data collector. In embodiments, as
illustrated in Figures 69 and 70, one or more external sensors 9026, which are
not explicitly part of a monitoring device 9020 but
rather are new, previously attached to or integrated into the equipment or
component, may be opportunistically connected to or
accessed by the monitoring device 9020. The monitoring device 9020 may include
a controller 9022. The controller 9022 may
include a response circuit 9010, a signal evaluation circuit 9008 and a data
acquisition circuit 9004. The signal evaluation circuit
9008 may include a peak detection circuit 9012 and optionally a phase
detection circuit 9016 and/or a bandpass filter circuit 9018.
The data acquisition circuit 9004 may include one or more input ports 9028.
The one or more external sensors 9026 may be directly
connected to the one or more input ports 9028 on the data acquisition circuit
9004 of the controller 9022 or may be accessed by the
data acquisition circuit 9004 wirelessly, such as by a reader, interrogator,
or other wireless connection, such as over a short-distance
wireless protocol. In embodiments as shown in Figure 70, a data acquisition
circuit 9004 may further comprise a wireless
communication circuit 9030. The data acquisition circuit 9004 may use the
wireless communication circuit 9030 to access detection
values corresponding to the one or more external sensors 9026 wirelessly or
via a separate source or some combination of these
methods.
[0618] In embodiments as illustrated in Figure 71, a data acquisition circuit
9036 may further comprise a multiplexer circuit 9038
as described elsewhere herein. Outputs from the multiplexer circuit 9038 may
be utilized by the signal evaluation circuit 9008. The
response circuit 9010 may have the ability to turn on and off portions of the
multiplexor circuit 9038. The response circuit 9010
may have the ability to control the control channels of the multiplexor
circuit 9038
[0619] The response circuit 9010 may evaluate the results of the signal
evaluation circuit 9008 and, based on certain criteria,
initiate an action. The criteria may include a predetert-I¨I --1¨ -1-1---
"¨alue from a specific sensor, a cumulative
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value of a sensor' s corresponding detection value over time, a change in peak
value, a rate of change in a peak value, and/or an
accumulated value (e.g., a time spent above/below a threshold value, a
weighted time spent above/below one or more threshold
values, and/or an area of the detected value above/below one or more threshold
values). The criteria may comprise combinations of
data from different sensors such as relative values, relative changes in
value, relative rates of change in value, relative values over
time, and the like. The relative criteria may change with other data or
information such as process stage, type of product being
processed, type of equipment, ambient temperature and humidity, external
vibrations from other equipment, and the like. The
relative criteria may be reflected in one or more calculated statistics or
metrics (including ones generated by further calculations on
multiple criteria or statistics), which in turn may be used for processing
(such as on board a data collector or by an external system),
such as to be provided as an input to one or more of the machine learning
capabilities described in this disclosure, to a control system
(which may be on board a data collector or remote, such as to control
selection of data inputs, multiplexing of sensor data, storage,
or the like), or as a data element that is an input to another system, such as
a data stream or data package that may be available to a
data marketplace, a SCADA system, a remote control system, a maintenance
system, an analytic system, or other system.
[0620] Certain embodiments are described herein as detected values exceeding
thresholds or predetermined values, but detected
values may also fall below thresholds or predetermined values ¨ for example
where an amount of change in the detected value is
expected to occur, but detected values indicate that the change may not have
occurred. For example, and without limitation,
vibrational data may indicate system agitation levels, properly operating
equipment, or the like, and vibrational data below amplitude
and/or frequency thresholds may be an indication of a process that is not
operating according to expectations. For example, in a
process involving a blender, a mixer, an agitator or the like, the absence of
vibration may indicate that a blade, fin, vane or other
working element is unable to move adequately, such as, for example, as a
result of a working material being excessively viscous or
as a result of a problem in gears (e.g., stripped gears, seizing in gears, or
the like (a clutch, or the like). Except where the context
clearly indicates otherwise, any description herein describing a determination
of a value above a threshold and/or exceeding a
predetermined or expected value is understood to include determination of a
value below a threshold and/or falling below a
predetermined or expected value.
[0621] The predetermined acceptable range may be based on anticipated system
response or vibration based on the equipment
geometry and control scheme such as number of bearings, relative rotational
speed, influx of power to the system at a certain
frequency, and the like. The predetermined acceptable range may also be based
on long term analysis of detection values across a
plurality of similar equipment and components and correlation of data with
equipment failure.
[0622] In embodiments, the response circuit 9010 may issue an alert based on
one or more of the criteria discussed above. In an
illustrative example, an increase in peak temperature beyond a predetermined
value may indicate a hot bearing that is starting to
fail. In embodiments, the relative criteria for an alarm may change with other
data or information such as process stage, type of
product being processed on equipment, ambient temperature and humidity,
external vibrations from other equipment and the like.
In an illustrative and non-limiting example, the response circuit 9010 may
initiate an alert if an amplitude, such as a vibrational
amplitude and/or frequency, exceeds a predetermined maximum value, if there is
a change or rate of change that exceeds a
predetermined acceptable range, and/or if an accumulated value based on such
amplitude and/or frequency exceeds a threshold.
[0623] In embodiments, the response circuit 9010 may cause the data
acquisition circuit 9036 to enable or disable the processing
of detection values corresponding to certain sensors based on one or more of
the criteria discussed above. This may include switching
to sensors having different response rates, sensitivity, ranges, and the like;
accessing new sensors or types of sensors, accessing data
from multiple sensors, and the like. Switching may be based on a detected peak
value for the sensor being switched or based on the
peak value of another sensor. Switching may be undertaken based on a model, a
set of rules, or the like. In embodiments, switching
may be under control of a machine learning system, such that switching is
controlled based on one or more metrics of success,
combined with input data, over a set of trials, which may occur under
supervision of a human supervisor or under control of an
automated system. Switching may involve switching from one input port to
another (such as to switch from one sensor to another).
Switching may involve altering the multiplexing of data, such as combining
different streams under different circumstances.
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Switching may involve activating a system to obtain additional data, such as
moving a mobile system (such as a robotic or drone
system), to a location where different or additional data is available (such
as positioning an image sensor for a different view or
positioning a sonar sensor for a different direction of collection) or to a
location where different sensors can be accessed (such as
moving a collector to connect up to a sensor that is disposed at a location in
an environment by a wired or wireless connection).
This switching may be implemented by changing the control signals for a
multiplexor circuit 9038 and/or by turning on or off certain
input sections of the multiplexor circuit 9038.
[0624] In embodiments, the response circuit 9010 may adjust a sensor scaling
value using the detected peak as a reference voltage.
The response circuit 9010 may adjust a sensor sampling rate such that the peak
value is captured.
[0625] The response circuit 9010 may identify sensor overload. In embodiments,
the response circuit 9010 may make
recommendations for the replacement of certain sensors in the future with
sensors having different response rates, sensitivity, ranges,
and the like. The response circuit 9010 may recommend design alterations for
future embodiments of the component, the piece of
equipment, the operating conditions, the process, and the like.
[0626] In embodiments, the response circuit 9010 may recommend maintenance at
an upcoming process stop or initiate a
maintenance call where the maintenance may include the replacement of the
sensor with the same or an alternate type of sensor
having a different response rate, sensitivity, range and the like. In
embodiments, the response circuit 9010 may implement or
recommend process changes ¨ for example to lower the utilization of a
component that is near a maintenance interval, operating
off-nominally, or failed for purpose but still at least partially operational,
to change the operating speed of a component (such as to
put it in a lower-demand mode), to initiate amelioration of an issue (such as
to signal for additional lubrication of a roller bearing
set, or to signal for an alignment process for a system that is out of
balance), and the like.
[0627] In embodiments, as shown in Figure 72, the data monitoring device 9040
may include sensors 9006 and a controller 9042
which may include a data acquisition circuit 9004, and a signal evaluation
circuit 9008. The signal evaluation circuit 9008 may
include a peak detection circuit 9012 and, optionally, a phase detection
circuit 9016 and/or a bandpass filter circuit 9018. The
controller 9042 may further include a data storage circuit 9044, memory, and
the like. The controller 9042 may further include a
response circuit 9010. The signal evaluation circuit 9008 may periodically
store certain detection values in the data storage circuit
9044 to enable the tracking of component performance over time.
[0628] In embodiments, based on relevant criteria as described elsewhere
herein, operating conditions and/or failure modes which
may occur as sensor values approach one or more criteria, the signal
evaluation circuit 9008 may store data in the data storage circuit
9044 based on the fit of data relative to one or more criteria, such as those
described throughout this disclosure. Based on one sensor
input meeting or approaching specified criteria or range, the signal
evaluation circuit 9008 may store additional data such as
revolutions per minute (RPMs), component loads, temperatures, pressures,
vibrations or other sensor data of the types described
throughout this disclosure in the data storage circuit 9044. The signal
evaluation circuit 9008 may store data at a higher data rate for
greater granularity in future processing, the ability to reprocess at
different sampling rates, and/or to enable diagnosing or post-
processing of system information where operational data of interest is
flagged, and the like.
[0629] In embodiments, the signal evaluation circuit 9008 may store new peaks
that indicate changes in overall scaling over a long
duration (e.g. scaling a data stream based on historical peaks over months of
analysis). The signal evaluation circuit 9008 may store
data when historical peak values are approached (e.g. as temperatures,
pressures, vibrations, velocities, accelerations and the like
approach historical peaks).
[0630] In embodiments as shown in Figures 73 and 74 and 75 and 76, a data
monitoring system 9046 9066 may include at least
one data monitoring device 9048. The at least one data monitoring device 9048
may include sensors 9006 and a controller 9050
comprising a data acquisition circuit 9004, a signal evaluation circuit 9008,
a data storage circuit 9044, and a communication circuit
9052 to allow data and analysis to be transmitted to a monitoring application
9056 on a remote server 9054. The signal evaluation
circuit 9008 may include at least one of a peak detection circuit 9012. The
signal evaluation circuit 9008 may periodically share
data with the communication circuit 9052 for transm-;*" 1-^tb 9951 to
enable the tracking of component and
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equipment performance over time and under varying conditions by a monitoring
application 9056. Because relevant operating
conditions and/or failure modes may occur in as sensor values approach one or
more criteria as described elsewhere herein, the
signal evaluation circuit 9008 may share data with the communication circuit
9052 for transmittal to the remote server 9054 based
on the fit of data relative to one or more criteria. Based on one sensor input
meeting or approaching specified criteria or range, the
signal evaluation circuit 9008 may share additional data such as RPMS,
component loads, temperatures, pressures, vibrations, and
the like for transmittal. The signal evaluation circuit 9008 may share data at
a higher data rate for transmittal to enable greater
granularity in processing on the remote server.
[0631] In embodiments as shown in Figure 73, the communication circuit 9052
may communicated data directly to a remote server
9054. In embodiments as shown in Figure 74, the communication circuit 9052 may
communicate data to an intermediate computer
9058 which may include a processor 9060 running an operating system 9062 and a
data storage circuit 9064.
[0632] In embodiments as illustrated in Figures 75 and 76, a data collection
system 9066 may have a plurality of monitoring
devices 9048 collecting data on multiple components in a single piece of
equipment, collecting data on the same component across
a plurality of pieces of equipment, (both the same and different types of
equipment) in the same facility as well as collecting data
from monitoring devices in multiple facilities. A monitoring application 9056
on a remote server 9054 may receive and store one
or more of detection values, timing signals and data coming from a plurality
of the various monitoring devices 9048.
[0633] In embodiments as shown in Figure 75, the communication circuits 9052
may communicated data directly to a remote
server 9054. In embodiments as shown in Figure 76, the communication circuits
9052 may communicate data to one or more
intermediate computers 9058, each of which may include a processor 9060
running an operating system 9062 and a data storage
circuit 9064. There may be an individual intermediate computer 9058 associated
with each monitoring device 9048 or an individual
intermediate computer 9058 may be associated with a plurality of monitoring
devices 9048 where the intermediate computer 9058
may collect data from a plurality of data monitoring devices and send the
cumulative data to the remote server 9054.
[0634] The monitoring application 9056 may select subsets of the detection
values, timing signals and data to jointly analyzed.
Subsets for analysis may be selected based on a single type of component or a
single type of equipment in which a component is
operating. Subsets for analysis may be selected or grouped based on common
operating conditions such as size of load, operational
condition (e.g. intermittent, continuous), operating speed or tachometer,
common ambient environmental conditions such as
humidity, temperature, air or fluid particulate, and the like. Subsets for
analysis may be selected based on the effects of other nearby
equipment such as nearby machines rotating at similar frequencies, nearby
equipment producing electromagnetic fields, nearby
equipment producing heat, nearby equipment inducing movement or vibration,
nearby equipment emitting vapors, chemicals or
particulates, or other potentially interfering or intervening effects.
[0635] The monitoring application 9056 may then analyze the selected subset.
In an illustrative example, data from a single
component may be analyzed over different time periods such as one operating
cycle, several operating cycles, a month, a year, the
life of the component or the like. Data from multiple components of the same
type may also be analyzed over different time periods.
Trends in the data such as changes in frequency or amplitude may be correlated
with failure and maintenance records associated
with the same or a related component or piece of equipment. Trends in the data
such as changing rates of change associated with
start-up or different points in the process may be identified. Additional data
may be introduced into the analysis such as output
product quality, output quantity (such as per unit of time), indicated success
or failure of a process, and the like. Correlation of
trends and values for different types of data may be analyzed to identify
those parameters whose short-term analysis might provide
the best prediction regarding expected performance. This information may be
transmitted back to the monitoring device to update
types of data collected and analyzed locally or to influence the design of
future monitoring devices.
[0636] In embodiments, the monitoring application 9056 may have access to
equipment specifications, equipment geometry,
component specifications, component materials, anticipated state information
for a plurality of component types, operational history,
historical detection values, component life models and the like for use
analyzing the selected subset using rule-based or model-
based analysis. In embodiments, the monitoring applica ti nil Q1)s6 m1v feed n
neiiri1 net xvith the selected subset to learn to recognize
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peaks in waveform patterns by feeding a large data set sample of waveform
behavior of a given type within which peaks are
designated (such as by human analysts).
[0637] 1. A monitoring system for data collection in an industrial
environment, the monitoring system comprising:
a data acquisition circuit structured to interpret a plurality of detection
values from a plurality of input sensors communicatively
coupled to the data acquisition circuit, each of the plurality of detection
values corresponding to at least one of the input sensors;
a peak detection circuit structured to determine at least one peak value in
response to the plurality of detection values; and
a peak response circuit structured to perform at least one operation in
response to the at least one peak value.
[0638] 2. The monitoring system of claim 1, wherein the at least one operation
is further in response to at least one of: a change
in amplitude of at least one of the plurality of detection values; a change in
frequency or relative phase of at least one of the plurality
of detection values; a rate of change in both amplitude and relative phase of
at least one the plurality of detection values; and a
relative rate of change in amplitude and relative phase of at least one the
plurality of detection values.
[0639] 3. The monitoring system of claim 1, wherein the at least one operation
comprises issuing an alert.
[0640] 4. The monitoring system of claim 3, wherein the alert may be one of
haptic, audible and visual.
[0641] 5. The monitoring system of claim 1, further comprising a data storage
circuit, wherein the relative phase difference and at
least one of the detection values and the timing signal are stored.
[0642] 6. The monitoring system of claim 5 wherein the at least one operation
further comprises storing additional data in the data
storage circuit.
[0643] 7. The monitoring system of claim 6, wherein the storing additional
data in the data storage circuit is further in response to
at least one of: a change in the relative phase difference and a relative rate
of change in the relative phase difference.
[0644] 8. The monitoring system of claim 1, wherein the data acquisition
circuit further comprises at least one multiplexer circuit
whereby alternative combinations of detection values may be selected based on
at least one of user input and a selected operating
parameter for a machine, wherein each of the plurality of detection values
corresponds to at least one of the input sensors.
[0645] 9. The monitoring system of claim 8, wherein the at least one operation
comprises enabling or disabling one or more
portions of the multiplexer circuit, or altering the multiplexer control
lines.
[0646] 10. The monitoring system of claim 8, wherein the data acquisition
circuit comprises at least two multiplexer circuits and
the at least one operation comprises changing connections between the at least
two multiplexer circuits.
[0647] 11. A monitoring system for data collection in an industrial
environment, the monitoring system structure to receive input
corresponding to a plurality of sensors, the monitor device comprising:
a data acquisition circuit structured to interpret a plurality of detection
values, each of the plurality of detection values corresponding
to at least one of the input sensors;
a peak detection circuit structured to determine at least one peak value in
response to the plurality of detection values; and
a peak response circuit structured to perform at least one operation in
response to the at least one peak value.
[0648] 12. The monitoring system of claim 11, wherein the at least one
operation is further in response to at least one of: a change
in amplitude of at least one of the plurality of detection values; a change in
frequency or relative phase of at least one of the plurality
of detection values; a rate of change in both amplitude and relative phase of
at least one the plurality of detection values; and a
relative rate of change in amplitude and relative phase of at least one the
plurality of detection values.
[0649] 13. The monitoring system of claim 11, wherein the at least one
operation comprises issuing an alert.
[0650] 14. The monitoring system of claim 13, wherein the alert may be one of
haptic, audible and visual.
[0651] 15. The monitoring system of claim 11, further comprising a data
storage circuit, wherein the relative phase difference and
at least one of the detection values and the timing signal are stored.
[0652] 16. The monitoring system of claim 15 wherein the at least one
operation further comprises storing additional data in the
data storage circuit.
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[0653] 17. The monitoring system of claim 16, wherein the storing additional
data in the data storage circuit is further in response
to at least one of: a change in the relative phase difference and a relative
rate of change in the relative phase difference.
[0654] 18. The monitoring system of claim 11, wherein the data acquisition
circuit further comprises at least one multiplexer
circuit whereby alternative combinations of detection values may be selected
based on at least one of user input and a selected
operating parameter for a machine, wherein each of the plurality of detection
values corresponds to at least one of the input sensors.
[0655] 19. The monitoring system of claim 18, wherein the at least one
operation comprises enabling or disabling one or more
portions of the multiplexer circuit, or altering the multiplexer control
lines.
[0656] 20. The monitoring system of claim 18, wherein the data acquisition
circuit comprises at least two multiplexer circuits and
the at least one operation comprises changing connections between the at least
two multiplexer circuits.
[0657] 21. A system for data collection, processing, and utilization of
signals in an industrial environment comprising:
a plurality of monitoring devices, each monitoring device comprising:
a data acquisition circuit structured to interpret a plurality of detection
values from a plurality of input sensors communicatively
coupled to the data acquisition circuit, each of the plurality of detection
values corresponding to at least one of the input sensors;
a peak detection circuit structured to determine at least one peak value in
response to the plurality of detection values;
a peak response circuit structured to select at least one detection value in
response to the at least one peak value;
a communication circuit structured to communicate the at least one selected
detection value to a remote server; and
a monitoring application on the remote server structured to:
receive the at least one selected detection value;
jointly analyze received detection values from a subset of the plurality of
monitoring devices; and
recommend an action.
[0658] 22. The system of claim 21, the system further structured to subset
detection values based on one of anticipated life of a
component associated with detection values, type of the equipment associated
with detection values, and operational conditions
under which detection values were measured.
[0659] 23. The system of claim 21, wherein the analysis of the subset of
detection values comprises feeding a neural net with the
subset of detection values and supplemental information to learn to recognize
various operating states, health states, life expectancies
and fault states utilizing deep learning techniques.
[0660] 24. The system of claim 21, wherein the supplemental information
comprises one of component specification, component
performance, equipment specification, equipment performance, maintenance
records, repair records and an anticipated state model.
[0661] 25. The system of claim 21, wherein the at least one operation is
further in response to at least one of: a change in amplitude
of at least one of the plurality of detection values; a change in frequency or
relative phase of at least one of the plurality of detection
values; a rate of change in both amplitude and relative phase of at least one
the plurality of detection values; and a relative rate of
change in amplitude and relative phase of at least one the plurality of
detection values.
[0662] 26. The system of claim 21, wherein the at least one operation
comprises issuing an alert.
[0663] 27. The system of claim 26, wherein the alert may be one of haptic,
audible and visual.
[0664] 28. The system of claim 21, further comprising a data storage circuit,
wherein the relative phase difference and at least one
of the detection values and the timing signal are stored.
[0665] 29. The system of claim 28 wherein the at least one operation further
comprises storing additional data in the data storage
circuit.
[0666] 30. The system of claim 29, wherein the storing additional data in the
data storage circuit is further in response to at least
one of: a change in the relative phase difference and a relative rate of
change in the relative phase difference.
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[0667] 31. The system of claim 21, wherein the data acquisition circuit
further comprises at least one multiplexer circuit whereby
alternative combinations of detection values may be selected based on at least
one of user input and a selected operating parameter
for a machine, wherein each of the plurality of detection values corresponds
to at least one of the input sensors.
[0668] 32. The system of claim 31, wherein the at least one operation
comprises enabling or disabling one or more portions of the
multiplexer circuit, or altering the multiplexer control lines.
[0669] 33. The system of claim 31, wherein the data acquisition circuit
comprises at least two multiplexer circuits and the at least
one operation comprises changing connections between the at least two
multiplexer circuits.
[0670] 34. A motor monitoring system, the motor monitoring system comprising:
a data acquisition circuit structured to interpret a plurality of detection
values from a plurality of input sensors communicatively
coupled to the data acquisition circuit, each of the plurality of detection
values corresponding to at least one of the input sensors;
a data storage circuit structured to store specifications, system geometry,
and anticipated state information for the motor and motor
components, store historical motor performance and buffer the plurality of
detection values for a predetermined length of time;
a peak detection circuit structured to determine a plurality of peak values
comprising at least a temperature peak value, a speed peak
value and a vibration peak value in response to the plurality of detection
values and analyze the peak values relative to buffered
detection values, specifications and anticipated state information resulting
in a motor performance parameter; and
a peak response circuit structured to perform at least one operation in
response to one of a peak value and a motor system
performance parameter.
[0671] 35. A system for estimating a vehicle steering system performance
parameter, the device comprising:
a data acquisition circuit structured to interpret a plurality of detection
values from a plurality of input sensors communicatively
coupled to the data acquisition circuit, each of the plurality of detection
values corresponding to at least one of the input sensors;
a data storage circuit structured to store specifications, system geometry,
and anticipated state information for the vehicle steering
system, the rack, the pinion, and the steering column, store historical
steering system performance and buffer the plurality of
detection values for a predetermined length of time;
a peak detection circuit structured to determine a plurality of peak values
comprising at least a temperature peak value, a speed peak
value and a vibration peak value in response to the plurality of detection
values and analyze the peak values relative to buffered
detection values, specifications and anticipated state information resulting
in a vehicle steering system performance parameter; and
a peak response circuit structured to perform at least one operation in
response to one of a peak value and a vehicle steering system
performance parameter.
[0672] 36. A system for estimating a pump performance parameter, the system
comprising:
a data acquisition circuit structured to interpret a plurality of detection
values from a plurality of input sensors communicatively
coupled to the data acquisition circuit, each of the plurality of detection
values corresponding to at least one of the input sensors;
a data storage circuit structured to store specifications, system geometry,
and anticipated state information for the pump and pump
components associated with the detection values, store historical pump
performance and buffer the plurality of detection values for
a predetermined length of time;
a peak detection circuit structured to determine a plurality of peak values
comprising at least a temperature peak value, a speed peak
value and a vibration peak value in response to the plurality of detection
values and analyze the peak values relative to buffered
detection values, specifications and anticipated state information resulting
in a pump performance parameter; and
a peak response circuit structured to perform at least one operation in
response to one of a peak value and a pump performance
parameter.
[0673] 37. The system of claim 36, wherein the pump is a water pump in a car.
[0674] 38. The system of claim 36, wherein the pump is a mineral pump.
[0675] 39. A system for estimating a drill performance parameter for a
drilling machine, the system comprising:
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a data acquisition circuit structured to interpret a plurality of detection
values from a plurality of input sensors communicatively
coupled to the data acquisition circuit, each of the plurality of detection
values corresponding to at least one of the input sensors;
a data storage circuit structured to store specifications, system geometry,
and anticipated state information for the drill and drill
components associated with the detection values, store historical drill
performance and buffer the plurality of detection values for a
predetermined length of time;
a peak detection circuit structured to determine a plurality of peak values
comprising at least a temperature peak value, a speed peak
value and a vibration peak value in response to the plurality of detection
values and analyze the peak values relative to buffered
detection values, specifications and anticipated state information resulting
in a drill performance parameter; and
a peak response circuit structured to perform at least one operation in
response to one of a peak value and a drill performance
parameter.
[0676] 40. The system of claim 39, wherein the drilling machine is one of an
oil drilling machine and a gas drilling machine.
[0677] 41. A system for estimating a conveyor health parameter, the system
comprising:
a data acquisition circuit structured to interpret a plurality of detection
values from a plurality of input sensors communicatively
coupled to the data acquisition circuit, each of the plurality of detection
values corresponding to at least one of the input sensors;
a data storage circuit structured to store specifications, system geometry,
and anticipated state information for a conveyor and
conveyor components associated with the detection values, store historical
conveyor performance and buffer the plurality of
detection values for a predetermined length of time;
a peak detection circuit structured to determine a plurality of peak values
comprising at least a temperature peak value, a speed peak
value and a vibration peak value in response to the plurality of detection
values and analyze the peak values relative to buffered
detection values, specifications and anticipated state information resulting
in a conveyor performance parameter; and
a peak response circuit structured to perform at least one operation in
response to one of a peak value and a conveyor performance
parameter.
[0678] 42. A system for estimating an agitator health parameter, the system
comprising:
a data acquisition circuit structured to interpret a plurality of detection
values from a plurality of input sensors communicatively
coupled to the data acquisition circuit, each of the plurality of detection
values corresponding to at least one of the input sensors;
a data storage circuit structured to store specifications, system geometry,
and anticipated state information for an agitator and agitator
components associated with the detection values, store historical agitator
performance and buffer the plurality of detection values
for a predetermined length of time;
a peak detection circuit structured to determine a plurality of peak values
comprising at least a temperature peak value, a speed peak
value and a vibration peak value in response to the plurality of detection
values and analyze the peak values relative to buffered
detection values, specifications and anticipated state information resulting
in an agitator performance parameter; and
a peak response circuit structured to perform at least one operation in
response to one of a peak value and an agitator performance
parameter.
[0679] 43. The system of claim 42 where the agitator is one of a rotating tank
mixer, a large tank mixer, a portable tank mixers, a
tote tank mixer, a drum mixer, a mounted mixer and a propeller mixer.
[0680] 44. A system for estimating a compressor health parameter, the system
comprising:
a data acquisition circuit structured to interpret a plurality of detection
values from a plurality of input sensors communicatively
coupled to the data acquisition circuit, each of the plurality of detection
values corresponding to at least one of the input sensors;
a data storage circuit structured to store specifications, system geometry,
and anticipated state information for a compressor and
compressor components associated with the detection values, store historical
compressor performance and buffer the plurality of
detection values for a predetermined length of time;
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a peak detection circuit structured to determine a plurality of peak values
comprising at least a temperature peak value, a speed peak
value and a vibration peak value in response to the plurality of detection
values and analyze the peak values relative to buffered
detection values, specifications and anticipated state information resulting
in a compressor performance parameter; and
a peak response circuit structured to perform at least one operation in
response to one of a peak value and a compressor performance
parameter.
[0681] 45. A system for estimating an air conditioner health parameter, the
system comprising:
a data acquisition circuit structured to interpret a plurality of detection
values from a plurality of input sensors communicatively
coupled to the data acquisition circuit, each of the plurality of detection
values corresponding to at least one of the input sensors;
a data storage circuit structured to store specifications, system geometry,
and anticipated state information for an air conditioner and
air conditioner components associated with the detection values, store
historical air conditioner performance and buffer the plurality
of detection values for a predetermined length of time;
a peak detection circuit structured to determine a plurality of peak values
comprising at least a temperature peak value, a speed peak
value, a pressure value and a vibration peak value in response to the
plurality of detection values and analyze the peak values relative
to buffered detection values, specifications and anticipated state information
resulting in an air conditioner performance parameter;
and
a peak response circuit structured to perform at least one operation in
response to one of a peak value and an air conditioner
performance parameter.
[0682] 46. A system for estimating a centrifuge health parameter, the system
comprising:
a data acquisition circuit structured to interpret a plurality of detection
values from a plurality of input sensors communicatively
coupled to the data acquisition circuit, each of the plurality of detection
values corresponding to at least one of the input sensors;
a data storage circuit structured to store specifications, system geometry,
and anticipated state information for a centrifuge and
centrifuge components associated with the detection values, store historical
centrifuge performance and buffer the plurality of
detection values for a predetermined length of time;
a peak detection circuit structured to determine a plurality of peak values
comprising at least a temperature peak value, a speed peak
value and a vibration peak value in response to the plurality of detection
values and analyze the peak values relative to buffered
detection values, specifications and anticipated state information resulting
in a centrifuge performance parameter; and
a peak response circuit structured to perform at least one operation in
response to one of a peak value and a centrifuge performance
parameter.
[0683] Bearings are used throughout many different types of equipment and
applications. Bearings may be present in or supporting
shafts, motors, rotors, stators, housings, frames, suspension systems and
components, gears, gear sets of various types, other
bearings, and other elements. Bearings may be used as support for high speed
vehicles such as maglev trains. Bearings are used to
support rotating shafts for engines, motors, generators, fans, compressors,
turbines and the like. Giant roller bearings may be used
to support buildings and physical infrastructure. Different types of bearings
may be used to support conventional, planetary and
other types of gears. Bearings may be used to support transmissions and gear
boxes such as with roller thrust bearings for example.
Bearings may be used to support wheels, wheel hubs and other rolling parts
using tapered roller bearings.
[0684] There are many different types of bearings such as roller bearings,
needle bearings, sleeve bearings, ball bearings, radial
bearings, thrust load bearings including ball thrust bearings used in low
speed applications and roller thrust bearings, taper bearings
and tapered roller bearings, specialized bearings, magnetic bearings, giant
roller bearings, jewel bearings (e.g., Sapphire), fluid
bearings, flexure bearings to support bending element loads, and the like.
References to bearings throughout this disclosure is
intended to include but not be limited by the above list.
[0685] In embodiments, information about the health or other status or state
information of or regarding a bearing in a piece of
industrial equipment or in an industrial process may be obtained by monitoring
the condition of various components of the industrial
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equipment or industrial process. Monitoring may include monitoring the
amplitude and/or frequency and/or phase of a sensor signal
measuring attributes such as temperature, humidity, acceleration, displacement
and the like.
[0686] An embodiment of a data monitoring device 9200 is shown in Figure 77
and may include a plurality of sensors 9206
communicatively coupled to a controller 9202. The controller 9202 may include
a data acquisition circuit 9204, a data storage circuit
9216, a signal evaluation circuit 9208 and, optionally, a response circuit
9210. The signal evaluation circuit 9208 may comprise a
frequency transformation circuit 9212 and a frequency evaluation circuit 9214.
[0687] The plurality of sensors 9206 may be wired to ports on the data
acquisition circuit 9204. The plurality of sensors 9206 may
be wirelessly connected to the data acquisition circuit 9204. The data
acquisition circuit 9204 may be able to access detection values
corresponding to the output of at least one of the plurality of sensors 9206
where the sensors 9206 may be capturing data on different
operational aspects of a bearing or piece of equipment or infrastructure.
[0688] The selection of the plurality of sensors 9206 for a data monitoring
device 9200 designed for a specific bearing or piece of
equipment may depend on a variety of considerations such as accessibility for
installing new sensors, incorporation of sensors in
the initial design, anticipated operational and failure conditions,
reliability of the sensors, and the like. The impact of failure may
drive the extent to which a bearing or piece of equipment is monitored with
more sensors and/or higher capability sensors being
dedicated to systems where unexpected or undetected bearing failure would be
costly or have severe consequences.
[0689] The signal evaluation circuit 9208 may process the detection values to
obtain information about a bearing being monitored.
The frequency transformation circuit 9212 may transform one or more time-based
detection values to frequency information. The
transformation may be accomplished using techniques such as a digital Fast
Fourier transform (FFT), Laplace transform, Z-
transform, wavelet transform, other frequency domain transform, or other
digital or analog signal analysis techniques, including,
without limitation, complex analysis, including complex phase evolution
analysis.
[0690] The frequency evaluation circuit 9212 may be structured to detect
signals at frequencies of interest. Frequencies of interest
may include frequencies higher than the frequency at which the equipment
rotates (as measured by a tachometer for instance).
Frequencies of interest may include various harmonics and/or resonant
frequencies associated with the equipment design and
operating conditions such as multiples of shaft rotation velocities or other
rotating components for the equipment that is borne by
the bearings. Changes in energy at frequencies close to the operating
frequency may be an indicator of balance/imbalance in the
system. Changes in energy at frequencies on the order of twice the operating
frequency may indicative of a system misalignment,
for example on the coupling, or a looseness in the system, e.g. rattling at
harmonics of the operating frequency. Changes in energy
at frequencies close to three or four times the operating frequency,
corresponding to the number of bolts on a coupling, may indicate
wear of on one of the couplings. Changes in energy at frequencies four or five
or more times the operating frequency may related
back to something that has corresponding number of elements, such as if there
are energy peaks or activity around five times the
operating frequency there may be wear or an imbalance in a five-vane pump of
the like.
[0691] In an illustrative and non-limiting example, in the analysis of roller
bearings, frequencies of interest may include ball spin
frequencies, cage spin frequencies, inner race frequency (as bearings often
sit on a race inside a cage), outer race frequency and the
like. Bearings which are damaged are beginning to fail may show humps of
energy at the frequencies mentioned above and
elsewhere in this disclosure. The energy at these frequencies may increase
over time as the bearings wear more and become more
damaged due to more variations in rotational acceleration, and pings
[0692] In an illustrative and non-limiting example, bad bearings may show
humps of energy and the intensity of high frequency
measurements may start to grow over time as bearings wear and become imperfect
(greater acceleration and pings may show up in
high frequency measurement domains). Those measurements may be indicators of
air gaps in the bearing system. As bearings begin
to wear, harder hits may cause the energy signal to move to higher
frequencies.
[0693] In embodiments, the signal evaluation circuit 9208 may also include one
or more of a phase detection circuit, a phase lock
loop circuit, a bandpass filter circuit, a peak detection circuit, and the
like.
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[0694] In embodiments, the signal evaluation circuit 9208 may include a
transitory signal analysis circuit. Transient signals may
cause small amplitude vibrations. However, the challenge for bearing analysis
is that you may receive a signal associated with a
single or non-periodic impact and an exponential decay. Thus, the oscillation
of the bearing may not be represented by a single sine
wave, but rather by a spectrum of many high frequency sine waves. For example,
a signal from a failing bearing may only be seen,
in a time-based signal, as a low amplitude spike for a short amount of time. A
signal from a failing bearing may be lower in amplitude
that a signal associated with an imbalance even though the consequences of a
failed bearing may be more significant it is important
to be able to identify these signals. This type of low amplitude, transient
signal may be best analyzed using transient analysis rather
than a conventional frequency transformation, such as an FFT, which would
treat the signal like a low frequency sine wave. A
higher resolution data stream may also provide additional data for the
detection of transitory signals in low speed operations. The
identification of transitory signals may enable the identification of defects
in a piece of equipment or component operating a low
RPMs.
[0695] In embodiments, the transitory signal analysis circuit for bearing
analysis may include envelope modulation analysis and
other transitory signal analysis techniques. The signal evaluation circuit
9208 may store long stream of detection values to the data
storage circuit 9216. The transitory signal analysis circuit may use envelope
analysis techniques on those long streams of detection
values to identify transient effects (such as impacts) which may not be
identified by conventional sine wave analysis (such as FFTs).
[0696] The signal evaluation circuit 9208 may utilize transitory signal
analysis models optimized for the type of component being
measured such as bearings, gears, variable speed machinery and the like. In an
illustrative and non-limiting example, a gear may
resonate close to its average rotational speed. In an illustrative and non-
limiting example, a bearing may resonate close to the bearing
rotation frequency and produce a ringing in amplitude around that frequency.
For example, if the shaft inner race is wearing there
may be chatter between the inner race and the shaft resulting in amplitude
modulation to the left and right of the bearing frequency.
The amplitude modulation may demonstrate its own sine wave characteristics
with its own side bands. Various signal processing
techniques may be used to eliminate the sinusoidal component and resulting in
a modulation envelope for analysis.
[0697] The signal evaluation circuit 9208 may be optimized for variable speed
machinery. Historically, variable speed machinery
was expensive to make, and it was common to use DC motors and variable shivs,
such that flow could be controlled using vanes.
Variable speed motors became more common with solid-state drive advances (SCR
devices). The base operating frequency of
equipment may be varied from the 50-60 Hz provided by standard utility
companies and either and slowed down or sped up to run
the equipment at different speeds depending on the application. The ability to
run the equipment at varying speeds may result in
energy savings. However, depending on the equipment geometry, there may be
some speeds which create vibrations at resonant
frequencies, reducing the life of the components. Variable speed motors may
also emit electricity into bearings which may damage
the bearings. In embodiments, the analysis of long data streams for envelope
modulation analysis and other transitory signal analysis
techniques as described herein may be useful in identifying these frequencies
such that control schemes for the equipment may be
designed to avoid those speeds which result in unacceptable vibrations and/or
damage to the bearings.
[0698] In an illustrative and non-limiting example, heating, ventilation and
air conditioning (HVAC) systems may be assembled
on site using variable speed motors, fans, belts, compressors and the like
where the operating speeds are not constant, and their
relative relationships are unknown. In an illustrative and non-limiting
example, variable speed motors may be used in fan pumps
for building air circulation. Variable speed motors may be used to vary the
speed of conveyors, for example in manufacturing
assembly lines or steel mills. Variable speed motors may be used for fans in a
pharmaceutical process, such as where it may be
critical to avoid vibration.
[0699] In an illustrative and non-limiting example, sleeve bearings may be
analyzed for defects. Sleeve bearings typically have an
oil system. If the oil flow stops or the oil becomes severely contaminated,
failure can occur very quickly. Therefore, a fluid
particulate sensor or fluid pressure sensors may be an important source of
detection values.
[0700] In an illustrative and non-limiting example, fan integrity may be
evaluated by measuring air pulsations related to blade
pass frequencies. For example, if a fan has 12 blade- 11 1te ¨sured.
Variations in the amplitude of the
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pulsations associated with the different blades may be indicative of changes
in a fan blade. Changes in frequencies associated with
the air pulsations may be indicative of bearing problems.
[0701] In an illustrative and non-limiting example, compressors used in in the
gas and oil field or in gas handling equipment on
an assembly line may be evaluated by measuring the periodic increases in
energy/pressure in the storage vessel as gas is pumped
into the vessel. Periodic variations in the amplitude of the energy increases
may be associated with piston wear or damage to a
portion of a rotary screw. Phase evaluation of the energy signal relative to
timing signals may be helpful in identifying which piston
or portion of the rotary screw has damage. Changes in frequencies associated
with the energy pulsations may be indicative of bearing
problems.
[0702] In an illustrative and non-limiting example, cavitation/air pockets in
pumps may create shuttering in the pump housing and
the output flow which may be identified with the frequency transformation and
frequency analysis techniques described above and
elsewhere herein.
[0703] In an illustrative and non-limiting example, the frequency
transformation and frequency analysis techniques described
above and elsewhere herein may assist in the identification of problems in
components of building HVAC systems such as big fans.
If the dampers of the system are set poorly it may result in ducts pulsing or
vibrating as air is pushed through the system. Monitoring
of vibration sensors on the ducts may assist in the balancing of the system.
If there are defects in the blades of the big fan this may
also result in uneven air flow and resulting pulsation in the buildings
ductwork.
[0704] In an illustrative and non-limiting example, detection values from
acoustical sensors located close to the bearings may
assist in the identification of issues in the engagement between gears or bad
bearings. Based on a knowledge of gear ratios, such as
the in and out gear ratios, for a system and measurements of the input and
output rotational speed, detection values may be evaluated
for energy occurring at those ratios, which in turn may be used to identify
bad bearings. This could be done with simple off the shelf
motors rather than requiring extensive retrofitting of the motor with sensors.
[0705] Based on the output of its various components, the signal evaluation
circuit 9208 may make a bearing life prediction,
identify a bearing health parameter, identify a bearing performance parameter,
determine a bearing health parameter (e.g. fault
conditions), and the like. The signal evaluation circuit 9208 may identify
wear on a bearing, identify the presence of foreign matter
(e.g. particulates) in the bearings, identify air gaps or a loss of fluid in
oil/fluid coated bearings, identify a loss of lubrication in a set
of bearings, identify a loss of power for magnetic bearings and the like,
identify strain/stress of flexure bearings, and the like. The
signal evaluation circuit 9208 may identify optimal operation parameters for a
piece of equipment to extend bearing life. The signal
evaluation circuit 9208 may identify behavior (resonant wobble) at a selected
operational frequency (e.g., shaft rotation rate).
[0706] The signal evaluation circuit 9208 may communicate with the data
storage circuit 9216 to access equipment specifications,
equipment geometry, bearing specifications, bearing materials, anticipated
state information for a plurality of bearing types,
operational history, historical detection values, and the like for use in
assessing the output of its various components. The signal
evaluation circuit 9208 may buffer a subset of the plurality of detection
values, intermediate data such as time-based detection values
transformed to frequency information, filtered detection values, identified
frequencies of interest, and the like for a predetermined
length of time. The signal evaluation circuit 9208 may periodically store
certain detection values in the data storage circuit 9216 to
enable the tracking of component performance over time. In embodiments, based
on relevant operating conditions and/or failure
modes that may occur as detection values approach one or more criteria, the
signal evaluation circuit 9208 may store data in the
data storage circuit 9216 based on the fit of data relative to one or more
criteria, such as those described throughout this disclosure.
Based on one sensor input meeting or approaching specified criteria or range,
the signal evaluation circuit 9208 may store additional
data such as RPMS, component loads, temperatures, pressures, vibrations or
other sensor data of the types described throughout this
disclosure in the data storage circuit 9216. The signal evaluation circuit
9208 may store data at a higher data rate for greater
granularity in future processing, the ability to reprocess at different
sampling rates, and/or to enable diagnosing or post-processing
of system information where operational data of interest is flagged, and the
like.
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[0707] Depending on the type of equipment, the component being measured, the
environment in which the equipment is operating
and the like, sensors 9206 may comprise one or more of, without limitation, a
vibration sensor, an optical vibration sensor, a
thermometer, a hygrometer, a voltage sensor, a current sensor, an
accelerometer, a velocity detector, a light or electromagnetic
sensor (e.g., determining temperature, composition and/or spectral analysis,
and/or object position or movement), an image sensor,
a structured light sensor, a laser-based image sensor, an infrared sensor, an
acoustic wave sensor, a heat flux sensor, a displacement
sensor, a turbidity meter, a viscosity meter, a load sensor, a tri-axial
vibration sensor, an accelerometer, a tachometer, a fluid pressure
meter, an air flow meter, a horsepower meter, a flow rate meter, a fluid
particle detector, an acoustical sensor, a pH sensor, and the
like, including, without limitation, any of the sensors described throughout
this disclosure and the documents incorporated by
reference. The sensors may typically comprise at least a temperature sensor, a
load sensor, a tri-axial sensor and a tachometer.
[0708] The sensors 9206 may provide a stream of data over time that has a
phase component, such as relating to acceleration or
vibration, allowing for the evaluation of phase or frequency analysis of
different operational aspects of a piece of equipment or an
operating component. The sensors 9206 may provide a stream of data that is not
conventionally phase-based, such as temperature,
humidity, load, and the like. The sensors 9206 may provide a continuous or
near continuous stream of data over time, periodic
readings, event-driven readings, and/or readings according to a selected
interval or schedule.
[0709] In embodiments, as illustrated in Figure 77, the sensors 9206 may be
part of the data monitoring device 9200, referred to
herein in some cases as a data collector, which in some cases may comprise a
mobile or portable data collector. In embodiments, as
illustrated in Figures 78 and 79, one or more external sensors 9224, which are
not explicitly part of a monitoring device 9218 but
rather are new, previously attached to or integrated into the equipment or
component, may be opportunistically connected to or
accessed by the monitoring device 9218. The monitoring device 9218 may include
a controller 9220. The controller 9220 may
include a data acquisition circuit 9222, a data storage circuit 9216, a signal
evaluation circuit 9208 and, optionally, a response circuit
9210. The signal evaluation circuit 9208 may comprise a frequency
transformation circuit 9212 and a frequency analysis circuit
9214. The data acquisition circuit 9222 may include one or more input ports
9226. The one or more external sensors 9224 may be
directly connected to the one or more input ports 9226 on the data acquisition
circuit 9222 of the controller 9220 or may be accessed
by the data acquisition circuit 9222 wirelessly, such as by a reader,
interrogator, or other wireless connection, such as over a short-
distance wireless protocol. In embodiments as shown in Figure 79, a data
acquisition circuit 9222 may further comprise a wireless
communications circuit 9212. The data acquisition circuit 9222 may use the
wireless communications circuit 9212 to access
detection values corresponding to the one or more external sensors 9224
wirelessly or via a separate source or some combination of
these methods.
[0710] In embodiments as illustrated in Figure 80, the data acquisition
circuit 9234 may further comprise a multiplexer circuit
9236 as described elsewhere herein. Outputs from the multiplexer circuit 9236
may be utilized by the signal evaluation circuit 9208.
The response circuit 9210 may have the ability to turn on and off portions of
the multiplexor circuit 9236. The response circuit 9210
may have the ability to control the control channels of the multiplexor
circuit 9236.
[0711] The response circuit 9210 may initiate actions based on a bearing
performance parameter, a bearing health value, a bearing
life prediction parameter, and the like. The response circuit 9210 may
evaluate the results of the signal evaluation circuit 9208 and,
based on certain criteria or the output from various components of the signal
evaluation circuit 9208, initiating an action. The criteria
may include a sensor's detection values at certain frequencies or phases
relative to a timer signal where the frequencies or phases
of interest may be based on the equipment geometry, equipment control schemes,
system input, historical data, current operating
conditions, and/or an anticipated response. The criteria may include a
sensor's detection values at certain frequencies or phases
relative to detection values of a second sensor. The criteria may include
signal strength at certain resonant frequencies/harmonics
relative to detection values associated with a system tachometer or
anticipated based on equipment geometry and operation
conditions. Criteria may include a predetermined peak value for a detection
value from a specific sensor, a cumulative value of a
sensor' s corresponding detection value over time, a change in peak value, a
rate of change in a peak value, and/or an accumulated
value (e.g., a time spent above/below a threshold value, a wei nhted time
snent above/below one or more threshold values, and/or an
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area of the detected value above/below one or more threshold values). The
criteria may comprise combinations of data from different
sensors such as relative values, relative changes in value, relative rates of
change in value, relative values over time, and the like.
The relative criteria may change with other data or information such as
process stage, type of product being processed, type of
equipment, ambient temperature and humidity, external vibrations from other
equipment, and the like. The relative criteria may be
reflected in one or more calculated statistics or metrics (including ones
generated by further calculations on multiple criteria or
statistics), which in turn may be used for processing (such as on board a data
collector or by an external system), such as to be
provided as an input to one or more of the machine learning capabilities
described in this disclosure, to a control system (which may
be on board a data collector or remote, such as to control selection of data
inputs, multiplexing of sensor data, storage, or the like),
or as a data element that is an input to another system, such as a data stream
or data package that may be available to a data
marketplace, a SCADA system, a remote control system, a maintenance system, an
analytic system, or other system.
[0712] Certain embodiments are described herein as detected values exceeding
thresholds or predetermined values, but detected
values may also fall below thresholds or predetermined values ¨ for example
where an amount of change in the detected value is
expected to occur, but detected values indicate that the change may not have
occurred. For example, and without limitation,
vibrational data may indicate system agitation levels, properly operating
equipment, or the like, and vibrational data below amplitude
and/or frequency thresholds may be an indication of a process that is not
operating according to expectations. Except where the
context clearly indicates otherwise, any description herein describing a
determination of a value above a threshold and/or exceeding
a predetermined or expected value is understood to include determination of a
value below a threshold and/or falling below a
predetermined or expected value.
[0713] The predetermined acceptable range may be based on anticipated system
response or vibration based on the equipment
geometry and control scheme such as number of bearings, relative rotational
speed, influx of power to the system at a certain
frequency, and the like. The predetermined acceptable range may also be based
on long term analysis of detection values across a
plurality of similar equipment and components and correlation of data with
equipment failure.
[0714] In some embodiments, an alert may be issued based on based on the some
of the criteria discussed above. In an illustrative
example, an increase in temperature and energy at certain frequencies may
indicate a hot bearing that is starting to fail. In
embodiments, the relative criteria for an alarm may change with other data or
information such as process stage, type of product
being processed on equipment, ambient temperature and humidity, external
vibrations from other equipment and the like. In an
illustrative and non-limiting example, the response circuit 9210 may initiate
an alert if a vibrational amplitude and/or frequency
exceeds a predetermined maximum value, if there is a change or rate of change
that exceeds a predetermined acceptable range,
and/or if an accumulated value based on vibrational amplitude and/or frequency
exceeds a threshold.
[0715] In embodiments, response circuit 9210 may cause the data acquisition
circuit 9234 to enable or disable the processing of
detection values corresponding to certain sensors based on the some of the
criteria discussed above. This may include switching to
sensors having different response rates, sensitivity, ranges, and the like;
accessing new sensors or types of sensors, and the like.
Switching may be undertaken based on a model, a set of rules, or the like. In
embodiments, switching may be under control of a
machine learning system, such that switching is controlled based on one or
more metrics of success, combined with input data, over
a set of trials, which may occur under supervision of a human supervisor or
under control of an automated system. Switching may
involve switching from one input port to another (such as to switch from one
sensor to another). Switching may involve altering the
multiplexing of data, such as combining different streams under different
circumstances. Switching may involve activating a system
to obtain additional data, such as moving a mobile system (such as a robotic
or drone system), to a location where different or
additional data is available (such as positioning an image sensor for a
different view or positioning a sonar sensor for a different
direction of collection) or to a location where different sensors can be
accessed (such as moving a collector to connect up to a sensor
that is disposed at a location in an environment by a wired or wireless
connection). This switching may be implemented by changing
the control signals for a multiplexor circuit 9236 and/or by turning on or off
certain input sections of the multiplexor circuit 9236.
The response circuit 9210 may make recommendations for the replacement of
certain sensors in the future with sensors having
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different response rates, sensitivity, ranges, and the like. The response
circuit 9210 may recommend design alterations for future
embodiments of the component, the piece of equipment, the operating
conditions, the process, and the like.
[0716] In embodiments, the response circuit 9210 may recommend maintenance at
an upcoming process stop or initiate a
maintenance call. The response circuit 9210 may recommend changes in process
or operating parameters to remotely balance the
piece of equipment. In embodiments, the response circuit 9210 may implement or
recommend process changes ¨ for example to
lower the utilization of a component that is near a maintenance interval,
operating off-nominally, or failed for purpose but still at
least partially operational, to change the operating speed of a component
(such as to put it in a lower-demand mode), to initiate
amelioration of an issue (such as to signal for additional lubrication of a
roller bearing set, or to signal for an alignment process for
a system that is out of balance), and the like.
[0717] In embodiments as shown in Figures 81 and 82, a data monitoring system
9240 may include at least one data monitoring
device 9250. The at least one data monitoring device 9250 may include sensors
9206 and a controller 9242 comprising a data
acquisition circuit 9204, a signal evaluation circuit 8708, a data storage
circuit 9216, and a communications circuit 9246. The signal
evaluation circuit 9208 may include at least one of a frequency transformation
circuit 9212 and a frequency analysis circuit 9214.
There may also be an optional response circuit as described above and
elsewhere herein. The signal evaluation circuit 9208 may
periodically share data with the communication circuit 9246 for transmittal to
a remote server 9244 to enable the tracking of
component and equipment performance over time and under varying conditions by
a monitoring application 9248. Because relevant
operating conditions and/or failure modes may occur in as sensor values
approach one or more criteria, the signal evaluation circuit
8708 may share data with the communication circuit 9246 for transmittal to the
remote server 9244 based on the fit of data relative
to one or more criteria. Based on one sensor input meeting or approaching
specified criteria or range, the signal evaluation circuit
8708 may share additional data such as RPMS, component loads, temperatures,
pressures, vibrations, and the like for transmittal.
The signal evaluation circuit 8708 may share data at a higher data rate for
transmittal to enable greater granularity in processing on
the remote server.
[0718] In embodiments as shown in Figure 81, the communications circuit 9246
may communicated data directly to a remote
server 9244. In embodiments as shown in Figure 82, the communications circuit
9246 may communicate data to an intermediate
computer 9252 which may include a processor 9254 running an operating system
9256 and a data storage circuit 9258. The
intermediate computer 9252 may collect data from a plurality of data
monitoring devices and send the cumulative data to the remote
server 9244.
[0719] In embodiments as illustrated in Figures 83 and 84, a data collection
system 9260 may have a plurality of monitoring
devices 9250 collecting data on multiple components in a single piece of
equipment, collecting data on the same component across
a plurality of pieces of equipment, (both the same and different types of
equipment) in the same facility as well as collecting data
from monitoring devices in multiple facilities. A monitoring application 9248
on a remote server 9244 may receive and store one
or more of detection values, timing signals and data coming from a plurality
of the various monitoring devices 9250. In embodiments
as shown in Figure 83, the communications circuit 9246 may communicated data
directly to a remote server 9244. In embodiments
as shown in Figure 84, the communications circuit 9246 may communicate data to
an intermediate computer 9252 which may
include a processor 9254 running an operating system 9256 and a data storage
circuit 9258. There may be an individual intermediate
computer 9252 associated with each monitoring device 9264 or an individual
intermediate computer 9252 may be associated with
a plurality of monitoring devices 9250 where the intermediate computer 9252
may collect data from a plurality of data monitoring
devices and send the cumulative data to the remote server 9244.
[0720] The monitoring application 9248 may select subsets of the detection
values, timing signals and data to jointly analyzed.
Subsets for analysis may be selected based on a bearing type, bearing
materials, a single type of equipment in which a bearing is
operating. Subsets for analysis may be selected or grouped based on common
operating conditions or operational history such as
size of load, operational condition (e.g. intermittent, continuous), operating
speed or tachometer, common ambient environmental
conditions such as humidity, temperature, air or fluid particulate. and the
like. Subsets for analysis may be selected based on common
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anticipated state information. Subsets for analysis may be selected based on
the effects of other nearby equipment such as nearby
machines rotating at similar frequencies, nearby equipment producing
electromagnetic fields, nearby equipment producing heat,
nearby equipment inducing movement or vibration, nearby equipment emitting
vapors, chemicals or particulates, or other potentially
interfering or intervening effects.
[0721] The monitoring application 9248 may analyze a selected subset. In an
illustrative example, data from a single component
may be analyzed over different time periods such as one operating cycle, cycle
to cycle comparisons, trends over several operating
cycles/time such as a month, a year, the life of the component or the like.
Data from multiple components of the same type may also
be analyzed over different time periods. Trends in the data such as changes in
frequency or amplitude may be correlated with failure
and maintenance records associated with the same component or piece of
equipment. Trends in the data such as changing rates of
change associated with start-up or different points in the process may be
identified. Additional data may be introduced into the
analysis such as output product quality, output quantity (such as per unit of
time), indicated success or failure of a process, and the
like. Correlation of trends and values for different types of data may be
analyzed to identify those parameters whose short-term
analysis might provide the best prediction regarding expected performance. The
analysis may identify model improvements to the
model for anticipated state information, recommendations around sensors to be
used, positioning of sensors and the like. The
analysis may identify additional data to collect and store. The analysis may
identify recommendations regarding needed maintenance
and repair and/or the scheduling of preventative maintenance. The analysis may
identify recommendations around purchasing
replacement bearings and the timing of the replacement of the bearings. The
analysis may result in warning regarding dangerous of
catastrophic failure conditions. This information may be transmitted back to
the monitoring device to update types of data collected
and analyzed locally or to influence the design of future monitoring devices.
[0722] In embodiments, the monitoring application 9248 may have access to
equipment specifications, equipment geometry,
bearing specifications, bearing materials, anticipated state information for a
plurality of bearing types, operational history, historical
detection values, bearing life models and the like for use analyzing the
selected subset using rule-based or model-based analysis. In
embodiments, the monitoring application 9248 may feed a neural net with the
selected subset to learn to recognize various operating
state, health states (e.g. lifetime predictions) and fault states utilizing
deep learning techniques. In embodiments, a hybrid of the two
techniques (model-based learning and deep learning) may be used.
[0723] In an illustrative and non-limiting example, bearing health on
conveyors and lifters in an assembly line may be monitored
using the frequency transformation and frequency analysis techniques, data
monitoring devices and data collection systems
described herein.
[0724] In an illustrative and non-limiting example, the health of bearings in
water pumps on industrial vehicles may be monitored
using the frequency transformation and frequency analysis techniques, data
monitoring devices and data collection systems
described herein.
[0725] In an illustrative and non-limiting example, the health of bearings in
compressors in gas handling systems may be monitored
using the frequency transformation and frequency analysis techniques, data
monitoring devices and data collection systems
described herein.
[0726] In an illustrative and non-limiting example, the health of bearings in
compressors situated out in the gas and oil fields may
be monitored using the frequency transformation and frequency analysis
techniques, data monitoring devices and data collection
systems described herein.
[0727] In an illustrative and non-limiting example, the health of bearings in
factory air conditioning units may be evaluated using
the frequency transformation and frequency analysis techniques, data
monitoring devices and data collection systems described
herein.
[0728] In an illustrative and non-limiting example, the health of bearings in
factory mineral pumps may be evaluated using the
frequency transformation and frequency analysis techniques, data monitoring
devices and data collection systems described herein.
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[0729] In an illustrative and non-limiting example, the health of bearings and
gears in drilling machines and screw drivers situated
in the oil and gas fields may be evaluated using the frequency transformation
and frequency analysis techniques, data monitoring
devices and data collection systems described herein.
[0730] In an illustrative and non-limiting example, the health of bearings,
gears and rotors of motors situated in the oil and gas
fields may be evaluated using the frequency transformation and frequency
analysis techniques, data monitoring devices and data
collection systems described herein.
[0731] In an illustrative and non-limiting example, the health of bearings,
blades, screws and other components of pumps situated
in the oil and gas fields may be evaluated using the frequency transformation
and frequency analysis techniques, data monitoring
devices and data collection systems described herein.
[0732] In an illustrative and non-limiting example, the health of bearings,
gears and other components of gearboxes situated in the
oil and gas fields may be evaluated using the frequency transformation and
frequency analysis techniques, data monitoring devices
and data collection systems described herein.
[0733] In an illustrative and non-limiting example, the health of bearings and
associated shafts, motors, rotors, stators, gears and
other components of vibrating conveyors situated in the oil and gas fields may
be evaluated using the frequency transformation and
frequency analysis techniques, data monitoring devices and data collection
systems described herein.
[0734] In an illustrative and non-limiting example, the health of bearings and
associated shafts, motors, rotors, stators, gears and
other components of mixers situated in the oil and gas fields may be evaluated
using the frequency transformation and frequency
analysis techniques, data monitoring devices and data collection systems
described herein.
[0735] In an illustrative and non-limiting example, the health of bearings and
associated shafts, motors, rotors, stators, gears and
other components of centrifuges situated in oil and gas refineries may be
evaluated using the frequency transformation and frequency
analysis techniques, data monitoring devices and data collection systems
described herein.
[0736] In an illustrative and non-limiting example, the health of bearings and
associated shafts, motors, rotors, stators, gears and
other components of refining tanks situated in oil and gas refineries may be
evaluated using the frequency transformation and
frequency analysis techniques, data monitoring devices and data collection
systems described herein.
[0737] In an illustrative and non-limiting example, the health of bearings and
associated shafts, motors, rotors, stators, gears and
other components of rotating tank/mixer agitators to promote chemical
reactions deployed in chemical and pharmaceutical
production lines may be evaluated using the frequency transformation and
frequency analysis techniques, data monitoring devices
and data collection systems described herein.
[0738] In an illustrative and non-limiting example, the health of bearings and
associated shafts, motors, rotors, stators, gears and
other components of mechanical/rotating agitators to promote chemical
reactions deployed in chemical and pharmaceutical
production lines may be evaluated using the frequency transformation and
frequency analysis techniques, data monitoring devices
and data collection systems described herein.
[0739] In an illustrative and non-limiting example, the health of bearings and
associated shafts, motors, rotors, stators, gears and
other components of propeller agitators to promote chemical reactions deployed
in chemical and pharmaceutical production lines
may be evaluated using the frequency transformation and frequency analysis
techniques, data monitoring devices and data collection
systems described herein.
[0740] In an illustrative and non-limiting example, the health of bearings and
associated shafts, motors, rotors, stators, gears and
other components of vehicle steering mechanisms may be evaluated using the
frequency transformation and frequency analysis
techniques, data monitoring devices and data collection systems described
herein.
[0741] In an illustrative and non-limiting example, the health of bearings and
associated shafts, motors, rotors, stators, gears and
other components of vehicle engines may be evaluated using the frequency
transformation and frequency analysis techniques, data
monitoring devices and data collection systems described herein.
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[0742] 1. A monitoring device for bearing analysis in an industrial
environment, the monitoring device comprising:
a data acquisition circuit structured to interpret a plurality of detection
values, each of the plurality of detection values corresponding
to at least one of a plurality of input sensors communicatively coupled to the
data acquisition circuit;
a data storage for storing specifications and anticipated state information
for a plurality of bearing types and buffering the plurality
of detection values for a predetermined length of time; and
a bearing analysis circuit structured to analyze buffered detection values
relative to specifications and anticipated state information
resulting in a bearing performance parameter.
[0743] 2. The monitoring device of claim 1, further comprising a response
circuit to perform at least one operation in response to
the bearing performance parameter, wherein the plurality of input sensors
includes at least two sensors selected from the group
consisting of a temperature sensor, a load sensor, an optical vibration
sensor, an acoustic wave sensor, a heat flux sensor, an infrared
sensor, an accelerometer, a tri-axial vibration sensor and a tachometer.
[0744] 3. The monitoring device of claim 2, wherein the at least one operation
is further in response to at least one of: a change in
amplitude of at least one of the plurality of detection values; a change in
frequency or relative phase of at least one of the plurality
of detection values; a rate of change in both amplitude and relative phase of
at least one the plurality of detection values; and a
relative rate of change in amplitude and relative phase of at least one the
plurality of detection values.
[0745] 4. The monitoring device of claim 2, wherein the at least one operation
comprises issuing an alert.
[0746] 5. The monitoring device of claim 4, wherein the alert may be one of
haptic, audible and visual.
[0747] 6. The monitoring device of claim 2 wherein the at least one operation
further comprises storing additional data in the data
storage circuit.
[0748] 7. The monitoring device of claim 6, wherein the storing additional
data in the data storage circuit is further in response to
at least one of: a change in the relative phase difference and a relative rate
of change in the relative phase difference.
[0749] 8. A monitoring device for bearing analysis in an industrial
environment, the monitoring device comprising:
a data acquisition circuit structured to interpret a plurality of detection
values, each of the plurality of detection values corresponding
to at least one of a plurality of input sensors communicatively coupled to the
data acquisition circuit;
a data storage for storing specifications and anticipated state information
for a plurality of bearing types and buffering the plurality
of detection values for a predetermined length of time; and
a bearing analysis circuit structured to analyze buffered detection values
relative to specifications and anticipated state information
resulting in a bearing health value.
[0750] 9. The monitoring device of claim 8, further comprising a response
circuit to perform at least one operation in response to
the bearing health value, wherein the plurality of input sensors includes at
least two sensors selected from the group consisting of a
temperature sensor, a load sensor, an optical vibration sensor, an acoustic
wave sensor, a heat flux sensor, an infrared sensor, an
accelerometer, a tri-axial vibration sensor and a tachometer.
[0751] 10. The monitoring device of claim 9, wherein the at least one
operation is further in response to at least one of: a change
in amplitude of at least one of the plurality of detection values; a change in
frequency or relative phase of at least one of the plurality
of detection values; a rate of change in both amplitude and relative phase of
at least one the plurality of detection values; and a
relative rate of change in amplitude and relative phase of at least one the
plurality of detection values.
[0752] 11. The monitoring device of claim 9, wherein the at least one
operation comprises issuing an alert.
[0753] 12. The monitoring device of claim 11, wherein the alert may be one of
haptic, audible and visual.
[0754] 13. The monitoring device of claim 9 wherein the at least one operation
further comprises storing additional data in the
data storage circuit.
[0755] 14. The monitoring device of claim 13, wherein the storing additional
data in the data storage circuit is further in response
to at least one of: a change in the relative phase difference and a relative
rate of change in the relative phase difference.
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[0756] 15. A monitoring device for bearing analysis in an industrial
environment, the monitoring device comprising:
a data acquisition circuit structured to interpret a plurality of detection
values, each of the plurality of detection values corresponding
to at least one of a plurality of input sensors communicatively coupled to the
data acquisition circuit;
a data storage for storing specifications and anticipated state information
for a plurality of bearing types and buffering the plurality
of detection values for a predetermined length of time; and
a bearing analysis circuit structured to analyze buffered detection values
relative to specifications and anticipated state information
resulting in a bearing life prediction parameter.
[0757] 16. The monitoring device of claim 15, further comprising a response
circuit to perform at least one operation in response
to the bearing life prediction parameter, wherein the plurality of input
sensors includes at least two sensors selected from the group
consisting of a temperature sensor, a load sensor, an optical vibration
sensor, an acoustic wave sensor, a heat flux sensor, an infrared
sensor, an accelerometer, a tri-axial vibration sensor and a tachometer.
[0758] 17. The monitoring device of claim 16, wherein the at least one
operation is further in response to at least one of: a change
in amplitude of at least one of the plurality of detection values; a change in
frequency or relative phase of at least one of the plurality
of detection values; a rate of change in both amplitude and relative phase of
at least one the plurality of detection values; and a
relative rate of change in amplitude and relative phase of at least one the
plurality of detection values.
[0759] 18. The monitoring device of claim 16, wherein the at least one
operation comprises issuing an alert.
[0760] 19. The monitoring device of claim 18, wherein the alert may be one of
haptic, audible and visual.
[0761] 20. The monitoring device of claim 16 wherein the at least one
operation further comprises storing additional data in the
data storage circuit.
[0762] 21. The monitoring device of claim 20, wherein the storing additional
data in the data storage circuit is further in response
to at least one of: a change in the relative phase difference and a relative
rate of change in the relative phase difference.
[0763] 22. A monitoring device for bearing analysis in an industrial
environment, the monitoring device comprising:
a data acquisition circuit structured to interpret a plurality of detection
values, each of the plurality of detection values corresponding
to at least one of a plurality of input sensors communicatively coupled to the
data acquisition circuit;
a data storage for storing specifications and anticipated state information
for a plurality of bearing types and buffering the plurality
of detection values for a predetermined length of time; and
a bearing analysis circuit structured to analyze buffered detection values
relative to specifications and anticipated state information
resulting in a bearing performance parameter, wherein the data acquisition
circuit comprises a multiplexer circuit whereby
alternative combinations of the detection values may be selected based on at
least one of user input, a detected state and a selected
operating parameter for a machine.
[0764] 23. The monitoring device of claim 22, further comprising a response
circuit to perform at least one operation in response
to the bearing performance parameter, wherein the plurality of input sensors
includes at least two sensors selected from the group
consisting of a temperature sensor, a load sensor, an optical vibration
sensor, an acoustic wave sensor, a heat flux sensor, an infrared
sensor, an accelerometer, a tri-axial vibration sensor and a tachometer.
[0765] 24. The monitoring device of claim 23, wherein the at least one
operation is further in response to at least one of: a change
in amplitude of at least one of the plurality of detection values; a change in
frequency or relative phase of at least one of the plurality
of detection values; a rate of change in both amplitude and relative phase of
at least one the plurality of detection values; and a
relative rate of change in amplitude and relative phase of at least one the
plurality of detection values.
[0766] 25. The monitoring device of claim 23, wherein the at least one
operation comprises issuing an alert.
[0767] 26. The monitoring device of claim 25, wherein the alert may be one of
haptic, audible and visual.
[0768] 27. The monitoring device of claim 23 wherein the at least one
operation further comprises storing additional data in the
data storage circuit.
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[0769] 28. The monitoring device of claim 27, wherein the storing additional
data in the data storage circuit is further in response
to at least one of: a change in the relative phase difference and a relative
rate of change in the relative phase difference.
[0770] 29. The monitoring device of claim 22, wherein the at least one
operation comprises enabling or disabling one or more
portions of the multiplexer circuit, or altering the multiplexer control
lines.
[0771] 30. The monitoring device of claim 22, wherein the data acquisition
circuit comprises at least two multiplexer circuits and
the at least one operation comprises changing connections between the at least
two multiplexer circuits.
[0772] 31. A system for data collection, processing, and bearing analysis in
an industrial environment comprising:
a plurality of monitoring devices, each monitoring device comprising:
a data acquisition circuit structured to interpret a plurality of detection
values, each of the plurality of detection values corresponding
to at least one of a plurality of input sensors communicatively coupled to the
data acquisition circuit;
a data storage for storing specifications and anticipated state information
for a plurality of bearing types and buffering the plurality
of detection values for a predetermined length of time;
a bearing analysis circuit structured to analyze buffered detection values
relative to specifications and anticipated state information
resulting in a bearing life prediction;
a communication circuit structured to communicate with a remote server
providing the bearing life prediction and a portion of the
buffered detection values to the remote server; and
a monitoring application on the remote server structured to receive, store and
jointly analyze a subset of the detection values from
the plurality of monitoring devices.
[0773] 32. The monitoring device of claim 31, further comprising a response
circuit to perform at least one operation in response
to the bearing life prediction, wherein the plurality of input sensors
includes at least two sensors selected from the group consisting
of a temperature sensor, a load sensor, an optical vibration sensor, an
acoustic wave sensor, a heat flux sensor, an infrared sensor,
an accelerometer, a tri-axial vibration sensor and a tachometer.
[0774] 33. The monitoring device of claim 32, wherein the at least one
operation is further in response to at least one of: a change
in amplitude of at least one of the plurality of detection values; a change in
frequency or relative phase of at least one of the plurality
of detection values; a rate of change in both amplitude and relative phase of
at least one the plurality of detection values; and a
relative rate of change in amplitude and relative phase of at least one the
plurality of detection values.
[0775] 34. The monitoring device of claim 32, wherein the at least one
operation comprises issuing an alert.
[0776] 35. The monitoring device of claim 34, wherein the alert may be one of
haptic, audible and visual.
[0777] 36. The monitoring device of claim 32 wherein the at least one
operation further comprises storing additional data in the
data storage circuit.
[0778] 37. The monitoring device of claim 36, wherein the storing additional
data in the data storage circuit is further in response
to at least one of: a change in the relative phase difference and a relative
rate of change in the relative phase difference.
[0779] 38. A system for data collection, processing, and bearing analysis in
an industrial environment comprising:
a plurality of monitoring devices, each comprising:
a data acquisition circuit structured to interpret a plurality of detection
values, each of the plurality of detection values corresponding
to at least one of a plurality of input sensors communicatively coupled to the
data acquisition circuit;
a data storage for storing specifications and anticipated state information
for a plurality of bearing types and buffering the plurality
of detection values for a predetermined length of time;
a bearing analysis circuit structured to analyze buffered detection values
relative to specifications and anticipated state information
resulting in a bearing performance parameter;
a communication circuit structured to communicate with a remote server
providing the life prediction and a portion of the buffered
detection values to the remote server; and
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a monitoring application on the remote server structured to receive, store and
jointly analyze a subset of the detection values from
the plurality of monitoring devices.
[0780] 39. The monitoring device of claim 38, further comprising a response
circuit to perform at least one operation in response
to the bearing performance parameter, wherein the plurality of input sensors
includes at least two sensors selected from the group
consisting of a temperature sensor, a load sensor, an optical vibration
sensor, an acoustic wave sensor, a heat flux sensor, an infrared
sensor, an accelerometer, a tri-axial vibration sensor and a tachometer.
[0781] 40. The monitoring device of claim 39, wherein the at least one
operation is further in response to at least one of: a change
in amplitude of at least one of the plurality of detection values; a change in
frequency or relative phase of at least one of the plurality
of detection values; a rate of change in both amplitude and relative phase of
at least one the plurality of detection values; and a
relative rate of change in amplitude and relative phase of at least one the
plurality of detection values.
[0782] 41. The monitoring device of claim 39, wherein the at least one
operation comprises issuing an alert.
[0783] 42. The monitoring device of claim 41, wherein the alert may be one of
haptic, audible and visual.
[0784] 43. The monitoring device of claim 39 wherein the at least one
operation further comprises storing additional data in the
data storage circuit.
[0785] 44. The monitoring device of claim 43, wherein storing additional data
in the data storage circuit is further in response to
at least one of: a change in the relative phase difference and a relative rate
of change in the relative phase difference.
[0786] 45. A system for data collection, processing, and bearing analysis in
an industrial environment comprising:
a plurality of monitoring devices, each monitoring device comprising:
a data acquisition circuit structured to interpret a plurality of detection
values, each of the plurality of detection values corresponding
to at least one of a plurality of input sensors communicatively coupled to the
data acquisition circuit;
a streaming circuit for streaming at least a subset of the acquired detection
values to a remote learning system; and
a remote learning system including a bearing analysis circuit structured to
analyze the detection values relative to a machine-based
understanding of the state of the at least one bearing.
[0787] 46. The system of claim 45, wherein the machine-based understanding is
developed based on a model of the bearing that
determines a state of the at least one bearing based at least in part on the
relationship of the behavior of the bearing to an operating
frequency of a component of the industrial machine.
[0788] 47. The system of claim 46, wherein the state of the at least one
bearing is at least one of an operating state, a health state,
a predicted lifetime state and a fault state.
[0789] 48. The system of claim 45, wherein the machine-based understanding is
developed based by providing inputs to a deep
learning machine, wherein the inputs comprise a plurality of streams of
detection values for a plurality of bearings and a plurality
of measured state values for the plurality of bearings.
[0790] 49. The system of claim 48, wherein the state of the at least one
bearing is at least one of an operating state, a health state,
a predicted lifetime state and a fault state.
[0791] 50. A method of analyzing bearings and sets of bearings, the method
comprising:
receiving a plurality of detection values corresponding to data from a
temperature sensor, a vibration sensor positioned near the
bearing or set of bearings and a tachometer to measure rotation of a shaft
associated with the bearing or set of bearings;
comparing the detection values corresponding to the temperature sensor to a
predetermined maximum level;
filtering the detection values corresponding to the vibration sensor through a
high pass filter where the filter is selected to eliminate
vibrations associated with detection values associated with the tachometer;
identifying rapid changes in at least one of a temperature peak and a
vibration peak;
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identifying frequencies at which spikes in the filtered detection values
corresponding to the vibration sensor occur and comparing
frequencies and spikes in amplitude relative to an anticipated state
information and specification associated with the bearing or set
of bearings; and
determining a bearing health parameter.
[0792] 51. A device for monitoring roller bearings in an industrial
environment, the device comprising:
a data acquisition circuit structured to interpret a plurality of detection
values, each of the plurality of detection values corresponding
to at least one of a plurality of input sensors communicatively coupled to the
data acquisition circuit;
a data storage circuit structured to store specifications and anticipated
state information for a plurality of types of roller bearings
and buffering the plurality of detection values for a predetermined length of
time;
a bearing analysis circuit structured to analyze buffered detection values
relative to specifications and anticipated state information
resulting in a bearing performance parameter; and
a response circuit to perform at least one operation in response to the
bearing performance prediction, wherein the plurality of input
sensors includes at least two sensors selected from the group consisting of a
temperature sensor, a load sensor, an optical vibration
sensor, an acoustic wave sensor, a heat flux sensor, an infrared sensor, an
accelerometer, a tri-axial vibration sensor and a tachometer.
[0793] 52. A device for monitoring sleeve bearings in an industrial
environment, the device comprising:
a data acquisition circuit structured to interpret a plurality of detection
values, each of the plurality of detection values corresponding
to at least one of a plurality of input sensors communicatively coupled to the
data acquisition circuit;
a data storage for storing sleeve bearing specifications and anticipated state
information for types of sleeve bearings and buffering
the plurality of detection values for a predetermined length of time;
a bearing analysis circuit structured to analyze buffered detection values
relative to specifications and anticipated state information
resulting in a bearing performance parameter; and
a response circuit to perform at least one operation in response to the
bearing performance parameter, wherein the plurality of input
sensors includes at least two sensors selected from the group consisting of a
temperature sensor, a load sensor, an optical vibration
sensor, an acoustic wave sensor, a heat flux sensor, an infrared sensor, an
accelerometer, a tri-axial vibration sensor and a tachometer.
[0794] 53. A system for monitoring pump bearings in an industrial environment,
the system comprising:
a data acquisition circuit structured to interpret a plurality of detection
values, each of the plurality of detection values corresponding
to at least one of a plurality of input sensors communicatively coupled to the
data acquisition circuit;
a data storage for storing pump specifications, bearing specifications,
anticipated state information for pump bearings and buffering
the plurality of detection values for a predetermined length of time;
a bearing analysis circuit structured to analyze buffered detection values
relative to specifications and anticipated state information
resulting in a bearing performance parameter; and
a response circuit to perform at least one operation in response to the
bearing performance parameter, wherein the plurality of input
sensors includes at least two sensors selected from the group consisting of a
temperature sensor, a load sensor, an optical vibration
sensor, an acoustic wave sensor, a heat flux sensor, an infrared sensor, an
accelerometer, a tri-axial vibration sensor and a tachometer.
[0795] 54. A system for collection, processing, and analyzing pump bearings in
an industrial environment comprising:
a plurality of monitoring devices, each comprising:
a data acquisition circuit structured to interpret a plurality of detection
values, each of the plurality of detection values corresponding
to at least one of a plurality of input sensors communicatively coupled to the
data acquisition circuit;
a data storage for storing pump specifications, bearing specifications,
anticipated state information for pump bearings and buffering
the plurality of detection values for a predetermined length of time;
a bearing analysis circuit structured to analyze buffered detection values
relative to the pump and bearing specifications and
anticipated state information resulting in a bearing performance parameter;
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a communication circuit structured to communicate with a remote server
providing the bearing performance parameter and a portion
of the buffered detection values to the remote server; and
a monitoring application on the remote server structured to receive, store and
jointly analyze a subset of the detection values from
the plurality of monitoring devices.
[0796] 55. A system for estimating a conveyor health parameter, the system
comprising:
a data acquisition circuit structured to interpret a plurality of detection
values, each of the plurality of detection values corresponding
to at least one of a plurality of input sensors, wherein the plurality of
input sensors comprises at least one of an angular position
sensor, an angular velocity sensor and an angular acceleration sensor
positioned to measure the rotating component;
a data storage circuit structured to store specifications, system geometry,
and anticipated state information for the conveyor and
associated rotating components, store historical conveyor and component
performance and buffer the plurality of detection values
for a predetermined length of time;
a bearing analysis circuit structured to analyze buffered detection values
relative to specifications and anticipated state information
resulting in a bearing performance parameter; and
a system analysis circuit structured to utilize the bearing performance and at
least one of an anticipated state, historical data and a
system geometry to estimate a conveyor health performance.
[0797] 56. A system for estimating an agitator health parameter, the system
comprising:
a data acquisition circuit structured to interpret a plurality of detection
values, each of the plurality of detection values corresponding
to at least one of a plurality of input sensors, wherein the plurality of
input sensors comprises at least one of an angular position
sensor, an angular velocity sensor and an angular acceleration sensor
positioned to measure the rotating component;
a data storage circuit structured to store specifications, system geometry,
and anticipated state information for the agitator and
associated components, store historical agitator and component performance and
buffer the plurality of detection values for a
predetermined length of time;
a bearing analysis circuit structured to analyze buffered detection values
relative to specifications and anticipated state information
resulting in a bearing performance parameter; and
a system analysis circuit structured to utilize the bearing performance and at
least one of an anticipated state, historical data and a
system geometry to estimate an agitation health parameter.
[0798] 57. The device of claim 56 where the agitator is one of a rotating tank
mixer, a large tank mixer, a portable tank mixers, a
tote tank mixer, a drum mixer, a mounted mixer and a propeller mixer.
[0799] 58. A system for estimating a vehicle steering system performance
parameter, the system comprising:
a data acquisition circuit structured to interpret a plurality of detection
values, each of the plurality of detection values corresponding
to at least one of a plurality of input sensors, wherein the plurality of
input sensors comprises at least one of an angular position
sensor, an angular velocity sensor and an angular acceleration sensor
positioned to measure the rotating component;
a data storage circuit structured to store specifications, system geometry,
and anticipated state information for the vehicle steering
system, the rack, the pinion, and the steering column, store historical
steering system performance and buffer the plurality of
detection values for a predetermined length of time;
a bearing analysis circuit structured to analyze buffered detection values
relative to specifications and anticipated state information
resulting in a bearing performance parameter; and
a system analysis circuit structured to utilize the bearing performance and at
least one of an anticipated state, historical data and a
system geometry to estimate a vehicle steering system performance parameter.
[0800] 59. A system for estimating a pump performance parameter, the system
comprising:
a data acquisition circuit structured to interpret a plurality of detection
values, each of the plurality of detection values corresponding
to at least one of a plurality of input sensors, wherein the plurality of
input sensors comprises at least one of an angular position
sensor, an angular velocity sensor and an angular acceleration sensor
nositioned to measure the rotating component;
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a data storage circuit structured to store specifications, system geometry,
and anticipated state information for the pump and pump
components, store historical steering system performance and buffer the
plurality of detection values for a predetermined length of
time;
a bearing analysis circuit structured to analyze buffered detection values
relative to specifications and anticipated state information
resulting in a bearing performance parameter;
a system analysis circuit structured to utilize the bearing performance and at
least one of an anticipated state, historical data and a
system geometry to estimate a pump performance parameter.
[0801] 60. The system of claim 59, wherein the pump is a water pump in a car.
[0802] 61. The system of claim 59, wherein the pump is a mineral pump.
[0803] 62. A system for estimating a performance parameter for a drilling
machine, the system comprising:
a data acquisition circuit structured to interpret a plurality of detection
values, each of the plurality of detection values corresponding
to at least one of a plurality of input sensors, wherein the plurality of
input sensors comprises at least one of an angular position
sensor, an angular velocity sensor and an angular acceleration sensor
positioned to measure the rotating component;
a data storage circuit structured to store specifications, system geometry,
and anticipated state information for the drilling machine
and drilling machine components, store historical drilling machine performance
and buffer the plurality of detection values for a
predetermined length of time;
a bearing analysis circuit structured to analyze buffered detection values
relative to specifications and anticipated state information
resulting in a bearing performance parameter; and
a system analysis circuit structured to utilize the bearing performance and at
least one of an anticipated state, historical data and a
system geometry to estimate a performance parameter for the drilling machine.
[0804] 63. The system of claim 62, wherein the drilling machine is one of an
oil drilling machine and a gas drilling machine.
[0805] 64. A system for estimating a performance parameter for a drilling
machine, the system comprising:
a data acquisition circuit structured to interpret a plurality of detection
values, each of the plurality of detection values corresponding
to at least one of a plurality of input sensors, wherein the plurality of
input sensors comprises at least one of an angular position
sensor, an angular velocity sensor and an angular acceleration sensor
positioned to measure the rotating component;
a data storage circuit structured to store specifications, system geometry,
and anticipated state information for the drilling machine
and drilling machine components, store historical drilling machine performance
and buffer the plurality of detection values for a
predetermined length of time;
a bearing analysis circuit structured to analyze buffered detection values
relative to specifications and anticipated state information
resulting in a bearing performance parameter; and
a system analysis circuit structured to utilize bearing performance and at
least one of an anticipated state, historical data and a system
geometry to estimate a performance parameter for the drilling machine.
[0806] Rotating components are used throughout many different types of
equipment and applications. Rotating components may
include shafts, motors, rotors, stators, bearings, fins, vanes, wings, blades,
fans, bearings, wheels, hubs, spokes, balls, rollers, pins,
gears and the like. In embodiments, information about the health or other
status or state information of or regarding a rotating
component in a piece of industrial equipment or in an industrial process may
be obtained by monitoring the condition of the
component or various other components of the industrial equipment or
industrial process and identifying torsion on the component.
Monitoring may include monitoring the amplitude and phase of a sensor signal,
such as one measuring attributes such as angular
position, angular velocity, angular acceleration, and the like.
[0807] An embodiment of a data monitoring device 9400 is shown in Figure 85
and may include a plurality of sensors 9406
communicatively coupled to a controller 9402. The controller 9402 may include
a data acquisition circuit 9404, a data storage circuit
9414, a system evaluation circuit 9408 and, optionally, a response circuit
9410. The system evaluation circuit 9408 may comprise a
torsion analysis circuit 9412.
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[0808] The plurality of sensors 9406 may be wired to ports on the data
acquisition circuit 9404. The plurality of sensors 9406 may
be wirelessly connected to the data acquisition circuit 9404. The data
acquisition circuit 9404 may be able to access detection values
corresponding to the output of at least one of the plurality of sensors 9406
where the sensors 9406 may be capturing data on different
operational aspects of a bearing or piece of equipment or infrastructure.
[0809] The selection of the plurality of sensors 9406 for a data monitoring
device 9400 designed to assess torsion on a component,
such as a shaft, motor, rotor, stator, bearing or gear, or other component
described herein, or a combination of components, such as
within or comprising a drive train or piece of equipment or system, may depend
on a variety of considerations such as accessibility
for installing new sensors, incorporation of sensors in the initial design,
anticipated operational and failure conditions, reliability of
the sensors, and the like. The impact of failure may drive the extent to which
a bearing or piece of equipment is monitored with
more sensors and/or higher capability sensors being dedicated to systems where
unexpected or undetected bearing failure would be
costly or have severe consequences. To assess torsion the sensors may include,
among other options, an angular position sensor
and/or an angular velocity sensor and/or an angular acceleration sensor.
[0810] Referring to Figure 85, a system evaluation circuit 9408 may process
the detection values to obtain information about one
or more rotating components being monitored using a torsional analysis circuit
9412 structured to identify torsion in a component
or system, such as based on anticipated state, historical state, system
geometry and the like, such as available from the data storage
circuit 9414. The torsional analysis circuit 9412 may be structured to
identify torsion using a variety of techniques such as amplitude,
phase and frequency differences in the detection values from two linear
accelerometers positioned at different locations on a shaft.
The torsional analysis circuit 9412 may identify torsion using difference in
amplitude and phase between an angular accelerometer
on a shaft and an angular accelerometer on a slip ring on the end of the
shaft. The torsional analysis circuit 9412 may identify shear
stress/elongation on a component using two strain gauges in a half bridge
configuration or four strain gauges in a full bridge
configuration. The torsional analysis circuit 9412 may use coder based
techniques such as markers to identify the rotation of a shaft,
bearing, rotor, stator, gear or other rotating component. The markers being
assessed may include visual markers such as gear teeth
or stripes on a shaft captured by an image sensor, light detector or the like.
The markers being assessed may include magnetic
components located on the rotating component and sensed by an electromagnetic
pickup. The sensor may be a Hall Effect sensor.
[0811] Additional input sensors may include a thermometer, a heat flux sensor,
a magnetometer, an axial load sensor, a radial load
sensor, an accelerometer, a shear-stress torque sensor, a twist angle sensor
and the like. Twist angle may include rotational
information at two positions on shaft or an angular velocity or angular
acceleration at two positions on a shaft. In embodiments, the
sensors may be positioned at different ends of the shaft.
[0812] The torsional analysis circuit 9412 may include one or more of a
transient signal analysis circuit and/or a frequency
transformation circuit and/or a frequency analysis circuit as described
elsewhere herein.
[0813] In embodiments, the transitory signal analysis circuit for torsional
analysis may include envelope modulation analysis, and
other transitory signal analysis techniques. The system evaluation circuit
9408 may store long stream of detection values to the data
storage circuit 9414. The transitory signal analysis circuit may use envelope
analysis techniques on those long streams of detection
values to identify transient effects (such as impacts) which may not be
identified by conventional sine wave analysis (such as FFTs).
[0814] In embodiments, the frequencies of interest may include identifying
energy at relation-order bandwidths for rotating
equipment. The maximum order observed may comprise a function of the bandwidth
of the system and the rotational speed of the
component. For varying speeds (run-ups, run-downs, etc.), the minimum RPM may
determine the maximum-observed order. In
embodiments, there may be torsional resonance at harmonics of the forcing
frequency/frequency at which a component is being
driven.
[0815] In an illustrative and non-limiting example, the monitoring device may
be used to collect and process sensor data to measure
torsion on a component. The monitoring device may be in communication with or
include a high resolution, high speed vibration
sensor to collect data over an extended period of time, enough to measure
multiple cycles of rotation. For gear driven equipment,
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the sampling resolution should be such that the number of samples taken per
cycle is at least equal to the number of gear teeth
driving the component. It will be understood that a lower sampling resolution
may also be utilized, which may result in a lower
confidence determination and/or taking data over a longer period of time to
develop sufficient statistical confidence. This data may
then be used in the generation of a phase reference (relative probe) or
tachometer signal for a piece of equipment. This phase
reference may be used to align phase data such as velocity and/or positional
and/or acceleration data from multiple sensors located
at different positions on a component or on different components within a
system. This information may facilitate the determination
of torsion for different components or the generation of an Operational
Deflection Shape (ODS), indicating the extent of torsion on
one or more components during an operational mode.
[0816] The higher resolution data stream may provide additional data for the
detection of transitory signals in low speed operations.
The identification of transitory signals may enable the identification of
defects in a piece of equipment or component
[0817] In an illustrative and non-limiting example, the monitoring device may
be used to identify mechanical jitter for use in
failure prediction models. The monitoring device may begin acquiring data when
the piece of equipment starts up through ramping
up to operating speed and then during operation. Once at operating speed, it
is anticipated that the torsional jitter should be minimal
and changes in torsion during this phase may be indicative of cracks, bearing
faults and the like. Additionally, known torsions may
be removed from the signal to facilitate in the identification of
unanticipated torsions resulting from system design flaws or
component wear. Having phase information associated with the data collected at
operating speed may facilitate identification of a
location of vibration and potential component wear. Relative phase information
for a plurality of sensors located throughout a
machine may facilitate the evaluation of torsion as it is propagated through a
piece of equipment.
[0818] Based on the output of its various components, the system evaluation
circuit 9408 may make a component life prediction,
identify a component health parameter, identify a component performance
parameter, and the like. The system evaluation circuit
9408 may identify unexpected torsion on a rotating component, identify
strain/stress of flexure bearings, and the like. The system
evaluation circuit 9408 may identify optimal operation parameters for a piece
of equipment to reduce torsion and extend component
life. The system evaluation circuit 9408 may identify torsion at selected
operational frequencies (e.g., shaft rotation rates).
Information about operational frequencies causing torsion may be facilitate
equipment operational balance in the future.
[0819] The system evaluation circuit 9408 may communicate with the data
storage circuit 9414 to access equipment specifications,
equipment geometry, bearing specifications, component materials, anticipated
state information for a plurality of component types,
operational history, historical detection values, and the like for use in
assessing the output of its various components. The system
evaluation circuit 9408 may buffer a subset of the plurality of detection
values, intermediate data such as time-based detection
values, time-based detection values transformed to frequency information,
filtered detection values, identified frequencies of
interest, and the like for a predetermined length of time. The system
evaluation circuit 9408 may periodically store certain detection
values in the data storage circuit 9414 to enable the tracking of component
performance over time. In embodiments, based on
relevant operating conditions and/or failure modes, which may occur as
detection values approach one or more criteria, the system
evaluation circuit 9408 may store data in the data storage circuit 9414 based
on the fit of data relative to one or more criteria, such
as those described throughout this disclosure. Based on one sensor input
meeting or approaching specified criteria or range, the
system evaluation circuit 9408 may store additional data such as RPM
information, component loads, temperatures, pressures,
vibrations or other sensor data of the types described throughout this
disclosure in the data storage circuit 9414. The system
evaluation circuit 9408 may store data in the data storage circuit at a higher
data rate for greater granularity in future processing, the
ability to reprocess at different sampling rates, and/or to enable diagnosing
or post-processing of system information where
operational data of interest is flagged, and the like.
[0820] Depending on the type of equipment, the component being measured, the
environment in which the equipment is operating
and the like, sensors 9406 may comprise one or more of, without limitation,
displacement sensor, an angular velocity sensor, an
angular accelerometer, a vibration sensor, an optical vibration sensor, a
thermometer, a hygrometer, a voltage sensor, a current
sensor, an accelerometer, a velocity detector, a light or electromagnetic
sensor (e.g.. determining temperature, composition and/or
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spectral analysis, and/or object position or movement), an image sensor, a
structured light sensor, a laser-based image sensor, an
infrared sensor, an acoustic wave sensor, a heat flux sensor, a displacement
sensor, a turbidity meter, a viscosity meter, a load sensor,
a tri-axial vibration sensor, an accelerometer, a tachometer, a fluid pressure
meter, an air flow meter, a horsepower meter, a flow
rate meter, a fluid particle detector, an acoustical sensor, a pH sensor, and
the like, including, without limitation, any of the sensors
described throughout this disclosure and the documents incorporated by
reference.
[0821] The sensors 9406 may provide a stream of data over time that has a
phase component, such as relating to angular velocity,
angular acceleration or vibration, allowing for the evaluation of phase or
frequency analysis of different operational aspects of a
piece of equipment or an operating component. The sensors 9406 may provide a
stream of data that is not conventionally phase-
based, such as temperature, humidity, load, and the like. The sensors 9406 may
provide a continuous or near continuous stream of
data over time, periodic readings, event-driven readings, and/or readings
according to a selected interval or schedule.
[0822] In an illustrative and non-limiting example, when assessing engine
components in may be desirable to remove vibrations
due to the timing of piston vibrations or anticipated vibrational input due to
crankshaft geometry to assist in identifying other
torsional forces on a component. This may assist in assessing the health of
such diverse components as a water pump in a vehicle,
and positive displacement pumps in general.
[0823] In an illustrative and non-limiting example, torsional analysis and the
identification of variations in torsion may assist in
the identification of stick-slip in a gear or transfer system. In some cases,
this may only occur once per cycle, and phase information
may be as important as or more important than the amplitude of the signal in
determining system state or behavior.
[0824] In an illustrative and non-limiting example, torsional analysis may
assist in the identification, prediction (e.g., timing) and
evaluation of lash in a drive train and the follow-on torsion resulting from a
change in direction or start up, which in turn may be
used for control of a system, for assessing needs for maintenance, for
assessing needs for balancing or otherwise re-setting
components, or the like.
[0825] In an illustrative and non-limiting example, when assessing
compressors, it may be desirable to remove vibrations due to
the timing of piston vibrations or anticipated vibrational input associated
with the techniques and geometry used for positive
displacement compressors to assist in identifying other torsional forces on a
component. This may assist in assessing the health of
compressors in such diverse environments as air conditioning units in
factories, compressors in gas handling systems in an industrial
environment, compressors in the oil fields, and other environments as
described elsewhere herein.
[0826] In an illustrative and non-limiting example, torsional analysis may
facilitate the understanding of the health and expected
life of various components associated with the drive trains of vehicles, such
as cranes, bulldozers, tractors, haulers, backhoes,
forklifts, agricultural equipment, mining equipment, boring and drilling
machines, digging machines, lifting machines, mixers (e.g.,
cement mixers), tank trucks, refrigeration trucks, security vehicles (e.g.,
including safes and similar facilities for preserving
valuables), underwater vehicles, watercraft, aircraft, automobiles, trucks,
trains and the like, as well as drive trains of moving
apparatus, such as assembly lines, lifts, cranes, conveyors, hauling systems,
and others. The evaluation of the sensor data with the
model of the system geometry and operating conditions may be useful in
identifying unexpected torsion and the transmission of that
torsion from the motor and drive shaft, from the drive shaft to the universal
joint and from the universal join to one or more wheel
axles.
[0827] In an illustrative and non-limiting example, torsional analysis may
facilitate in the understanding of the health and expected
life of various components associated with train/tram wheels and wheel sets.
As discussed above, torsional analysis may facilitate
in the identification of stick-slip between the wheels or wheel sets and the
rail. The torsional analysis in view of the system geometry
may facilitate the identification of torsional vibration due to stick-slip as
opposed to the torsional vibration due to the driving
geometry connecting the engine to the drive shaft to the wheel axle.
[0828] In embodiments, as illustrated in Figure 85, the sensors 9406 may be
part of the data monitoring device 9400, referred to
herein in some cases as a data collector, which in some cases may comprise a
mobile or portable data collector. In embodiments, as
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illustrated in Figures 86 and 87, one or more external sensors 9422, which are
not explicitly part of a monitoring device 9416 but
rather are new, previously attached to or integrated into the equipment or
component, may be opportunistically connected to or
accessed by the monitoring device 9416. The monitoring device 9416 may include
a controller 9418. The controller 9418 may
include a data acquisition circuit 9420, a data storage circuit 9414, a system
evaluation circuit 9408 and, optionally, a response
circuit 9410. The system evaluation circuit 9408 may comprise a torsional
analysis circuit 9412. The data acquisition circuit 9420
may include one or more input ports 9424. In embodiments as shown in Figure
87, a data acquisition circuit 9420 may further
comprise a wireless communications circuit 9426. The one or more external
sensors 9422 may be directly connected to the one or
more input ports 9424 on the data acquisition circuit 9420 of the controller
9418 or may be accessed by the data acquisition circuit
9420 wirelessly using the wireless communications circuit 9426, such as by a
reader, interrogator, or other wireless connection,
such as over a short-distance wireless protocol. The data acquisition circuit
9420 may use the wireless communications circuit 9426
to access detection values corresponding to the one or more external sensors
9422 wirelessly or via a separate source or some
combination of these methods.
[0829] In embodiments as illustrated in Figure 88, the sensors 9406 may be in
communication with a monitoring device 9430
which may include a data acquisition circuit 9432, a signal evaluation circuit
9408 and data storage 9414. The data acquisition
circuit 9432 may further comprise a multiplexer circuit 9434 as described
elsewhere herein. Outputs from the multiplexer circuit
9434 may be utilized by the system evaluation circuit 9408. The system
evaluation circuit may comprise a torsional analysis circuit
9412. The response circuit 9410 may have the ability to turn on and off
portions of the multiplexor circuit 9434. The response
circuit 9410 may have the ability to control the control channels of the
multiplexor circuit 9434
[0830] The response circuit 9410 may initiate actions based on a component
performance parameter, a component health value, a
component life prediction parameter, and the like. The response circuit 9410
may evaluate the results of the system evaluation circuit
9408 and, based on certain criteria or the output from various components of
the system evaluation circuit 9408, may initiate an
action. The criteria may include identification of torsion on a component by
the torsional analysis circuit. The criteria may include
a sensor's detection values at certain frequencies or phases relative to a
timer signal where the frequencies or phases of interest may
be based on the equipment geometry, equipment control schemes, system input,
historical data, current operating conditions, and/or
an anticipated response. The criteria may include a sensor's detection values
at certain frequencies or phases relative to detection
values of a second sensor. The criteria may include signal strength at certain
resonant frequencies/harmonics relative to detection
values associated with a system tachometer or anticipated based on equipment
geometry and operation conditions. Criteria may
include a predetermined peak value for a detection value from a specific
sensor, a cumulative value of a sensor's corresponding
detection value over time, a change in peak value, a rate of change in a peak
value, and/or an accumulated value (e.g., a time spent
above/below a threshold value, a weighted time spent above/below one or more
threshold values, and/or an area of the detected
value above/below one or more threshold values). The criteria may comprise
combinations of data from different sensors such as
relative values, relative changes in value, relative rates of change in value,
relative values overtime, and the like. The relative criteria
may change with other data or information such as process stage, type of
product being processed, type of equipment, ambient
temperature and humidity, external vibrations from other equipment, and the
like. The relative criteria may be reflected in one or
more calculated statistics or metrics (including ones generated by further
calculations on multiple criteria or statistics), which in
turn may be used for processing (such as on board a data collector or by an
external system), such as to be provided as an input to
one or more of the machine learning capabilities described in this disclosure,
to a control system (which may be on board a data
collector or remote, such as to control selection of data inputs, multiplexing
of sensor data, storage, or the like), or as a data element
that is an input to another system, such as a data stream or data package that
may be available to a data marketplace, a SCADA
system, a remote control system, a maintenance system, an analytic system, or
other system.
[0831] Certain embodiments are described herein as detected values exceeding
thresholds or predetermined values, but detected
values may also fall below thresholds or predetermined values ¨ for example
where an amount of change in the detected value is
expected to occur, but detected values indicate that the change may not have
occurred. Except where the context clearly indicates
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otherwise, any description herein describing a determination of a value above
a threshold and/or exceeding a predetermined or
expected value is understood to include determination of a value below a
threshold and/or falling below a predetermined or expected
value.
[0832] The predetermined acceptable range may be based on anticipated torsion
based on equipment geometry, the geometry of a
transfer system, an equipment configuration or control scheme, such as a
piston firing sequence, and the like. The predetermined
acceptable range may also be based on historical performance or predicted
performance, such as based on long term analysis of
signals and performance both from the past run and from the past several runs.
The predetermined acceptable range may also be
based on historical performance or predicted performance, or based on long
term analysis of signals and performance across a
plurality of similar equipment and components (both within a specific
environment, within an individual company, within multiple
companies in the same industry and across industries. The predetermined
acceptable range may also be based on a correlation of
sensor data with actual equipment and component performance.
[0833] In some embodiments, an alert may be issued based on some of the
criteria discussed above. In embodiments, the relative
criteria for an alarm may change with other data or information, such as
process stage, type of product being processed on equipment,
ambient temperature and humidity, external vibrations from other equipment and
the like. In an illustrative and non-limiting
example, the response circuit 9410 may initiate an alert if a torsion in a
component across a plurality of components exceeds a
predetermined maximum value, if there is a change or rate of change that
exceeds a predetermined acceptable range, and/or if an
accumulated value based on torsion amplitude and/or frequency exceeds a
threshold.
[0834] In embodiments, response circuit 9410 may cause the data acquisition
circuit 9432 to enable or disable the processing of
detection values corresponding to certain sensors based on the some of the
criteria discussed above. This may include switching to
sensors having different response rates, sensitivity, ranges, and the like;
accessing new sensors or types of sensors, and the like.
Switching may be undertaken based on a model, a set of rules, or the like. In
embodiments, switching may be under control of a
machine learning system, such that switching is controlled based on one or
more metrics of success, combined with input data, over
a set of trials, which may occur under supervision of a human supervisor or
under control of an automated system. Switching may
involve switching from one input port to another (such as to switch from one
sensor to another). Switching may involve altering the
multiplexing of data, such as combining different streams under different
circumstances. Switching may involve activating a system
to obtain additional data, such as moving a mobile system (such as a robotic
or drone system), to a location where different or
additional data is available (such as positioning an image sensor for a
different view or positioning a sonar sensor for a different
direction of collection) or to a location where different sensors can be
accessed (such as moving a collector to connect up to a sensor
that is disposed at a location in an environment by a wired or wireless
connection). This switching may be implemented by changing
the control signals for a multiplexor circuit 9434 and/or by turning on or off
certain input sections of the multiplexor circuit 9434.
[0835] The response circuit 9410 may calculate transmission effectiveness
based on differences between a measured and
theoretical angular position and velocity of an output shaft after accounting
for the gear ration and any phase differential between
input and output.
[0836] The response circuit 9410 may identify equipment or components that are
due for maintenance. The response circuit 9410
may make recommendations for the replacement of certain sensors in the future
with sensors having different response rates,
sensitivity, ranges, and the like. The response circuit 9410 may recommend
design alterations for future embodiments of the
component, the piece of equipment, the operating conditions, the process, and
the like.
[0837] In embodiments, the response circuit 9410 may recommend maintenance at
an upcoming process stop or initiate a
maintenance call. The response circuit 9410 may recommend changes in process
or operating parameters to remotely balance the
piece of equipment. In embodiments, the response circuit 9410 may implement or
recommend process changes ¨ for example to
lower the utilization of a component that is near a maintenance interval,
operating off-nominally, or failed for purpose but still at
least partially operational, to change the operating speed of a component
(such as to put it in a lower-demand mode), to initiate
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amelioration of an issue (such as to signal for additional lubrication of a
roller bearing set, or to signal for an alignment process for
a system that is out of balance), and the like.
[0838] In embodiments as shown in Figures 89 and 90, a data monitoring system
9436 may include at least one data monitoring
device 9448. The at least one data monitoring device 9448 may include sensors
9406 and a controller 9438 comprising a data
acquisition circuit 9404, a system evaluation circuit 9408, a data storage
circuit 9414, and a communications circuit 9442. The
system evaluation circuit 9408 may include a torsional analysis circuit 9412.
There may also be an optional response circuit as
described above and elsewhere herein. The system evaluation circuit 9408 may
periodically share data with the communication
circuit 9442 for transmittal to the remote server 9440 to enable the tracking
of component and equipment performance over time
and under varying conditions by a monitoring application 9446. Because
relevant operating conditions and/or failure modes may
occur in as sensor values approach one or more criteria, the system evaluation
circuit 9408 may share data with the communication
circuit 9442 for transmittal to the remote server 9440 based on the fit of
data relative to one or more criteria. Based on one sensor
input meeting or approaching specified criteria or range, the system
evaluation circuit 9408 may share additional data such as RPMS,
component loads, temperatures, pressures, vibrations, and the like for
transmittal. The system evaluation circuit 9408 may share
data at a higher data rate for transmittal to enable greater granularity in
processing on the remote server. In embodiments as shown
in Figure 89, the communications circuit 9442 may communicate data directly to
a remote server 9440. In embodiments as shown
in Figure 90, the communications circuit 9442 may communicate data to an
intermediate computer 9450 which may include a
processor 9452 running an operating system 9454 and a data storage circuit
9456.
[0839] In embodiments as illustrated in Figures 91 and 92, a data collection
system 9458 may have a plurality of monitoring
devices 9448 collecting data on multiple components in a single piece of
equipment, collecting data on the same component across
a plurality of pieces of equipment, (both the same and different types of
equipment) in the same facility as well as collecting data
from monitoring devices in multiple facilities. A monitoring application 9446
on a remote server 9440 may receive and store one
or more of detection values, timing signals and data coming from the plurality
of the monitoring devices 9448. In embodiments as
shown in Figure 91, the communications circuits 9442 of a portion of the
plurality of monitoring devices 9448 may communicate
data directly to a remote server 9440. In embodiments as shown in Figure 92,
the communications circuits 9442 of a portion of the
of the plurality of monitoring devices 9448 may communicate data one or more
intermediate computers 9450, each of which may
include a processor 9452 running an operating system 9454 and a data storage
circuit 9456. There may be an individual intermediate
computer 9450 associated with each monitoring device 9264 or an individual
intermediate computer 9450 may be associated with
a plurality of monitoring devices 9448 where the intermediate computer 9450
may collect data from a plurality of data monitoring
devices and send the cumulative data to the remote server 9440.
[0840] The monitoring application 9446 may select subsets of detection values,
timing signals, data, product performance and the
like to be jointly analyzed. Subsets for analysis may be selected based on a
component type, component materials, a single type of
equipment in which a component is operating. Subsets for analysis may be
selected or grouped based on common operating
conditions or operational history such as size of load, operational condition
(e.g. intermittent, continuous), operating speed or
tachometer, common ambient environmental conditions such as humidity,
temperature, air or fluid particulate, and the like. Subsets
for analysis may be selected based on common anticipated state information.
Subsets for analysis may be selected based on the
effects of other nearby equipment such as nearby machines rotating at similar
frequencies, nearby equipment producing
electromagnetic fields, nearby equipment producing heat, nearby equipment
inducing movement or vibration, nearby equipment
emitting vapors, chemicals or particulates, or other potentially interfering
or intervening effects.
[0841] The monitoring application 9446 may analyze a selected subset. In an
illustrative example, data from a single component
may be analyzed over different time periods such as one operating cycle, cycle
to cycle comparisons, trends over several operating
cycles/time such as a month, a year, the life of the component or the like.
Data from multiple components of the same type may also
be analyzed over different time periods. Trends in the data such as changes in
frequency or amplitude may be correlated with failure
and maintenance records associated with the same component or piece of
equipment. Trends in the data such as changing rates of
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change associated with start-up or different points in the process may be
identified. Additional data may be introduced into the
analysis such as output product quality, output quantity (such as per unit of
time), indicated success or failure of a process, and the
like. Correlation of trends and values for different types of data may be
analyzed to identify those parameters whose short-term
analysis might provide the best prediction regarding expected performance. The
analysis may identify model improvements to the
model for anticipated state information, recommendations around sensors to be
used, positioning of sensors and the like. The
analysis may identify additional data to collect and store. The analysis may
identify recommendations regarding needed maintenance
and repair and/or the scheduling of preventative maintenance. The analysis may
identify recommendations around purchasing
replacement components and the timing of the replacement of the components.
The analysis may identify recommendations
regarding future geometry changes to reduce torsion on components. The
analysis may result in warning regarding dangerous of
catastrophic failure conditions. This information may be transmitted back to
the monitoring device to update types of data collected
and analyzed locally or to influence the design of future monitoring devices.
[0842] In embodiments, the monitoring application 9446 may have access to
equipment specifications, equipment geometry,
component specifications, component materials, anticipated state information
for a plurality of component types, operational history,
historical detection values, component life models and the like for use
analyzing the selected subset using rule-based or model-
based analysis. In embodiments, the monitoring application 9446 may feed a
neural net with the selected subset to learn to recognize
various operating state, health states (e.g. lifetime predictions) and fault
states utilizing deep learning techniques. In embodiments,
a hybrid of the two techniques (model-based learning and deep learning) may be
used.
[0843] In an illustrative and non-limiting example, the health of rotating
components on conveyors and lifters in an assembly line
may be monitored using the torsional analysis techniques, data monitoring
devices and data collection systems described herein.
[0844] In an illustrative and non-limiting example, the health of the health
of rotating components in water pumps on industrial
vehicles may be monitored using the using the torsional analysis techniques,
data monitoring devices and data collection systems
described herein.
[0845] In an illustrative and non-limiting example, the health of rotating
components in compressors in gas handling systems may
be monitored using the data monitoring devices and data collection systems
described herein.
[0846] In an illustrative and non-limiting example, the health of the health
of rotating components on in compressors situated out
in the gas and oil fields may be monitored using the data monitoring devices
and data collection systems described herein.
[0847] In an illustrative and non-limiting example, the health of the health
of rotating components on in factory air conditioning
units may be evaluated using the techniques, data monitoring devices and data
collection systems described herein.
[0848] In an illustrative and non-limiting example, the health of the health
of rotating components on in factory mineral pumps
may be evaluated using the techniques, data monitoring devices and data
collection systems described herein.
[0849] In an illustrative and non-limiting example, the health of the health
of rotating components such as shafts, bearings, and
gears in drilling machines and screw drivers situated in the oil and gas
fields may be evaluated using the torsional analysis
techniques, data monitoring devices and data collection systems described
herein.
[0850] In an illustrative and non-limiting example, the health of rotating
components such as shafts, bearings, gears and rotors of
motors situated in the oil and gas fields may be evaluated using the torsional
analysis techniques, data monitoring devices and data
collection systems described herein.
[0851] In an illustrative and non-limiting example, the health of rotating
components such as blades, screws and other components
of pumps situated in the oil and gas fields may be evaluated using the
torsional analysis techniques, data monitoring devices and
data collection systems described herein.
[0852] In an illustrative and non-limiting example, the health of rotating
components such as shafts, bearings, motors, rotors,
stators, gears and other components of vibrating conveyors situated in the oil
and gas fields may be evaluated using the torsional
analysis techniques, data monitoring devices and data collection systems
described herein.
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[0853] In an illustrative and non-limiting example, the health of rotating
components such as bearings, shafts, motors, rotors,
stators, gears and other components of mixers situated in the oil and gas
fields may be evaluated using the torsional analysis
techniques, data monitoring devices and data collection systems described
herein.
[0854] In an illustrative and non-limiting example, the health of rotating
components such as bearings, shafts, motors, rotors,
stators, gears and other components of centrifuges situated in oil and gas
refineries may be evaluated using the torsional analysis
techniques, data monitoring devices and data collection systems described
herein.
[0855] In an illustrative and non-limiting example, the health of rotating
components such as bearings, shafts, motors, rotors,
stators, gears and other components of refining tanks situated in oil and gas
refineries may be evaluated using the torsional analysis
techniques, data monitoring devices and data collection systems described
herein.
[0856] In an illustrative and non-limiting example, the health of rotating
components such as bearings, shafts, motors, rotors,
stators, gears and other components of rotating tank/mixer agitators to
promote chemical reactions deployed in chemical and
pharmaceutical production lines may be evaluated using the torsional analysis
techniques, data monitoring devices and data
collection systems described herein.
[0857] In an illustrative and non-limiting example, the health of rotating
components such as bearings, shafts, motors, rotors,
stators, gears and other components of mechanical/rotating agitators to
promote chemical reactions deployed in chemical and
pharmaceutical production lines may be evaluated using the torsional analysis
techniques, data monitoring devices and data
collection systems described herein.
[0858] In an illustrative and non-limiting example, the health of rotating
components such as bearings, shafts, motors, rotors,
stators, gears and other components of propeller agitators to promote chemical
reactions deployed in chemical and pharmaceutical
production lines may be evaluated using the torsional analysis techniques,
data monitoring devices and data collection systems
described herein.
[0859] In an illustrative and non-limiting example, the health of bearings and
associated shafts, motors, rotors, stators, gears and
other components of vehicle steering mechanisms may be evaluated using the
torsional analysis techniques, data monitoring devices
and data collection systems described herein.
[0860] In an illustrative and non-limiting example, the health of bearings and
associated shafts, motors, rotors, stators, gears and
other components of vehicle engines may be evaluated using the torsional
analysis techniques, data monitoring devices and data
collection systems described herein.
[0861] 1. A monitoring device for estimating an anticipated lifetime of a
rotating component in an industrial machine, the
monitoring device comprising:
a data acquisition circuit structured to interpret a plurality of detection
values, each of the plurality of detection values corresponding
to at least one of a plurality of input sensors, wherein the plurality of
input sensors comprises at least one of an angular position
sensor, an angular velocity sensor and an angular acceleration sensor
positioned to measure the rotating component;
a data storage circuit structured to store specifications, system geometry,
and anticipated state information for a plurality of rotating
components, store historical component performance and buffer the plurality of
detection values for a predetermined length of time;
and
a torsional analysis circuit structured to utilize transitory signal analysis
to analyze the buffered detection values relative to the
rotating component specifications and anticipated state information resulting
in the identification of torsional vibration; and
a system analysis circuit structured to utilize the identified torsional
vibration and at least one of an anticipated state, historical data
and a system geometry to identify an anticipated lifetime of the rotating
component.
[0862] 2. The monitoring device of claim 1, further comprising a response
circuit to perform at least one operation in response to
the anticipated lifetime of the rotating component, wherein the plurality of
input sensors includes at least two sensors selected from
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the group consisting of a temperature sensor, a load sensor, an optical
vibration sensor, an acoustic wave sensor, a heat flux sensor,
an infrared sensor, an accelerometer, a tri-axial vibration sensor and a
tachometer.
[0863] 3. The monitoring device of claim 2, wherein the at least one operation
comprises issuing at least one of an alert and a
warning.
[0864] 4. The monitoring device of claim 2, wherein the at least one operation
comprises storing additional data in the data storage
circuit.
[0865] 5. The monitoring device of claim 2, wherein the at least one operation
comprises one or ordering a replacement of the
rotating component, scheduling replacement of the rotating component, and
recommending alternatives to the rotating component.
[0866] 6. A monitoring device for evaluating a health of a rotating component
in an industrial machine, the monitoring device
comprising:
a data acquisition circuit structured to interpret a plurality of detection
values, each of the plurality of detection values corresponding
to at least one of a plurality of input sensors, wherein the plurality of
input sensors comprises at least one of an angular position
sensor, an angular velocity sensor and an angular acceleration sensor
positioned to measure the rotating component;
a data storage circuit structured to store specifications, system geometry,
and anticipated state information for a plurality of rotating
components, store historical component performance and buffer the plurality of
detection values for a predetermined length of time;
and
a torsional analysis circuit structured to utilize transitory signal analysis
to analyze the buffered detection values relative to the
rotating component specifications and anticipated state information resulting
in the identification of torsional vibration; and
a system analysis circuit structured to utilize the identified torsional
vibration and at least one of an anticipated state, historical data
and a system geometry to identify the health of the rotating component.
[0867] 7. The monitoring device of claim 6, further comprising a response
circuit to perform at least one operation in response to
the health of the rotating component, wherein the plurality of input sensors
includes at least two sensors selected from the group
consisting of a temperature sensor, a load sensor, an optical vibration
sensor, an acoustic wave sensor, a heat flux sensor, an infrared
sensor, an accelerometer, a tri-axial vibration sensor and a tachometer.
[0868] 8. The monitoring device of claim 7, wherein the at least one operation
comprises issuing at least one of an alert and an
alarm.
[0869] 9. The monitoring device of claim 7, wherein the at least one operation
comprises storing additional data in the data storage
circuit.
[0870] 10. The monitoring device of claim 7, wherein the at least one
operation comprises one or ordering a replacement of the
rotating component, scheduling replacement of the rotating component, and
recommending alternatives to the rotating component.
[0871] 11. A monitoring device for evaluating the operational state of a
rotating component in an industrial machine, the
monitoring device comprising:
a data acquisition circuit structured to interpret a plurality of detection
values, each of the plurality of detection values corresponding
to at least one of a plurality of input sensors, wherein the plurality of
input sensors comprises at least one of an angular position
sensor, an angular velocity sensor and an angular acceleration sensor
positioned to measure the rotating component;
a data storage circuit structured to store specifications, system geometry,
and anticipated state information for a plurality of rotating
components, store historical component performance and buffer the plurality of
detection values for a predetermined length of time;
and
a torsional analysis circuit structured to utilize transitory signal analysis
to analyze the buffered detection values relative to the
rotating component specifications and anticipated state information resulting
in the identification of torsional vibration; and
a system analysis circuit structured to utilize the identified torsional
vibration and at least one of an anticipated state, historical data
and a system geometry to identify the operational state of the rotating
component.
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[0872] 12. The system of claim 11, wherein the operational state is a current
or future operational state.
[0873] 13. The monitoring device of claim 11, further comprising a response
circuit to perform at least one operation in response
to operational state of the rotating component, wherein the plurality of input
sensors includes at least two sensors selected from the
group consisting of a temperature sensor, a load sensor, an optical vibration
sensor, an acoustic wave sensor, a heat flux sensor, an
infrared sensor, an accelerometer, a tri-axial vibration sensor and a
tachometer.
[0874] 14. The monitoring device of claim 13, wherein the at least one
operation comprises issuing at least one of an alert and an
alarm.
[0875] 15. The monitoring device of claim 13, wherein the at least one
operation comprises storing additional data in the data
storage circuit.
[0876] 16. The monitoring device of claim 13, wherein the at least one
operation comprises one or ordering a replacement of the
rotating component, scheduling replacement of the rotating component, and
recommending alternatives to the rotating component.
[0877] 17. A monitoring device for evaluating the operational state of a
rotating component in an industrial machine, the
monitoring device comprising:
a data acquisition circuit structured to interpret a plurality of detection
values, each of the plurality of detection values corresponding
to at least one of a plurality of input sensors, wherein the plurality of
input sensors comprises at least one of an angular position
sensor, an angular velocity sensor and an angular acceleration sensor
positioned to measure the rotating component;
a data storage circuit structured to store specifications, system geometry,
and anticipated state information for a plurality of rotating
components, store historical component performance and buffer the plurality of
detection values for a predetermined length of time;
and
a torsional analysis circuit structured to utilize transitory signal analysis
to analyze the buffered detection values relative to the
rotating component specifications and anticipated state information resulting
in the identification of torsional vibration; and
a system analysis circuit structured to utilize the identified torsional
vibration and at least one of an anticipated state, historical data
and a system geometry to identify the operational state of the rotating
component,
wherein the data acquisition circuit comprises a multiplexer circuit whereby
alternative combinations of the detection values may
be selected based on at least one of user input, a detected state and a
selected operating parameter for a machine.
[0878] 18. The system of claim 17, wherein the operational state is a current
or future operational state.
[0879] 19. The monitoring device of claim 16, further comprising a response
circuit to perform at least one operation in response
to operational state of the rotating component, wherein the plurality of input
sensors includes at least two sensors selected from the
group consisting of a temperature sensor, a load sensor, an optical vibration
sensor, an acoustic wave sensor, a heat flux sensor, an
infrared sensor, an accelerometer, a tri-axial vibration sensor and a
tachometer.
[0880] 20. The monitoring device of claim 19, wherein the at least one
operation comprises issuing at least one of an alert and an
alarm.
[0881] 21. The monitoring device of claim 19, wherein the at least one
operation comprises storing additional data in the data
storage circuit.
[0882] 22. The monitoring device of claim 19, wherein the at least one
operation comprises one or ordering a replacement of the
rotating component, scheduling replacement of the rotating component, and
recommending alternatives to the rotating component.
[0883] 23. The monitoring device of claim 19, wherein the at least one
operation comprises enabling or disabling one or more
portions of the multiplexer circuit, or altering the multiplexer control
lines.
[0884] 24. The monitoring device of claim 19, wherein the data acquisition
circuit comprises at least two multiplexer circuits and
the at least one operation comprises changing connections between the at least
two multiplexer circuits.
[0885] 25. A system for evaluating an operational state a rotating component
in a piece of equipment comprising:
at least one monitoring device comprising:
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a data acquisition circuit structured to interpret a plurality of detection
values, each of the plurality of detection values corresponding
to at least one of a plurality of input sensors, wherein the plurality of
input sensors comprises at least one of an angular position
sensor, an angular velocity sensor and an angular acceleration sensor
positioned to measure the rotating component;
a data storage circuit structured to store specifications, system geometry,
and anticipated state information for a plurality of rotating
components, store historical component performance and buffer the plurality of
detection values for a predetermined length of time;
and
a torsional analysis circuit structured to utilize transitory signal analysis
to analyze the buffered detection values relative to the
rotating component specifications and anticipated state information resulting
in identification of any torsional vibration;
a system analysis circuit structured to utilize the torsional vibration and at
least one of an anticipated state, historical data and a
system geometry to identify the operational state of the rotating component;
and
a communication module enabled to communicate the operational state of the
rotating component, the torsional vibration and
detection values to a remote server, wherein the detection values communicated
are based partly on the operational state of the
rotating component and the torsional vibration; and
a monitoring application on the remote server structured to receive, store and
jointly analyze a subset of the detection values from
the monitoring devices.
[0886] 26. The system of claim 25, wherein the analysis of the subset of
detection values comprises transitory signal analysis to
identify the presence of high frequency torsional vibration.
[0887] 27. The system of claim 25, the monitoring application further
structured to subset detection values based on one of
operational state, torsional vibration, type of the rotating component,
operational conditions under which detection values were
measured, and type or equipment.
[0888] 28. The system of claim 25, wherein the analysis of the subset of
detection values comprises feeding a neural net with the
subset of detection values and supplemental information to learn to recognize
various operating states, health states and fault states
utilizing deep learning techniques.
[0889] 29. The system of claim 28, wherein the supplemental information
comprises one of component specification, component
performance, equipment specification, equipment performance, maintenance
records, repair records and an anticipated state model.
[0890] 30. The system of claim 25, wherein the operational state is a current
or future operational state.
[0891] 31. The system of claim 25, the monitoring device further comprising a
response circuit to perform at least one operation
in response to operational state of the rotating component, wherein the
plurality of input sensors includes at least two sensors selected
from the group consisting of a temperature sensor, a load sensor, an optical
vibration sensor, an acoustic wave sensor, a heat flux
sensor, an infrared sensor, an accelerometer, a tri-axial vibration sensor and
a tachometer.
[0892] 32. The system of claim31, wherein the at least one operation comprises
issuing at least one of an alert and an alarm.
[0893] 33. The system of claim 31, wherein the at least one operation
comprises storing additional data in the data storage circuit.
[0894] 34. The system of claim 31, wherein the at least one operation
comprises one or ordering a replacement of the rotating
component, scheduling replacement of the rotating component, and recommending
alternatives to the rotating component.
[0895] 35. A system for evaluating a health of a rotating component in a piece
of equipment comprising:
at least one monitoring device comprising:
a data acquisition circuit structured to interpret a plurality of detection
values, each of the plurality of detection values corresponding
to at least one of a plurality of input sensors, wherein the plurality of
input sensors comprises at least one of an angular position
sensor, an angular velocity sensor and an angular acceleration sensor
positioned to measure the rotating component;
a data storage circuit structured to store specifications, system geometry,
and anticipated state information for a plurality of rotating
components, store historical component performance and buffer the plurality of
detection values for a predetermined length of time;
and
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a torsional analysis circuit structured to utilize transitory signal analysis
to analyze the buffered detection values relative to the
rotating component specifications and anticipated state information resulting
in identification of torsional vibration;
a system analysis circuit structured to utilize the torsional vibration and at
least one of an anticipated state, historical data and a
system geometry to identify the health of the rotating component; and
a communication module enabled to communicate the health of the rotating
component, the torsional vibrations and detection values
to a remote server, wherein the detection values communicated are based partly
on the health of the rotating component and the
torsional vibration; and
a monitoring application on the remote server structured to receive, store and
jointly analyze a subset of the detection values from
the monitoring devices.
[0896] 36. The system of claim 35, wherein the analysis of the subset of
detection values comprises transitory signal analysis to
identify the presence of high frequency torsional vibration.
[0897] 37. The system of claim 35, the monitoring application further
structured to subset detection values based on one of
operational state, torsional vibration, type of the rotating component,
operational conditions under which detection values were
measured, and type or equipment.
[0898] 38. The system of claim 35, wherein the analysis of the subset of
detection values comprises feeding a neural net with the
subset of detection values and supplemental information to learn to recognize
various operating states, health states and fault states
utilizing deep learning techniques.
[0899] 39. The system of claim 38, wherein the supplemental information
comprises one of component specification, component
performance, equipment specification, equipment performance, maintenance
records, repair records and an anticipated state model.
[0900] 40. The system of claim 35, wherein the operational state is a current
or future operational state.
[0901] 41. The system of claim 35, the monitoring device further comprising a
response circuit to perform at least one operation
in response to the health of the rotating component, wherein the plurality of
input sensors includes at least two sensors selected from
the group consisting of a temperature sensor, a load sensor, an optical
vibration sensor, an acoustic wave sensor, a heat flux sensor,
an infrared sensor, an accelerometer, a tri-axial vibration sensor and a
tachometer.
[0902] 42. The system of claim31, wherein the at least one operation comprises
issuing at least one of an alert and an alarm.
[0903] 43. The system of claim 31, wherein the at least one operation
comprises storing additional data in the data storage circuit.
[0904] 44. The system of claim 31, wherein the at least one operation
comprises one or ordering a replacement of the rotating
component, scheduling replacement of the rotating component, and recommending
alternatives to the rotating component.
[0905] 45. A system for estimating an anticipated lifetime a rotating
component in a piece of equipment comprising:
at least one monitoring device comprising:
a data acquisition circuit structured to interpret a plurality of detection
values, each of the plurality of detection values corresponding
to at least one of a plurality of input sensors, wherein the plurality of
input sensors comprises at least one of an angular position
sensor, an angular velocity sensor and an angular acceleration sensor
positioned to measure the rotating component;
a data storage circuit structured to store specifications, system geometry,
and anticipated state information for a plurality of rotating
components, store historical component performance and buffer the plurality of
detection values for a predetermined length of time;
and
a torsional analysis circuit structured to utilize transitory signal analysis
to analyze the buffered detection values relative to the
rotating component specifications and anticipated state information resulting
in identification of torsional vibration;
a system analysis circuit structured to utilize the torsional vibration and at
least one of an anticipated state, historical data and a
system geometry to identify an anticipated life the rotating component; and
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a communication module enabled to communicate the anticipated life of the
rotating component, the torsional vibrations and
detection values to a remote server, wherein the detection values communicated
are based partly on the anticipated life of the rotating
component and the torsional vibration; and
a monitoring application on the remote server structured to receive, store and
jointly analyze a subset of the detection values from
the monitoring devices.
[0906] 46. The system of claim 45, wherein the analysis of the subset of
detection values comprises transitory signal analysis to
identify the presence of high frequency torsional vibration.
[0907] 47. The system of claim 45, the monitoring application further
structured to subset detection values based on one of
anticipated life of the rotating component, torsional vibration, type of the
rotating component, operational conditions under which
detection values were measured, and type or equipment.
[0908] 48. The system of claim 45, wherein the analysis of the subset of
detection values comprises feeding a neural net with the
subset of detection values and supplemental information to learn to recognize
various operating states, health states, life expectancies
and fault states utilizing deep learning techniques.
[0909] 49. The system of claim 48, wherein the supplemental information
comprises one of component specification, component
performance, equipment specification, equipment performance, maintenance
records, repair records and an anticipated state model.
[0910] 50. The system of claim 45, the monitoring device further comprising a
response circuit to perform at least one operation
in response to the anticipated life of the rotating component, wherein the
plurality of input sensors includes at least two sensors
selected from the group consisting of a temperature sensor, a load sensor, an
optical vibration sensor, an acoustic wave sensor, a
heat flux sensor, an infrared sensor, an accelerometer, a tri-axial vibration
sensor and a tachometer.
[0911] 51. The system of claim 50, wherein the at least one operation
comprises issuing at least one of an alert and an alarm.
[0912] 52. The system of claim 50, wherein the at least one operation
comprises storing additional data in the data storage circuit.
[0913] 53. The system of claim 50, wherein the at least one operation
comprises one or ordering a replacement of the rotating
component, scheduling replacement of the rotating component, and recommending
alternatives to the rotating component.
[0914] 54. A system for evaluating the health of a variable frequency motor in
an industrial environment comprising:
at least one monitoring device comprising:
a data acquisition circuit structured to interpret a plurality of detection
values, each of the plurality of detection values corresponding
to at least one of a plurality of input sensors, wherein the plurality of
input sensors comprises at least one of an angular position
sensor, an angular velocity sensor and an angular acceleration sensor
positioned to measure the rotating component;
a data storage circuit structured to store specifications, system geometry,
and anticipated state information for a plurality of rotating
components, store historical component performance and buffer the plurality of
detection values for a predetermined length of time;
and
a torsional analysis circuit structured to utilize transitory signal analysis
to analyze the buffered detection values relative to the
rotating component specifications and anticipated state information resulting
in identification of torsional vibration;
a system analysis circuit structured to utilize the torsional vibration and at
least one of an anticipated state, historical data and a
system geometry to identify a motor health parameter; and
a communication module enabled to communicate the motor health parameter, the
torsional vibrations and detection values to a
remote server, wherein the detection values communicated are based partly on
the motor health parameter and the torsional vibration;
and
a monitoring application on the remote server structured to receive, store and
jointly analyze a subset of the detection values from
the monitoring devices.
[0915] 55. A system for data collection, processing, and torsional analysis of
a rotating component in an industrial environment
comprising:
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a plurality of monitoring devices, each monitoring device comprising:
a data acquisition circuit structured to interpret a plurality of detection
values, each of the plurality of detection values corresponding
to at least one of a plurality of input sensors, wherein the plurality of
input sensors comprises at least one of an angular position
sensor, an angular velocity sensor and an angular acceleration sensor
positioned to measure the rotating component;
a streaming circuit for streaming at least a subset of the acquired detection
values to a remote learning system; and
a remote learning system including a torsional analysis circuit structured to
analyze the detection values relative to a machine-based
understanding of the state of the at least one rotating component.
[0916] 56. The system of claim 55, wherein the machine-based understanding is
developed based on a model of the rotating
component that determines a state of the at least one rotating component based
at least in part on the relationship of the behavior of
the rotating component to an operating frequency of a component of the
industrial machine.
[0917] 57. The system of claim 56, wherein the state of the at least one
rotating component is at least one of an operating state, a
health state, a predicted lifetime state and a fault state.
[0918] 58. The system of claim 55, wherein the machine-based understanding is
developed based by providing inputs to a deep
learning machine, wherein the inputs comprise a plurality of streams of
detection values for a plurality of rotating components and
a plurality of measured state values for the plurality of rotating components.
[0919] 60. The system of claim 58, wherein the state of the at least one
rotating component is at least one of an operating state, a
health state, a predicted lifetime state and a fault state.
[0920] In embodiments, information about the health or other status or state
information of or regarding a component or piece of
industrial equipment may be obtained by monitoring the condition of various
components throughout a process. Monitoring may
include monitoring the amplitude of a sensor signal measuring attributes such
as temperature, humidity, acceleration, displacement
and the like. An embodiment of a data monitoring device 9700 is shown in
Figure 93 and may include a plurality of sensors 9706
communicatively coupled to a controller 9702. The controller 9702 may include
a data acquisition circuit 9704, a signal evaluation
circuit 9708, a data storage circuit 9716 and a response circuit 9710. The
signal evaluation circuit 9708 may comprise a circuit for
detecting a fault in one or more sensors, or a set of sensors, such as an
overload detection circuit 9712, a sensor fault detection circuit
9714, or both. Additionally, the signal evaluation circuit 9708 may optionally
comprise one or more of a peak detection circuit, a
phase detection circuit, a bandpass filter circuit, a frequency transformation
circuit, a frequency analysis circuit, a phase lock loop
circuit, a torsional analysis circuit, a bearing analysis circuit, and the
like.
[0921] The plurality of sensors 9706 may be wired to ports on the data
acquisition circuit 9704. The plurality of sensors 9706 may
be wirelessly connected to the data acquisition circuit 9704. The data
acquisition circuit 9704 may be able to access detection values
corresponding to the output of at least one of the plurality of sensors 9706
where the sensors 9706 may be capturing data on different
operational aspects of a piece of equipment or an operating component.
[0922] The selection of the plurality of sensors 9706 for a data monitoring
device 9700 designed for a specific component or piece
of equipment may depend on a variety of considerations such as accessibility
for installing new sensors, incorporation of sensors in
the initial design, anticipated operational and failure conditions, resolution
desired at various positions in a process or plant,
reliability of the sensors, and the like. The impact of a failure, time
response of a failure (e.g. warning time and/or off-nominal
modes occurring before failure), likelihood of failure, and/or sensitivity
required and/or difficulty to detection failure conditions
may drive the extent to which a component or piece of equipment is monitored
with more sensors and/or higher capability sensors
being dedicated to systems where unexpected or undetected failure would be
costly or have severe consequences.
[0923] Depending on the type of equipment, the component being measured, the
environment in which the equipment is operating
and the like, sensors 9706 may comprise one or more of, without limitation, a
vibration sensor, a thermometer, a hygrometer, a
voltage sensor and/or a current sensor (for the component and/or other sensors
measuring the component), an accelerometer, a
velocity detector, a light or electromagnetic sensor (e.g., determining
temperature, composition and/or spectral analysis, and/or
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object position or movement), an image sensor, a structured light sensor, a
laser-based image sensor, a thermal imager, an acoustic
wave sensor, a displacement sensor, a turbidity meter, a viscosity meter, a
axial load sensor, a radial load sensor, a tri-axial sensor,
an accelerometer, a speedometer, a tachometer, a fluid pressure meter, an air
flow meter, a horsepower meter, a flow rate meter, a
fluid particle detector, an optical (laser) particle counter, an ultrasonic
sensor, an acoustical sensor, a heat flux sensor, a galvanic
sensor, a magnetometer, a pH sensor, and the like, including, without
limitation, any of the sensors described throughout this
disclosure and the documents incorporated by reference..
[0924] The sensors 9706 may provide a stream of data over time that has a
phase component, such as relating to acceleration or
vibration, allowing for the evaluation of phase or frequency analysis of
different operational aspects of a piece of equipment or an
operating component. The sensors 9706 may provide a stream of data that is not
conventionally phase-based, such as temperature,
humidity, load, and the like. The sensors 9706 may provide a continuous or
near continuous stream of data over time, periodic
readings, event-driven readings, and/or readings according to a selected
interval or schedule.
[0925] In embodiments, as illustrated in Figure 93, the sensors 9706 may be
part of the data monitoring device 9700, referred to
herein in some cases as a data collector, which in some cases may comprise a
mobile or portable data collector. In embodiments, as
illustrated in Figures 94, 95,and 96 one or more external sensors 9724, which
are not explicitly part of a monitoring device 9718 but
rather are new, previously attached to or integrated into the equipment or
component, may be opportunistically connected to or
accessed by the monitoring device 9718. The monitoring device may include a
controller 9720 which may include a data acquisition
circuit 9704, a signal evaluation circuit 9708, a data storage circuit 9716
and a response circuit 9710. The signal evaluation circuit
9708 may comprise an overload detection circuit 9712, a sensor fault detection
circuit 9714, or both. Additionally, the signal
evaluation circuit 9708 may optionally comprise one or more of a peak
detection circuit, a phase detection circuit, a bandpass filter
circuit, a frequency transformation circuit, a frequency analysis circuit, a
phase lock loop circuit, a torsional analysis circuit, a
bearing analysis circuit, and the like. The data acquisition circuit 9704 may
include one or more input ports 9726.
[0926] The one or more external sensors 9724 may be directly connected to the
one or more input ports 9726 on the data acquisition
circuit 9704 of the controller 9720 or may be accessed by the data acquisition
circuit 9704 wirelessly, such as by a reader,
interrogator, or other wireless connection, such as over a short-distance
wireless protocol. In embodiments as shown in Figure 95,
a data acquisition circuit 9704 may further comprise a wireless communication
circuit 9730. The data acquisition circuit 9704 may
use the wireless communication circuit 9730 to access detection values
corresponding to the one or more external sensors 9724
wirelessly or via a separate source or some combination of these methods.
[0927] In embodiments, the data storage circuit 9716 may be structured to
store sensor specifications, anticipated state information
and detected values. The data storage circuit 9716 may provide specifications
and anticipated state information to the signal
evaluation circuit 9708.
[0928] In embodiments, an overload detection circuit 9712 may detect sensor
overload by comparing the detected value associated
with the sensor with a detected value associated with a sensor having a
greater range/lower resolution monitoring the same
component/attribute. Inconsistencies in measured value may indicate that the
higher resolution sensor may be overloaded. In
embodiments, an overload detection circuit 9712 may detect sensor overload by
evaluating consistency of sensor reading with
readings from other sensor data (monitoring the same or different aspects of
the component/piece of equipment. In embodiments,
an overload detection circuit 9712 may detect sensor overload by evaluating
data collected by other sensors to identify conditions
likely to result in sensor overload (e.g. heat flux sensor data indicative of
the likelihood of overloading a sensor in a given location,
accelerometer data indicating a likelihood of overloading a velocity sensor,
and the like). In embodiments, an overload detection
circuit 9712 may detect sensor overload by identifying flat line output
following a rising trend. In embodiments, an overload
detection circuit 9712 may detect sensor overload by transforming the sensor
data to frequency data, using for example a Fast
Fourier Transform (FFT), and then looking for a "ski-jump" in the frequency
data which may result from the data being clipped due
to an overloaded sensor. A sensor fault detection circuit 9714 may identify
failure of the sensor itself, sensor health, or potential
concerns re. validity of sensor data. Rate of value change may be used to
identify failure of the sensor itself. For example, a sudden
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jump to a maximum output may indicate a failure in the sensor rather than an
overload of the sensor. In embodiments, an overload
detection circuit 9712 and/or a sensor fault detection circuit 9714 may
utilize sensor specifications, anticipated state information,
sensor models and the like in the identification of sensor overload, failure,
error, invalid data, and the like. In embodiments, the
overload detection circuit 9712 or the sensor fault detection circuit 9714 may
use detection values from other sensors and output
from additional components such as a peak detection circuit and/or a phase
detection circuit and/or a bandpass filter circuit and/or
a frequency transformation circuit and/or a frequency analysis circuit and/or
a phase lock loop circuit and the like to identify potential
sources for the identified sensor overload, sensor faults, sensor failure, or
the like. Sources or factors involved in sensor overload
may include limitations on sensor range, sensor resolution, and sensor
sampling frequency. Sources of apparent sensor overload
may be due to a range, resolution or sampling frequency of a multiplexor
suppling detection values associated with the sensor.
Sources of factors involved in apparent sensor faults or failures may include
environmental conditions; for example, excessive heat
or cold may be associated with damage to semiconductor-based sensors, which
may result in erratic sensor data, failure of a sensor
to produce data, data that appears out of the range of normal behavior (e.g.,
large, discrete jumps in temperature for a system that
does not normally experience such changes). Surges in current and/or voltage
may be associated with damage to electrically
connected sensors with sensitive components. Excessive vibration may result in
physical damage to sensitive components of a
sensor such as wires and/or connectors. An impact, which may be indicated by
sudden acceleration or acoustical data may result in
physical damage to a sensor with sensitive components such as wires and/or
connectors. A rapid increase in humidity in the
environment surrounding a sensor or an absence of oxygen may indicate water
damage to a sensor. A sudden absence of signal from
a sensor may be indicative of sensor disconnection which may due to vibration,
impact and the like. A sensor that requires power
may run out of battery power or be disconnected from a power source. In
embodiments, the overload detection circuit 9712 or the
sensor fault detection circuit 9714 may output a sensor status where the
sensor status may be one of sensor overload, sensor failure,
sensor fault, sensor healthy, and the like. The sensor fault detection circuit
9714 may determine one of a sensor fault status and a
sensor validity status.
[0929] In embodiments as illustrated in Figure 96, the data acquisition
circuit 9704 may further comprise a multiplexer circuit
8114 as described elsewhere herein. Outputs from the multiplexer circuit 8114
may be utilized by the signal evaluation circuit 9708.
The response circuit 9710 may have the ability to turn on and off portions of
the multiplexor circuit 8114. The response circuit 9710
may have the ability to control the control channels of the multiplexor
circuit 8114.
[0930] In embodiments, the response circuit 9710 may initiate a variety of
actions based on the sensor status provided by the
overload detection circuit 9712. The response circuit 9710 may continue using
the sensor if the sensor status is "sensor healthy."
The response circuit 9710 may adjust a sensor scaling value (e.g. from
100mV/gram to 10 mV/gram). The response circuit 9710
may increase an acquisition range for an alternate sensor. The response
circuit 9710 may back sensor data out of previous
calculations and evaluations such as bearing analysis, torsional analysis and
the like. The response circuit 9710 may use projected
or anticipated data (based on data acquired prior to overload/failure) in
place of the actual sensor data for calculations and evaluations
such as bearing analysis, torsional analysis and the like. The response
circuit 9710 may issue an alarm. The response circuit 9710
may issue an alert where the alert may comprise notification that the sensor
is out of range together with information regarding the
extent of the overload such as "overload range- data response may not be
reliable and/or linear", "destructive range- sensor may be
damaged," and the like. The response circuit 9710 may issue an alert where the
alert may comprise information regarding the effect
of sensor load such as "unable to monitor machine health" due to sensor
overload/failure," and the like.
[0931] In embodiments, the response circuit 9710 may cause the data
acquisition circuit 9704 may control the multiplexor circuit
8114 to enable or disable the processing of detection values corresponding to
certain sensors based on the sensor statues described
above. This may include switching to sensors having different response rates,
sensitivity, ranges, and the like; accessing new sensors
or types of sensors, accessing data from multiple sensors, recruiting
additional data collectors (such as routing the collectors to a
point of work, using routing methods and systems disclosed throughout this
disclosure and the documents incorporated by reference)
and the like. Switching may be undertaken based on a model, a set of rules, or
the like. In embodiments, switching may be under
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control of a machine learning system, such that switching is controlled based
on one or more metrics of success, combined with
input data, over a set of trials, which may occur under supervision of a human
supervisor or under control of an automated system.
Switching may involve switching from one input port to another (such as to
switch from one sensor to another). Switching may
involve altering the multiplexing of data, such as combining different streams
under different circumstances. Switching may involve
activating a system to obtain additional data, such as moving a mobile system
(such as a robotic or drone system), to a location
where different or additional data is available (such as positioning an image
sensor for a different view or positioning a sonar sensor
for a different direction of collection) or to a location where different
sensors can be accessed (such as moving a collector to connect
up to a sensor that is disposed at a location in an environment by a wired or
wireless connection). This switching may be
implemented by changing the control signals for a multiplexor circuit 8114
and/or by turning on or off certain input sections of the
multiplexor circuit 8114.
[0932] In embodiments, the response circuit 9710 may make recommendations for
the replacement of certain sensors in the future
with sensors having different response rates, sensitivity, ranges, and the
like. The response circuit 9710 may recommend design
alterations for future embodiments of the component, the piece of equipment,
the operating conditions, the process, and the like.
[0933] In embodiments, the response circuit 9710 may recommend maintenance at
an upcoming process stop or initiate a
maintenance call where the maintenance may include the replacement of the
sensor with the same or an alternate type of sensor
having a different response rate, sensitivity, range and the like. In
embodiments, the response circuit 9710 may implement or
recommend process changes ¨ for example to lower the utilization of a
component that is near a maintenance interval, operating
off-nominally, or failed for purpose but still at least partially operational,
to change the operating speed of a component (such as to
put it in a lower-demand mode), to initiate amelioration of an issue (such as
to signal for additional lubrication of a roller bearing
set, or to signal for an alignment process for a system that is out of
balance), and the like.
[0934] In embodiments, the signal evaluation circuit 9708 and/or the response
circuit 9710 may periodically store certain detection
values in the data storage circuit 9716 to enable the tracking of component
performance over time. In embodiments, based on sensor
status, as described elsewhere herein recently measured sensor data and
related operating conditions such as RPMS, component
loads, temperatures, pressures, vibrations or other sensor data of the types
described throughout this disclosure in the data storage
circuit 9716 to enable the backing out of overloaded/failed sensor data. The
signal evaluation circuit 9708 may store data at a higher
data rate for greater granularity in future processing, the ability to
reprocess at different sampling rates, and/or to enable diagnosing
or post-processing of system information where operational data of interest is
flagged, and the like.
[0935] In embodiments as shown in Figures 97 and 98, a data monitoring system
9746 may include at least one data monitoring
device 9728. The at least one data monitoring device 9728 may include sensors
9706 and a controller 9731 comprising a data
acquisition circuit 9704, a signal evaluation circuit 9708, a data storage
circuit 9716, and a communication circuit 9732 to allow
data and analysis to be transmitted to a monitoring application 9734 on a
remote server 9736. The signal evaluation circuit 9708
may include at least an overload detection circuit 9712. The signal evaluation
circuit 9708 may periodically share data with the
communication circuit 9732 for transmittal to the remote server 9736 to enable
the tracking of component and equipment
performance over time and under varying conditions by a monitoring application
9734. Based on the sensor status, the signal
evaluation circuit 9708 and/or response circuit 9710 may share data with the
communication circuit 9732 for transmittal to the
remote server 9736 based on the fit of data relative to one or more criteria.
Data may include recent sensor data and additional data
such as RPMS, component loads, temperatures, pressures, vibrations, and the
like for transmittal. The signal evaluation circuit 9708
may share data at a higher data rate for transmittal to enable greater
granularity in processing on the remote server.
[0936] In embodiments as shown in Figure 97, the communication circuit 9732
may communicated data directly to a remote server
9736. In embodiments as shown in Figure 98, the communication circuit 9732 may
communicate data to an intermediate computer
9738 which may include a processor 9740 running an operating system 9742 and a
data storage circuit 9744.
[0937] In embodiments as illustrated in Figures 99 and 100, a data collection
system 9746 may have a plurality of monitoring
devices 9728 collecting data on multiple components in n Qinole ni POP nf
ecniinment r (meeting data on the same component across
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a plurality of pieces of equipment, (both the same and different types of
equipment) in the same facility as well as collecting data
from monitoring devices in multiple facilities. A monitoring application 9736
on a remote server 9734 may receive and store one
or more of detection values, timing signals and data coming from a plurality
of the various monitoring devices 9728.
[0938] In embodiments as shown in Figure 99, the communication circuit 9732
may communicated data directly to a remote server
9734. In embodiments as shown in Figure 100, the communication circuit 9732
may communicate data to an intermediate computer
9738 which may include a processor 9740 running an operating system 9742 and a
data storage circuit 9744. There may be an
individual intermediate computer 9738 associated with each monitoring device
9728 or an individual intermediate computer 9738
may be associated with a plurality of monitoring devices 9728 where the
intermediate computer 9738 may collect data from a
plurality of data monitoring devices and send the cumulative data to the
remote server 9734. Communication to the remote server
9734 may be streaming, batch (e.g. when a connection is available) or
opportunistic.
[0939] The monitoring application 9736 may select subsets of the detection
values to jointly analyzed. Subsets for analysis may
be selected based on a single type of sensor, component or a single type of
equipment in which a component is operating. Subsets
for analysis may be selected or grouped based on common operating conditions
such as size of load, operational condition (e.g.
intermittent, continuous), operating speed or tachometer, common ambient
environmental conditions such as humidity, temperature,
air or fluid particulate, and the like. Subsets for analysis may be selected
based on the effects of other nearby equipment such as
nearby machines rotating at similar frequencies, nearby equipment producing
electromagnetic fields, nearby equipment producing
heat, nearby equipment inducing movement or vibration, nearby equipment
emitting vapors, chemicals or particulates, or other
potentially interfering or intervening effects.
[0940] In embodiments, the monitoring application 9736 may analyze the
selected subset. In an illustrative example, data from a
single sensor may be analyzed over different time periods such as one
operating cycle, several operating cycles, a month, a year,
the life of the component or the like. Data from multiple sensors of a common
type measuring a common component type may also
be analyzed over different time periods. Trends in the data such as changing
rates of change associated with start-up or different
points in the process may be identified. Correlation of trends and values for
different sensors may be analyzed to identify those
parameters whose short-term analysis might provide the best prediction
regarding expected sensor performance. This information
may be transmitted back to the monitoring device to update sensor models,
sensor selection, sensor range, sensor scaling, sensor
sampling frequency, types of data collected and analyzed locally or to
influence the design of future monitoring devices.
[0941] In embodiments, the monitoring application 9736 may have access to
equipment specifications, equipment geometry,
component specifications, component materials, anticipated state information
for a plurality of sensors, operational history,
historical detection values, sensor life models and the like for use analyzing
the selected subset using rule-based or model-based
analysis. The monitoring application 9736 may provide recommendations
regarding sensor selection, additional data to collect, data
to store with sensor data. The monitoring application 9736 may provide
recommendations regarding scheduling repairs and/or
maintenance. The monitoring application 9736 may provide recommendations
regarding replacing a sensor. The replacement sensor
may match the sensor being replaced or the replacement sensor may have a
different range, sensitivity, sampling frequency and the
like.
[0942] In embodiments, the monitoring application 9736 may include a remote
learning circuit structured to analyze sensor status
data (e.g. sensor overload, sensor faults, sensor failure) together with data
from other sensors, failure data on components being
monitored, equipment being monitored, product being produced, and the like.
The remote learning system may identify correlations
between sensor overload and data from other sensors.
[0943] 1. A monitoring system for data collection in an industrial
environment, the monitoring system comprising:
a data acquisition circuit structured to interpret a plurality of detection
values, each of the plurality of detection values corresponding
to at least one of a plurality of input sensors;
a data storage circuit structured to store sensor specifications, anticipated
state information and detected values;
a signal evaluation circuit comprising:
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an overload identification circuit structured to determine a sensor overload
status of at least one sensor in response to the plurality
of detection values and at least one of anticipated state information and
sensor specification;
a sensor fault detection circuit structured to determine one of a sensor fault
status and a sensor validity status of at least one sensor
in response to the plurality of detection values and at least one of
anticipated state information and sensor specification; and
a response circuit structured to perform at least one operation in response to
one of a sensor overload status, a sensor health status,
and a sensor validity status.
[0944] 2. A monitoring system of claim 1, the system further comprising a
mobile data collector for collecting data from the
plurality of input sensors.
[0945] 3. The monitoring system of claim 1, wherein the at least one operation
comprises issuing an alert or an alarm.
[0946] 4. The monitoring system of claim 1, wherein the at least one operation
further comprises storing additional data in the
data storage circuit.
[0947] 5. The monitoring system of claim 1, the system further comprising a
multiplexor (MUX) circuit.
[0948] 6. The monitoring system of claim 5, wherein the at least one operation
comprises at least one of enabling or disabling one
or more portions of the multiplexer circuit and altering the multiplexer
control lines.
[0949] 7. The monitoring system of claim 5, the system further comprising at
least two multiplexer (MUX) circuits and the at least
one operation comprises changing connections between the at least two
multiplexer circuits.
[0950] 8. The monitoring system of claim 7, the system further comprising a
MUX control circuit structured to interpret a subset
of the plurality of detection values and provide the logical control of the
MUX and the correspondence of MUX input and detected
values as a result, wherein the logic control of the MUX comprises adaptive
scheduling of the multiplexer control lines.
[0951] 9. A system for data collection, processing, and component analysis in
an industrial environment comprising:
a plurality of monitoring devices, each monitoring device comprising:
a data acquisition circuit structured to interpret a plurality of detection
values, each of the plurality of detection values corresponding
to at least one of a plurality of input sensors;
a data storage for storing specifications and anticipated state information
for a plurality of sensor types and buffering the plurality
of detection values for a predetermined length of time;
a signal evaluation circuit comprising:
an overload identification circuit structured to determine a sensor overload
status of at least one sensor in response to the plurality
of detection values and at least one of anticipated state information and
sensor specification;
a sensor fault detection circuit structured to determine one of a sensor fault
status and a sensor validity status of at least one sensor
in response to the plurality of detection values and at least one of
anticipated state information and sensor specification; and
a response circuit structured to perform at least one operation in response to
one of a sensor overload status, a sensor health status,
and a sensor validity status;
a communication circuit structured to communicate with a remote server
providing one of the sensor overload status, the sensor
health status, and the sensor validity status and a portion of the buffered
detection values to the remote server; and
a monitoring application on the remote server structured to:
receive the at least one selected detection value and one of the sensor
overload status, the sensor health status, and the sensor validity
status;
jointly analyze a subset of the detection values received from the plurality
of monitoring devices; and
recommend an action.
[0952] 10. The system of claim 9, at least one of the monitoring devices
further comprising a mobile data collector for collecting
data from the plurality of input sensors.
[0953] 11. The system of claim 9, wherein the at least one operation comprises
issuing an alert or an alarm.
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[0954] 12. The monitoring system of claim 9, wherein the at least one
operation further comprises storing additional data in the
data storage circuit.
[0955] 13. The system of claim 9, at least one of the monitoring devices
further comprising further comprising a multiplexor
(MUX) circuit.
[0956] 14. The system of claim 13, wherein the at least one operation
comprises at least one of enabling or disabling one or more
portions of the multiplexer circuit and altering the multiplexer control
lines.
[0957] 15. The system of claim 9, at least one of the monitoring devices
further comprising at least two multiplexer (MUX) circuits
and the at least one operation comprises changing connections between the at
least two multiplexer circuits.
[0958] 16. The monitoring system of claim 15, the system further comprising a
MUX control circuit structured to interpret a subset
of the plurality of detection values and provide the logical control of the
MUX and the correspondence of MUX input and detected
values as a result, wherein the logic control of the MUX comprises adaptive
scheduling of the multiplexer control lines.
[0959] 17. The system of claim 9, wherein the monitoring application comprises
a remote learning circuit structured to analyze
sensor status data together sensor data and identify correlations between
sensor overload and data from other systems.
[0960] 18. The system of claim 9, the monitoring application structured to
subset detection values based on one of the sensor
overload status, the sensor health status, the sensor validity status, the
anticipated life of a sensor associated with detection values,
the anticipated type of the equipment associated with detection values, and
operational conditions under which detection values
were measured.
[0961] 19. The system of claim 9, wherein the supplemental information
comprises one of sensor specification, sensor historic
performance, maintenance records, repair records and an anticipated state
model.
[0962] 20. The system of claim 19, wherein the analysis of the subset of
detection values comprises feeding a neural net with the
subset of detection values and supplemental information to learn to recognize
various sensor operating states, health states, life
expectancies and fault states utilizing deep learning techniques.
[0963] Figure 101 shows a system for data collection in an industrial
environment having a self-sufficient data acquisition box for
capturing and analyzing data in an industrial environment including sensor
inputs 11700, 11702, 11704, 11706 that connect to a
data circuit 11708 for analyzing the sensor inputs, a network communication
interface 11712, a network control circuit 11710 for
sending and receiving information related to the sensor inputs to an external
system and a data filter circuit configured to dynamically
adjust what portion of the information is sent based on instructions received
over the network communication interface. A variety
of sensor inputs X connect to the data circuit Y. The data circuit
intercommunicates with a network control circuit, which is
connected to one or more network interfaces. These interfaces may include
wired interfaces or wireless interfaces, communicating
via a star, multi-hop, peer-to-peer, hub-and-spoke, mesh, ring, hierarchical,
daisy-chained, broadcast, or other networking protocol.
These interfaces may be multi-pair as in Ethernet, or single-wire networking
protocol such as I2C. The networking protocol may
interface one or more of a variety of variants of Ethernet and other protocols
for real-time communication in an industrial network,
including Modbus over TCP, Industrial Ethernet, Ethernet Powerlink,
Ethernet/IP, EtherCAT, Sercos, Profinet, CAN bus, serial
protocols, near-field protocols, as well as home automation protocols such as
ZigBee, Z-Wave, or wireless WWAN or WLAN
protocols such as LTE, WiFi, Bluetooth, or others. The sensor inputs can be
permanently or removably connected to the thing they
are measuring, or may be integrated in a standalone data acquisition box. The
entire system may be integrated into the apparatus
that is being measured, such as a vehicle (e.g., a car, a truck, a commercial
vehicle, a tractor, a construction vehicle or other type of
vehicle), a component or item of equipment (e.g., a compressor, agitator,
motor, fan, turbine, generator, conveyor, lift, robotic
assembly, or any other item as described throughout this disclosure), an
infrastructure element (such as a foundation, a housing, a
wall, a floor, a ceiling, a roof, a doorway, a ramp, a stairway, or the like)
or other feature or aspect of an industrial environment.
The entire system may be integrated into a stationary industrial system such
as a production assembly, static components of an
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assembly line subject to wear and stress (such as rail guides), or motive
elements such as robotics, linear actuators, gearboxes, and
vibrators.
[0964] Figure 102 shows an airborne drone 11730 data acquisition box with
onboard sensors 11732 and four motors 11734 to
provide lift and movement control and at least one camera 11788. In
embodiments, the drone 11730 has a charging dock capability
and in embodiments, a battery changing capability so that the same drone 11730
can return to inspection after a brief return to base
for battery replacement. The drone 11730 can travel from a location near the
systems to be sensed. The drone 11730 can detect the
presence of other sensor drone and avoid collisions based on both active
sensors and network-coordinated flight plans. These sensor
drones 11730 inspect and sense environmental and apparatus conditions based on
scheduled tours of sensor reconnaissance. They
also respond to specific events, either command driven (human requests for
additional data), requests from other drone s, events
such as a detected anomaly in an item to be sensed with more scrutiny e.g.
sensing by multiple drone s with multiple sensors. They
respond to Al both integrated into the drone 11730 or located in a remote
server, that analyzes conditions and generates a request
for additional data and inspection of an environment or apparatus. The drone
11730 can be configured with multiple sensors 11732.
For instance, most drones 11730 are equipped with some sort of visual sensor,
either in visual light or infrared range, as well as
certain forms of active guidance sensor technology such as light-pulse
distance sensing, sonar-pulse sensing. In addition, drones
11730 can be equipped with additional sensors such as specific chemical
sensors and magnetic sensors designed to analyze the
materials of specific apparatus and machinery.
[0965] Figure 103 shows an autonomous drone 11780 with multiple modes of
mobility, optionally including flight, rolling and
walking modes of mobility. In embodiments, telescoping and articulating
robotic legs allow positioning on uneven surfaces. In
embodiments, the drone may have four wheels. The various mobile platforms may
include articulating legs can pull up and away to
allow rolling on wheels on smooth surfaces. The legs may include end members
(e.g., "feet") that may be enabled with various
forms of attachment by which the drone may attach to an element of its
environment, such as a landing spot on a piece of industrial
equipment proximal to a point of sensing (e.g., near a set of bearings of a
rotating component). The end members may be enabled
with various forms of attachment, such as magnetic attachment, suction cups,
adhesives, or the like. In embodiments, the drone
may have multiple forms that can be engaged by alternative mechanisms on end
members (e.g., rotating between elements with
different attachment types) or that can be retrieved by the articulating legs
from a storage location on the drone. In embodiments,
the drone 11780 may have a robotic arm 11782 that has the ability to place an
adhesive-backed hook and loop fastener element onto
a machine to allow attachment, disengagement and reattachment by the drone at
a desired landing point. Placement may be
undertaken under control of a vision system, which may include a remote-
control vision or other sensing system and/or an automated
landing system that recognizes a type of landing point and automatically,
optionally with pattern recognition and machine learning,
can land the drone and initiate attachment. Placement may be based both on the
recognition (including by machine vision or sensor-
based recognition) of an appropriate sensing location (such as based on an
identified need for sensing, a trigger or input, or the like)
and of an appropriate landing position (such as where the drone can establish
a stable attachment and reach the point of sensing,
such as with an articulating robotic arm). In embodiments, a camera system and
other sensors can detect surface geometry and
characteristics to select appropriate landing and engagement modes (e.g., a
rough vertical surface, if recognized, can trigger use of
legs and articulated fingers to hold on, while a smooth vertical surface, if
recognized, can trigger use of suction cups or magnets to
establish temporary attachment).
[0966] In embodiments, machine learning can vary and select landing and
engagement modes by variation and selection, including
testing security of various forms of attachment. Machine learning can be, or
be initiated using, a set of rules for landing and
engagement, a set of models (which may be populated with information about
machines, infrastructure elements and other features
of an industrial environment), a training set (including one created by having
human operators land a set of drones and engage with
sensors), or by deep learning approach fusing various vision and other sensors
through a large set of trial landing and engagement
events.
[0967] In embodiments, a camera 11788 may have object recognition capabilities
(including pattern recognition improved by
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machine learning, rule-based pattern matching to library of images of machines
and other features, or a hybrid or combination of
techniques).
[0968] In embodiments, sensor-based recognition of industrial machines may be
provided, where a machine is recognized based
on sensor signatures (e.g., based on matching to known vibration patterns,
heat signatures, sounds, and the like that characterize
generators, turbomachines, compressors, pumps, motors, etc.). This may occur
based on rules, models, or the like, with machine
learning (including deep learning or learning based on human-generated
training sets), or various combinations of these.
[0969] In embodiments, as depicted in Figures 103 and 104, the mobile
platforms may contain one or more multi-sensor data
collectors (MDC) 11790 may be disposed on one or more articulating robotic
arms 11782, which may move from the interior to the
exterior of the drone 11730. In embodiments, the drone may have one or more of
its own articulating robotic arm(s) 11782, such as
for picking up and placing individual sensors, attaching sensors to a point of
sensing, attaching sensors to power sources, reading
sensors, or the like.
[0970] In embodiments, as depicted in Fig. 105, the MDC 11790 can swap in and
out various sensors, both at the point of sensing
and by interacting with a central station 11792, where the drone 11730 can
replenish the MDC 11790 with new or different sensors,
can re-stock any disposable or consumable elements (such as test strips,
biological sensors, or the like) or the like. Replenishment
and re-stocking can be undertaken with control elements described throughout
this disclosure that involve selection of sensor sets,
including rule-based, model-based, and machine learning control within an
expert system.
[0971] In embodiments, a drone 11730 can be paired with the central station
11792, such as for wireless re-charging, re-stocking
of sensors, secure file downloads (e.g., requiring physical connection and
verification such as a port 11802), or the like. The central
station 11792 may have network communication with a remote operator (including
an expert system) and/or with local operators,
such as via one or more applications, such as mobile applications, for
controlling elements of the drone 11730 or central station
11792 or for reporting or otherwise using information collected by the drone
11730 or the central station 11792.
[0972] In embodiments, the central station 11792 can have a 3D printer, such
as for printing suitable connectors for interfacing
with machines, for printing disposable or consumable elements used in sensors,
for printing elements such as end members for
assisting with landing, and the like.
[0973] In embodiments, the MDC 11790 has interface ports for various forms of
interface, including physical interfaces (e.g.,
USB ports, firewire ports, lighting ports, and the like) and wireless
interfaces (e.g., Bluetooth, Bluetooth Low Energy, NFC, Wifi
and the like).
[0974] In embodiments, MDC 11790 interfaces can include electrical probes,
such as for detecting voltages and currents, such as
for detecting and processing operating signatures of electrical components of
an industrial machine.
[0975] In embodiments, the MDC 11790 carries or accesses (such as within the
drone 11730, or the central station 11792) various
connectors to allow it to interface with a wide variety of machines and
equipment.
[0976] In embodiments, the camera 11788 can identify a suitable interface port
for an industrial machine and select and under user
remote control or automatically (optionally under control of an expert system
disposed on the drone 11730 or located remotely) use
the appropriate connector for the interface port, such as to establish data
communication (e.g., with an onboard diagnostic or other
instrumentation system), to establish a power connection, or the like.
[0977] In embodiments, the robotic arm 11782 of the MDC 11790 can insert one
or more cables or connectors as needed, such as
ones retrieved from storage of the drone 11730 or from a central station. The
central station can print a new connector interface as
needed.
[0978] In embodiments, the drone 11730 is self-organizing and can be part of a
self-organizing swarm that includes intelligent
collective routing of several drones 11730 for data collection. The drone
11730 can have and interact with a secure physical interface
for data collection, such as one that requires local presence in order to get
access to control features.
[0979] The drone 11730 may use wireless communication, including by a
cognitive, ad hoc mobile network of a mesh network of
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drones 11730, which mesh network may also include other devices, such as a
master controller (e.g., a mobile device with human
interface).
[0980] In embodiments, the drone 11730 has a touch screen display for user
interaction and mobile application interaction.
[0981] In embodiments, the drone 11730 can use the MDC 11790 to collect data
that is relevant to placement of sensors for
instrumentation of machines (e.g., collect vibration data from a set of
possible locations and select a preferred location for data
collection, then dispose a semi-permanent vibration sensor there for future
data gathering).
[0982] Intelligent routing can include machine-based mapping, including
referencing a pre-existing map or blueprint of an
industrial environment and using machine learning to update the map based on
detected conditions (e.g., detecting by camera, IR,
sonar, LIDAR, etc. the presence of features, machines, obstacles or the like,
whether fixed or transient and updating the map and
any relevant routes to reflect changing features).
[0983] In embodiments, the drone 11730 may include a facility for sensor-based
detection of biological signatures (e.g., IR-sensing
for base-level recognition of presence of humans, such as for safety), as well
as other physiological sensors, such as for identity
(e.g., using biometric authentication of a human before permitting access to
collected data or control functions) and human status
conditions (such as determining health status, alertness or other conditions
of humans in the environment). In embodiments, the
drone 11730 may store or handle emergency first aid items, such as for
delivery to a point of emergency in case that an emergency
health status is determined.
[0984] In embodiments, the drone 11730 can have collision detection and
avoidance (LIDAR; IR, etc.), such as to avoid collisions
with other drones 11730, equipment, infrastructure, or human workers.
[0985] In another embodiment, the system in Figure 103 is informed, based on a
scheduled event, to evaluate the condition of
various aspects of a factory floor. The system, configured with a learning
algorithm, takes samples of various sensors in various
positions. It is provided with positive reinforcement of a correctly operating
factory floor on a regular basis. When there is a fault it
will be instructed to evaluate the condition of various aspects and taught
that there is a fault. It records the sensor data such as
temperature, speed of motion, position sensors. It also integrates additional
sensor data such as data from sensors that are integrated
into the system to be analyzed, such as position, temperature, and structural
integrity sensors integrated in a rail guide in an assembly
line. These sensors communicate sensor data including real-time and historical
sensor data to the system via a one of the network
communication interfaces.
[0986] In another embodiment, the system in Figure 103 has a robotic arm and
carries with it numerous attachable modules each
of which provides sensing of a different type of signal or data. For instance,
the system may carry with it four modules, capable of
sensing temperature, magnetic waves, lubricant contamination, and rust. It is
capable of attaching and detaching and securely storing
each type of module. The mobile drone 11730 is capable of returning to a
charging station and selecting additional modules to
measure additional types of signal. For instance, the system may receive an
indication that a portion of a factory has a fault in the
area where a vibrator is designed to shake tiny components into hopper which
pours into a conveyer belt, which feeds into a pick-
and-place robotic arm comprising gear boxes and actuators. The system, having
received an indication that there is a failure mode
such as a slowdown or jam in this general area, retrieves a chemical analysis
module and tests the viscosity and chemical condition
of the lubricant in the mechanical vibrator. It then retrieves a different
chemical analysis module to analyze a different type of
lubricant used in the gear box and actuator of the robotic arm. It then,
delivering the data over a network interface and receiving an
indication to continue testing, retrieves a new module capable of detecting
mechanical faults as well as a visual camera module.
Having retrieved these modules, the system then performs a visual analysis of
the parts of the assembly line and sends them to a
remote server (or keeps them locally) to be compared with historical pictures
of the same portion of assembly line. The system
continues in this way until all of the sensors which an external system has
specified (such as a manually controlling human or a
predetermined list) have been completed, or until one of the sensors detects
an anomaly which is quantified and communicated to
an external system to propose a repair.
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[0987] Figure 104 shows a drone data acquisition system which is movably
attached to a track and which can, through translational
motion and repositioning of a sensor arm, position itself in proximity to a
portion of a system to be sensed and diagnosed for failure
modes. The robotic arm 11782 is capable of positioning, for instance, a highly
sensitive metallurgical fault detection system such
as an x-ray or gamma-ray radiograph or a non-destructive scanning electron
microscope. The robotic arm 11782 positions its sensing
arm and measurement device in various positions on a static or dynamically
moving target such as a set of rolling bearings in an
assembly line. The robotic arm 11782 of the system performs high-resolution
image capture and failure mode detection on the
structural aspects of the roller bearings such as detecting if there are any
roller bearing failure modes such as pitting, bruising,
grooving, etching, corrosion, etc. The system then communicates the findings
of the failure mode detection to a remote system over
a network interface.
[0988] In another embodiment, the data acquisition system of Figure 104
continually performs a predetermined set of
measurements over time and compares these over time. For instance, it can
measure the decibels of sound received at a precisely
positioned directional sound input sensor aimed at each of a set of roller
bearings over time. When, after some time a roller bearing
diverges from the usual or common or specified decibel range for audio, the
failure mode of that specific roller bearing is indicated,
and the system then communicates the findings of the failure mode detection to
a remote system over a network interface.
[0989] Figure 105 shows a stationary guide rail 11800 in an industrial
environment, and below it, a pair of ports 11802 including
a network interface jack and a power port jack. A mobile data acquisition
system such as a flying drone 11730 or wheeled sensor
robot approaches the guide rail and uses a moving extension to "jack in" to
the ports. At this point, the system can continue to
operate indefinitely because it is in network communication and has continuous
power. In embodiments, a remote operating user
can now activate any of the sensors available to the mobile system and direct
them to any reachable portion of the target, including
the rail guide and any machinery moving on the guide. The rail guide can be
chemically inspected, visually inspected, the portion
of the assembly line in which the rail guide operates can be visually
monitored by the remote user operating through the system
sensor, the system can perform auditory testing of the machinery operating and
moving along the rail guide. Any sensors embedded
in the rail guide can communicate their sensor data to the attached roving
system. Similarly, the sensor input from the attached
roving system can be integrated with any embedded sensor data from the rail
guide and delivered together with it over the wired
network interface. Any drone 11730 connected to hover in proximity to the rail
guide and its associated functionality can operate
indefinitely and provide "zoomed in" monitoring of that portion of the
assembly line. If a portion of an assembly line indicated a
fault, a group of drones and wheeled data acquisition systems can be recruited
to more closely monitor that area. In the case of a
remote human operator, this additional sensor visibility affords them numerous
real-time streams of sensor information on various
aspects of the portion of the assembly line. The remote human operator can
reposition and change the sensing modes of the various
data acquisition systems. In another embodiment, a remote machine learning
system operates the multiple sensing systems to zoom
in and acquire additional data about the area of the assembly line that has
been detected to be at fault. Through iterative trials and
feedback, the machine learning system operates the data acquisition systems to
test different signals with different sensors in
different positions until one or more failure modes have been positively
diagnosed. The machine learning system then takes
appropriate action such as disabling that section of the assembly line to
prevent loss of value from further damage, communicating
to an on-site operator what the diagnosed fault was, automatically ordering
the correct parts for delivery and creating a trouble ticket
in a repair system, automatically calling a service technician to go to the
location and repair the fault, estimating the total predicted
downtime and automatically updating an accounting system with the modified
throughput based on when the system will be
producing again.
[0990] Figure 106 shows a portion of the drive train 11810 and chassis of a
vehicle 11812 such as a car or truck for transportation
or an industrial vehicle such as a tractor for use in construction or farming.
It consists of an engine 11814 a transmission 11818, a
propeller shaft 11820, a rear differential gear box 11822, axles, and wheel
ends. The various sensor drones disclosed herein can
sense, monitor, analyze and re-monitor the vehicle 11812. The sensor drone
11730 may be airborne during its data recording. The
sensor drone 11840 may be connected to the vehicle during the entire assembly
process or at certain stations in the process. Figure
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109 shows a portion of a turbine 11900. The various sensor drones disclosed
herein can sense, monitor, analyze and re-monitor the
turbine 11900. The sensor drone 11730 may be airborne during its data
recording. The sensor drone 11840 may be connected to
the vehicle during the entire assembly process or at certain stations in the
process. These various components are metallic and are
subject to wear and damage from overuse and underuse outside their duty cycle
and working output range. In order to operate this
equipment and maintain these various components in proper order, numerous
sensors are disposed throughout these.
Conventionally, the most active elements such as the transmission contain
numerous sensors which are used to operate the device
correctly and provide feedback, but not necessarily to diagnose or monitor the
health or failure modes of the device. These sensors
include throttle position sensors, mass air flow sensors, brake sensors
various pressure and temperature, and fluid level sensors.
These same sensors along with numerous other additional sensors can be used
not only for operation but for maintenance and
diagnosis of the device. Additional sensors which can be permanently installed
and distributed throughout include lubricant
pollution chemical sensors such as solid-state sensors, gear position sensors,
pressure sensors, fluid leak sensors, rotational sensors,
bearing sensors, wheel tread sensors, visual sensors, audio sensors, and
numerous other sensors listed herein.
[0991] Figure 107 shows a micro, mobile magnetically driven attachable drone
sensor system 11840 that attaches to metal and
can be used to perform analysis of a vehicle in motion or at rest. It consists
of a small rectangular or square mobile sensor unit which
can be sized smaller than a matchbox. It has numerous wheels or castors or
ball bearings and it attaches to metal using a permanent
or electromagnet. It can be curved to mate more easily to curved surfaces such
as a rear differential or drive or propeller shaft.
[0992] Figure 108 shows a closer view of the mobile sensor system, showing its
wheels and four sensors, an ultrasonic sensor, a
chemical sensor, a magnetic sensor and a visual (camera) sensor. The system
travels around and throughout the target area for failure
mode detection, such as the undercarriage of a transportation or industrial
vehicle. The sensor captures comprehensive data and is
capable of covering the entire surface and undercarriage of the vehicle and
can detect faults such as rusted out components, chemical
changes, fluid leaks, lubricant leaks, foreign contamination, acids, soil and
dirt, damaged seals, and the like. The sensor system
reports this information over a network interface to another sensor, to a
computer on the vehicle itself, or to a remote system in order
to facilitate data capture and ensure that the data is fully recorded. The
system also runs on a periodic basis performing the same or
similar coverage of the vehicle so that a baseline measurement can be compared
with later measurements to determine the state of
maintenance of the vehicle. This can be used to detect failure modes but can
also be used to create an image of the vehicle for
insurance, for depreciation, for maintenance scheduling, or surveillance
purposes.
[0993] In embodiments, the mobile attaching drone sensor 11840 can be
removably attached to a portion of a vehicle and can
move freely around the undercarriage of a vehicle. It can also be placed there
as a sensing module by the mobile robotic sensor
system of Figure 103 and subsequently retrieved when it has completed its
sensing tasks.
[0994] In embodiments, the mobile attaching sensor 11840 may take the form of
a swimming device that can travel through fluid,
or a multi-pedal unit with chemically-adhesive or magnetic or vacuum-adhesive
pods or feet that allow it to move freely on the
surface of a target to be sensed.
[0995] In embodiments, the modular sensors shown in Figure 103 can be
removeably or permanently integrated into mobile or
portable sensors such as drones, multi-pedal or wheeled industrial measurement
robots, or self-propelled floating, climbing,
swimming, or magnetically crawling micro-data acquisition systems Any of the
sensors can take multiple measurements from
different positions on the same target to get a fuller picture of the health
or condition of the target.
[0996] The sensors deployed on the various drones, mobile platforms, robots,
and the like may take numerous forms. For instance,
a set of roller bearing sensors may be integrated within the roller bearing
itself, using the energy off the motion of the roller bearing
to generate an inductive force sufficient to generate data signals to
communicate to a data circuit the state of the roller bearing, such
as velocity, rotations per unit time, as well as analog data indicating any
minor perturbations in the smooth rotation of the bearing
over time. A deformation sensor can take the form of a passive (visual,
infrared) or active scanning (Lidar, sonar) system that
captures data from a target and compares it to historical data on the shape or
orientation of the component to detect variations.
Camera sensors are configured with a lens to capture rinititillnliQ find Qti11
vkihle nild invisible photon information cast upon or
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reflected by a target. Ultraviolet sensors can similarly capture continuous
and still frame information about a target and its surrounds.
Infrared sensors can capture light and heat emission data from a target. Audio
sensors such as directional and omnichannel
microphones can measure the frequency and amplitude of sonic wave data
emitting from a target or its environment, and this data
can be compared over time to detect anomalies when the amplitude or quality of
the sound generated by the target exceeds or varies
from predetermined or historical levels. Vibration sensors can be used in a
similar manner, capturing extremely low frequency
sound as well as physical perturbations and rhythms of a target over time.
Viscosity sensors can be installed in-line in the lubrication
system of a system or vehicle or can be movable and make ad-hoc measurements
and evaluations of the continuous or instantaneous
viscosity of the lubricating material for a target. Chemical sensors can vary
widely in what analyte (target chemical) they detect,
and in the case of vehicles or stationary machinery, can be configured with
variable receptors capable of capturing and recognizing
numerous conditions of a target. Specific target sensors such as rust sensors
or overheat sensors can sense when a target such as an
apparatus, metal structure or chemical lubricant has started to change
chemically over time. These chemical sensors can be multi-
or single-purpose, and can be integrated within a structure, such as the frame
or chassis of a vehicle or the stationary or movable
portions of an assembly line, or the mechanical motive power of an engine or
robotic machinery. Or they can be attached to a
portable self-propelled data acquisition system that is deployed to measure
the target. When activated these chemical sensors make
contact or take samples from the target and perform chemical analysis and
report the state of the results to a data circuit. A solid
chemical sensor can take solid chemical samples (rather than gaseous or liquid
samples) and determine the presence of a particular
chemical or the composition by detecting multiple chemicals in a sample. A pH
sensor can be used to detect the level of acidity of
a target and can be used to determine specific changes in the environment of a
target, the fluid conditions surrounding a target, or
the state of an operational fluid such as a coolant or lubricant in a target,
and similarly, fluid and gaseous chemical sensors perform
additional component and presence detection on these targets. A lubricant
sensor can be as simple as an indicator of whether
sufficient lubricant is still present (by detecting chafing or a lack of
distance between conductive or hard components) or can use a
combination of chemical, pressure, visual, olfactory, or vibrational feedback
tests (vibrating the target and measuring response) to
determine the instant or continuous presence or quantity of lubricant in a
target. Contaminant sensors can look for the presence of
foreign or damaged elements added to the surface, substance or fluid contents
of a target, such as a lubricant which has been
contaminated with metal particles from component wear, or when a lubricant or
motive fluid such as in a pneumatic has been
contaminated due to the breaking of a seal. Particulate sensors can detect the
presence of specific types of particles within a fluid or
on a target. Weight or mass sensors can determine the continuous or changing
weight of a component, and can be on coarse scale
such as a weighing device for weighing large machinery down to an integrated
MEMS scale that determines the continuous and
instantaneous changes in weight of a target that may lose mass over time due
to damage or abrasion or evaporation, sublimation,
etc. A rotation sensor can be optical, audio-based, or use numerous other
techniques to detect the periodic acceleration, velocity and
frequency of rotation of a target. Temperature sensors can be configured to
measure coarse environmental temperature in a general
area as well as fine, precise temperature of a region of a target component
and can be disposed throughout an engine, a robotic
system, or any stationary or moving component. Temperature sensors can also be
mobile and deployed to take periodic or ad-hoc
measurements of a target component, surface, material or system to determine
if it is operating in a correct temperature range.
Position sensors can be as simple as interrupted visual reflections, to visual
systems with image-recognition algorithms being
performed on continuous video, to magnetic or mechanical switch systems that
durably detect either precisely or coarsely the
position of various moveable elements with respect to one another. Ultrasonic
sensors can be used for a variety of distance, shape,
solidity and orientation measurements by projecting ultrasonic energy in the
direction of a target or group of targets or measuring
the reflected ultrasonic energy reflected by those targets. Ultrasonic sensors
may comprise multiple emitters and receivers in order
to add dimensions and precision to the measurements and even produce 2D or 3D
outlines of a region for further analysis. A radiation
sensor can detect the presence of forms of radioactivity as alpha, beta, gamma
or x-ray radiation and some can identify the directional
source, the field and area of the radiation and the intensity. An x-ray
radiograph can actively determine structure, structural changes
and structural defects as well as providing a visual depiction of otherwise
obscured physical characteristics of a target. Similarly, a
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