Note: Descriptions are shown in the official language in which they were submitted.
CA 02899069 2015-07-30
SYSTEM AND METHOD FOR DETERMINING
A LUBRICANT DISCARD INTERVAL
RELATED APPLICATION
[0001] This application is a continuation-in-part of application Serial
No. 13/363,433,
filed February 1, 2012, now pending.
FIELD OF THE DISCLOSURE
[0002] The present disclosure relates to a system, a method, and a
computer program for
determining usability of lubricants and when to replace the lubricants in, for
example, an
engine, a power transmission device, a turbine, a generator, a motor, or the
like.
BACKGROUND AND SUMMARY OF THE DISCLOSURE
[0003] Engines (or motors) are designed to convert one form of energy
(such as, for
example, fuel combustion, electricity, nuclear reactions, and the like) to
mechanical
energy, such as, for example, mechanical motion. For instance, combustion
engines
convert fuel combustion energy to motion energy. These engines typically
include one or
more combustion chambers that contain and confine the combustion of a fuel
(e.g., a
fossil fuel), allowing the resultant high temperature and high pressure gases
to expand
and drive mechanical components such as, for example, pistons, turbine blades,
or the
like.
[0004] Internal combustion engines are typically used in vehicles,
including, e.g.,
motorcycles, scooters, automobiles, boats, trucks, locomotives, watercraft,
aircraft, ships,
gas turbines, generators, heavy duty machinery, and the like. During operation
of, for
example, an internal combustion engine that comprises one or more pistons, a
piston may
be driven by expanding gases resulting from the combustion of the fuel in the
chamber,
causing the piston to move along a predetermined path for a predetermined
distance
along a length of the chamber. The piston may be connected to a crankshaft
through a
connecting rod to translate the movement of the piston to a rotation of the
crankshaft.
The engine may further include an intake valve or port and an exhaust valve or
port. The
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engine may comprise any number of sets of pistons, connecting rods and
chambers. The
various moving parts of the engine cause friction, which results in the wear
of the moving
parts and diminished power output of the engine.
100051 Most of the moving parts in the engine are made of metal. During
operation,
metal to metal contact of the moving parts causes wear on the moving parts. To
minimize wear of the moving parts, and, therefore, to maximize engine
durability and
longevity, a lubricant (e.g., an engine oil) is used to lubricate the moving
parts in the
engine. The lubricant may also function to clean, inhibit corrosion, improve
sealing, and
cool the engine by carrying heat away from the moving parts. The lubricant
reduces
friction by, for example, creating a separating film between surfaces of
adjacent moving
parts to minimize direct contact between the surfaces, decreasing heat caused
by the
friction and reducing wear.
100061 Most lubricants are made from a petroleum hydrocarbon derived from
crude oil.
Alternatively (or additionally), the lubricants may be made from synthetic
materials, such
as, e.g., synthetic esters, polyalphaolefins, and the like. Additives are
added to the
lubricant to maintain or improve certain properties of the lubricant. The
additives may
include, for example, detergents, dispersants, corrosion inhibitors, alkaline
additives, and
the like. One of the most important properties of lubricants is to maintain a
lubricating
film between the moving parts of the engine. Another important property of
lubricants is
its ability to neutralize acids.
100071 In engines, the lubricants are exposed to the byproducts of internal
combustion,
including, for example, carbonaceous particles, metallic particles, and the
like. During
operation of the engine, the lubricants undergo both thermal and mechanical
degradation,
and contamination which impairs their function. Eventually the loss of
performance may
become significant enough to necessitate removal of the used lubricant and
replacement
with a fresh lubricant. Thus, time-based (e.g., 92 days, 184 days, 276 days,
every 6
months, or the like) and/or distance-based (e.g., every three thousand miles,
every five
thousand miles, or the like) lubricant drainage intervals (LDIs) are typically
used in
determining when to replace the lubricants in an engine.
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[0008] In
the railroad industry, engine oil samples are typically taken from locomotive
engines about every 2 to 3 weeks. These samples are then analyzed to identify
problems,
such as, e.g., coolant leaks, fuel dilution, metal wear, oil deterioration,
improper oil in
use, and the like. The railroads schedule oil change intervals based on, e.g.,
original
equipment manufacturer (OEM) recommendations, operating history, and the like.
Currently, a common industry practice for drain intervals is about every 184
days.
However, this drain interval may be too long for some engines, such as, e.g.,
engines that
are operated under severe conditions, or engines that are experiencing
performance
issues, or new engines that have just been placed into service and are
susceptible to
break-in wear. Further, the time between drain intervals may be shorter than
optimal for
some engines, such as, e.g., engines that are operated under ideally optimal
conditions.
Also, new engines may require more frequent oil changes than older engines.
[0009] In
the trucking industry, for example, truck fleets have often utilized oil
analysis
to establish oil drain intervals for entire fleets. The oil drain intervals,
however, are based
on fleets rather than individual engines. Again, the established oil drain
intervals may be
too long for some engines, while shorter than necessary for others.
[00010] While lubricant drainage intervals are typically set based on the time
in service or
the distance that a vehicle has traveled, actual operating conditions and
engine hours of
operation may vary drastically for a give time in service or a distance
traveled by a
vehicle. Thus, fixed time/distance lubricant discard (or drain) intervals may
result in the
continued use of spent engine lubricant where an engine is operated under
severe
conditions or where the engine is not operating properly, which may result in
poor fuel
efficiency, costly maintenance, premature engine failure, and the like. The
fixed
time/distance lubricant discard intervals may also result in the premature,
and therefore,
inefficient discarding of engine lubricant that remains unspent at the discard
interval,
thereby increasing the amount of waste byproduct to be disposed of, as well as
the costs
associated with the replacement of the engine lubricant (including, e.g., the
cost of the
lubricant, the cost of labor to replace the lubricant, disposal costs, engine
down time
costs, and the like).
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[00011] The engine lubricant may be considered to be spent when, for example,
the
properties of the engine lubricant have been degraded to a point where the
engine
lubricant ceases to properly lubricate the engine parts, inhibit corrosion, or
the like.
[00012] Although it would seem ideal to analyze the condition of used oil from
each piece
of equipment and only change it when the analysis indicates it is close to the
end of its
useful life, there are other costs to consider in determining the most cost
effective time to
change oil. In their use, engines contribute to revenue production making it
costly to take
them out of service. As a consequence many maintenance tasks for equipment are
preplanned and grouped together enabling these tasks to be performed during a
planned
shutdown of the equipment, or when many of the tasks can be performed
simultaneously
to minimize downtime. Equipment operators usually schedule maintenance to
optimize
overall cost. This means that to maximize production, individual maintenance
tasks may
be performed before they are actually needed.
[00013] Some maintenance tasks need to be performed more frequently than
others.
Preplanned maintenance is often based around a set of schedules. For example a
fleet of
trucks may have an A schedule every 30 days, a B schedule every 60 days, and a
C
Schedule every 120 days. A truck coming in for its first maintenance after 30
days would
have all the services performed that are required in Schedule A. 30 days later
it would
have services A and B performed. 30 days after that (90 days cumulative) it
would
require the services in schedule A only. At 120 days of service it would
require all the
procedures in schedules A, B and C. The cycle would then be repeated.
[00014] If the fleet oil drain interval was scheduled for 30 days, and it was
determined that
a 45 day oil change interval would be safe, it is highly unlikely that taking
these trucks
out of service at 45 days only to change oil would be a cost effective
undertaking.
Moving the fleet to a 60 day oil change would be a practical endeavor, if that
was
determined to be a safe drain interval, because it would convert the oil
change from a
schedule A to a schedule B function, cut the oil change costs in half, and not
result in any
new out of service costs. If the oil change happened to be the only item in
maintenance
schedule A, this would result in a productivity improvement because the
equipment
would be taken out of service less frequently.
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[00015]
Because it is often difficult to predict how much useful life remains in a
used oil,
oil change intervals are frequently standardized across like pieces of
equipment in a
business unit. The oil change interval selection can be based on many
different factors
including the business unit's maintenance history with the specific equipment,
the
severity of service, the equipment manufacturer's recommendation, used oil
analysis, etc.
The oil change interval is usually chosen by what the business unit believes
is the lowest
overall cost in the trade-off between maintenance costs, repair costs, and
downtime.
Because no two units are identical, or used in identical service, the oil
change interval is
usually chosen to accommodate the most severe situation. This means that in a
set of like
engines, some engines that are milder or in milder service, and may be able to
operate
quite effectively on longer oil drain intervals.
[00016] A good example is railroad locomotives. These engines require safety
inspections
every 92 days. Oil changes used to be performed every 92 days to coincide with
this out
of service point. Many locomotive fleets have found that conditions are such
that they
can now change oil every 184 days. The next logical oil change interval
increase would
be to 276 days to coincide with a safety inspection. Some locomotives,
particularly some
GE FDL units under some operating conditions, cannot safely go for 276 days
without an
oil change. Thus, an unfulfilled need exists for a system and method to test
used oil and
predict at, for example, 150 days of service, based on the used oil analysis,
which units
should be changed at, e.g., 184 days and which units can safely continue to,
e.g., 276
days without an oil change.
[00017] The foregoing oil change intervals contemplate removing all of the
used oil from
the engine and replacing all of the used oil with fresh oil on a selected oil
change interval.
Methods for predicting such interval typically use a linear regression model
to determine
one or more oil parameters that are predicted to be exceeded at some future
point in time.
While such models may be useful for such predictions, there continues to be a
need for a
more accurate prediction model for determining a lubricant discard (or drain
interval).
[00018] In view of the foregoing and other needs, the disclosure provides a
system, a
method, and a computer program for predicting a lubricant drain interval in an
engine
based on a plurality of analysis parameter values measured in a plurality of
samples of
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used engine lubricant taken from the engine over a period of time. The system
includes a
first input that receives the plurality of analysis parameter values and a
plurality of
historical analysis parameter values for the engine that are indicative of one
or more
characteristics of the used lubricant and stores the plurality of analysis
parameter values
and historical analysis parameter values in a memory of a processor. A second
input that
receives an analysis parameter threshold value for the used lubricant at the
end of a
service interval is stored in the memory of the processor. A determiner
determines a
future analysis parameter value for determining the lubricant drain interval
by performing
modeling of the plurality of analysis parameter values and historical analysis
parameter
values, and comparing the future analysis parameter value to the analysis
parameter
threshold value to determine whether or not the future analysis parameter
value exceeds
(or is less than) the analysis parameter threshold value at the end of the
service interval in
order to provide an output indicating the lubricant discard (or drain)
interval (LDI) in an
engine. The modeling performed by the determiner is selected from a partial
least
squares regression model, a neural network model, a general linear model
regression
model, and the like.
1000191 The determiner may be configured to generate the lubricant drain
interval for the
engine. The determiner may perform modeling on the historical analysis
parameter value
and said analysis parameter value to determine the future analysis parameter
value. The
modeling may comprise: a neural network analysis, a general linear model
regression
analysis, a generalized linear model regression analysis, a principle
components
regression analysis, a partial least squares analysis; and the like. The
determiner may
compare the future analysis parameter value to the analysis parameter
threshold value.
The determiner may generate the lubricant drain interval for the engine based
on the
comparison of the future analysis parameter value to the analysis parameter
threshold
value for the lubricant.
[000201 The first input may receive an additional analysis parameter value,
and the
determiner may perform a neural network analysis, a general linear model
regression
analysis, a generalized linear model regression analysis, a principle
components
regression analysis, a partial least squares analysis; and the like on the
additional analysis
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parameter value. The analysis parameter value may include, for example, oil
pressure,
megawatt hours produced, locomotive unit age, a concentration of iron in the
engine
lubricant sample and the additional analysis parameter value may include, for
example, a
concentration of lead in the engine lubricant sample. The analysis parameter
value and
the additional analysis parameter value may be selected, for example, from
iron, lead, tin,
copper aluminum, boron, oxidation, nitration, potassium, silicon, sodium,
soot, TBN,
water, fuel, sludge, and insolubles in the engine lubricant sample.
[000211 The analysis parameter m is selected, for example, from a group of
analysis
parameters consisting of iron, lead, tin, copper aluminum, boron, oxidation,
nitration,
potassium, silicon, sodium, soot, water, fuel, sludge, insolubles, etc.
1000221 In addition to "used lubricant analysis parameters" described above,
it may be
beneficial to include "non used lubricant parameters" in the predictive model
to ascertain
the appropriate LDI and the need for unit servicing. Some examples of non used
lubricant parameters that may be included in the model for determining the LDI
and their
potential effects on the determination of the LDI may include, but are not
limited to: (1)
oil pressure, wherein a decrease in oil pressure may indicate a water leak and
an increase
in oil pressure may indicate that the oil viscosity or soot content of the oil
is increasing;
(2) run time hours of an engine or unit wherein an increase or decrease in run
time hours
may affect the "aging" rate of the lubricant in the unit or engine; (3) engine
temperature,
wherein high temperature engine conditions may affect the oxidation rate and
volatile
content losses of the lubricant. Volatile content losses may result in a
lubricant viscosity
increase due to thickening of the lubricant in the engine; (4) megawatt hours
produced,
wherein units that produce comparatively high levels of power may increase the
"aging"
rate of the lubricant in the crankcase, while production of low levels of
power may
decrease the "aging" rate of the lubricant; (5) total miles of the unit or
engine between oil
changes, wherein units having higher or lower cumulative miles between oil
changes may
affect the "aging" rate of the lubricant in the engine; and (6) the age of the
engine or unit,
wherein engines or units that are relatively new may exhibit a higher metal
content in the
lubricant due to mating parts wearing or "breaking in" during the early life
of the engine
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,
or unit. All of the foregoing parameters and relationships may be linear, non-
linear, or
interactive with one or more used oil analysis parameters.
[00023] The method may further comprise predicting a probability when the
future
analysis parameter value will exceed the analysis parameter threshold value
based on an
equation derived from a logistic regression analysis or neural network
(classification)
analysis.
[00024] The present disclosure provides a system, a method, and a computer
program for
testing used oil and, using the methodology described herein, and predicting
(or enabling
a user to predict) at, for example, 150 days of service, based on the used oil
analysis,
which units in, e.g., a railroad locomotive fleet should be changed at, for
example, 184
days and which can safely continue to, for example, 276 days without an oil
change.
[00025] According to a further aspect of the disclosure, a method is provided
for selecting
a plurality of engines for an extended lubricant drain interval, the method
comprising:
retrieving lubricant discard interval data for a plurality of engines;
categorizing the
lubricant discard data into at least two categories, including an extended
lubricant discard
interval category and a normal lubricant discard interval category; and
generating a
lubricant discard interval schedule for the plurality of engines. The extended
lubricant
discard interval category may, for example, comprise 276 days and a normal
lubricant
discard interval category comprises 184 days.
[00026] According to a still further aspect of the disclosure, a method is
provided for
predicting a lubricant drain interval in an engine based on an analysis
parameter value
that is measured in a sample of engine lubricant taken from the engine, the
method
comprising: receiving at a first input the analysis parameter value; receiving
at a second
input an analysis parameter threshold value; and predicting a future analysis
parameter
value based on the analysis parameter value and the analysis parameter
threshold value.
[00027] According to a still further aspect of the disclosure, a computer
readable medium
may be provided that comprises a computer program, as described herein, for
carrying
out the process described herein.
[00028] Additional features, advantages, and embodiments of the disclosure may
be set
forth or apparent from consideration of the detailed description and drawings.
Moreover,
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it is noted that the foregoing summary of the disclosure and the following
detailed
description and drawings provide non-limiting examples of the disclosure,
which are
intended to provide explanation without limiting the scope of the disclosure
as claimed.
BRIEF DESCRIPTION OF THE DRAWINGS
[00029] The accompanying drawings, which are included to provide a further
understanding of the disclosure, are incorporated in and constitute a part of
this
specification, illustrate embodiments of the disclosure and together with the
detailed
description serve to explain the principles of the disclosure. No attempt is
made to show
structural details of the disclosure in more detail than may be necessary for
a fundamental
understanding of the disclosure and the various ways in which it may be
practiced. In the
drawings:
[00030] FIG. 1 is a graphical representation of a multilayer perceptron and
radial basis
neural network used for analysis according to an embodiment of the disclosure;
[00031]
FIG. 2A shows an example of a system that determines the usability of a
lubricant and when to replace the lubricant;
[00032] FIG. 2B shows a representation of a determiner module that may be
included in
the system of FIG. 2A;
[00033] FIG. 3 shows an example of a lubricant analysis process for analyzing
a sample of
an engine lubricant;
[00034] FIG. 4 shows an example of an engine lubricant discard interval
determination
process for determining the usability of an engine lubricant and establishing
an engine
lubricant discard interval for a particular engine;
[00035] FIG. 5 shows an example of an implementation of the system of FIG. 2A;
[00036] FIG. 6 shows an example of General Electric (GE) OEM recommendations
for a
GE locomotive engine;
[00037] FIG. 7 shows an example of Electro-Motive Diesel (EMD) OEM
recommendations for an EMD locomotive engine;
[00038] FIG. 8 shows an example of historical data that may be retrieved from
a data
storage for a particular engine;
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[00039] FIG. 9 shows an example of eight scatter plot charts of iron (Fe)
versus oil-age for
a locomotive unit;
[00040] FIG. 10 shows an example of eight scatter plot charts of soot versus
oil-age for a
locomotive unit;
[00041] FIG. 11 shows an example of eight scatter plot charts of TBN versus
oil-age for a
locomotive unit;
[00042] FIG. 12 shows an example of a scatter plot chart of soot versus oil-
age for a
locomotive unit;
[00043] FIG. 13 shows an example of eight scatter plot charts of iron (Fe)
versus oil-age
for a locomotive unit;
1000441 FIG. 14 shows an example of eight scatter plot charts of soot versus
oil-age for a
locomotive unit;
[00045] FIG. 15 shows an example of a matrix scatter plot chart for another
locomotive
unit; and
[00046] FIG. 16 shows an example of a process for setting a maintenance
schedule for one
or more engines;
[00047] FIG. 17 is a scatter plot for iron versus oil age with predicted
values of iron at 184
days and at 276 days.
[00048] FIG. 18 is a scatter plot for predicted viscosity at 100 C according
to one
embodiment of the disclosure;
[00049] FIG. 19 is a graphical representation of a partial least squares model
fit with three
latent variables according to the embodiment of the disclosure depicted in
FIG. 17;
[00050] FIG. 20 is a coefficient chart for the three latent variables depicted
in FIG. 19; and
[00051] FIG. 21 is a matrix plot of multivariate data for predicting iron
content in oil at t +
30 days and at t + 120 days;
[00052] FIG. 22 is a matrix plot of multivariate data for predicting soot
content in oil at t +
30 days and at t + 120 days;
[00053] FIG. 23 is a scatter plot for predicted viscosity at 100 C according
to a second
embodiment of the disclosure.
[00054] The present disclosure is further described in the detailed
description that follows.
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DETAILED DESCRIPTION OF THE DISCLOSURE
1000551 The disclosure and the various features and advantageous details
thereof are
explained more fully with reference to the non-limiting embodiments and
examples that
are described and/or illustrated in the accompanying drawings and detailed in
the
following description. It is noted that the features illustrated in the
drawings and
attachment are not necessarily drawn to scale, and features of one embodiment
may be
employed with other embodiments as the skilled artisan would recognize, even
if not
explicitly stated herein. Descriptions of well-known components and processing
techniques may be omitted so as to not unnecessarily obscure the embodiments
of the
disclosure. The examples used herein are intended merely to facilitate an
understanding
of ways in which the disclosure may be practiced and to further enable those
of skill in
the art to practice the embodiments of the disclosure. Accordingly, the
examples and
embodiments herein should not be construed as limiting the scope of the
disclosure.
Moreover, it is noted that like reference numerals represent similar parts
throughout the
several views of the drawings.
[000561 "Used lubricant" as used in the disclosure refers to lubricant from an
engine that
has been operating for a period of time. The used lubricant may be from an
initial charge
of lubricant at a service interval or may be lubricant that is supplemented
between service
intervals by fresh lubricant.
1000571 A "computer," as used in this disclosure, means any machine, device,
circuit,
component, or module, or any system of machines, devices, circuits,
components,
modules, or the like, which are capable of manipulating data according to one
or more
instructions, such as, for example, without limitation, a processor, a
microprocessor, a
central processing unit, a general purpose computer, a super computer, a
personal
computer, a laptop computer, a palmtop computer, a notebook computer, a cloud
computer, a desktop computer, a workstation computer, a server, or the like,
or an array
of processors, microprocessors, central processing units, general purpose
computers,
super computers, personal computers, laptop computers, palmtop computers,
notebook
computers, desktop computers, workstation computers, servers, or the like.
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[00058] A "server," as used in this disclosure, means any combination of
software and/or
hardware, including at least one application and/or at least one computer to
perform
services for connected clients as part of a client-server architecture. The at
least one
server application may include, but is not limited to, for example, an
application program
that can accept connections to service requests from clients by sending back
responses to
the clients. The server may be configured to run the at least one application,
often under
heavy workloads, unattended, for extended periods of time with minimal human
direction. The server may include a plurality of computers configured, with
the at least
one application being divided among the computers depending upon the workload.
For
example, under light loading, the at least one application can run on a single
computer.
However, under heavy loading, multiple computers may be required to run the at
least
one application. The server, or any if its computers, may also be used as a
workstation.
[00059] "Linear regression," as used in this disclosure, means any known
linear regression
methodology known by those skilled in the art, including general linear models
(GLM)
such as, for example, polynomial expressions that may be restricted to a class
of
problems that satisfy a set of requirements. These requirements pertain to the
model
error. The model error is the difference between the observed value and the
predicted
value. The investigation of the model error is a key factor for evaluating
model
adequacy. The required assumptions for general linear models include: the
errors have a
mean of zero; the errors are uncorrelated; the errors are normally
distributed; and the
errors have a constant variance. If any of the foregoing assumptions are
violated, then it
is generally required to apply some sort of transformation, add more variables
to
accommodate systemic sources of variance, or apply another type of modeling
method
such as a non-linear type of modeling approach.
[00060] "Linear regression," as used in this disclosure, may include a
"generalized linear
model" (GLZ). A GLZ has two key features that distinguish it from the GLM
method. It
includes a link and a distribution function. The link is a transformation
function such as
an identity, a power, or log. The distribution function pertains to the error
component. In
GLM, the errors are normally distributed. With GLZ, the errors can be
specified as
normal or from one of the exponential family of distributions. Some examples
include
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the Poisson, binomial, gamma, and inverse Gaussian. Due to the link and
distribution
function, this type of modeling approach may be referred to as a "nonlinear"
type of
modeling.
[00061] "Logistic regression" is a unique modeling approach for binary or
dichotomous
type response data. Logistic regression may be applied to problems that have
pass/fail
{0, 1} data. The two unique features for the logistic regression model
include: the
conditional mean of the regression equation must be formulated to be bounded
between 0
and 1; and the binomial distribution describes the distribution of the errors.
The
predicted value for the logistic model can be expressed as the logged odds or
probability
of a pass/fail for a unique set of conditions of the independent (x)
variables.
[00062] In the case of the used lubricant (or oil) analysis, logistic
regression models may
be used to predict the probability that a critical threshold for a used
lubricant parameter
will be exceeded. If the predicted probability is high that a critical
lubricant life
parameter will be exceeded, then the conclusion will be that the lubricant
drain interval
should not be extended.
[00063] Other modeling techniques such as Partial Least Squares. Principal
Components
Regression, General Linear Models, Generalized Linear Models, Neural Networks,
and
the like may be applied to predict/forecast the value for a set of used
lubricant critical
parameter(s). Alternatively, a discriminate analysis can also be applied to
identify the
variables/attributes that separate the used lubricant data into two different
groups. The
first and second groups in the discriminate analysis correspond to the
conditions that can
and cannot lead to the extension of the lubricant drain interval.
[00064] A "neural network" may be an effective nonlinear and assumption free
type of
modeling approach. Two common architectures of Neural Networks include, for
example, Multi-Layer Perceptron (MLP) and Radial Basis Function (RBF). The
output
of the RBF network is a function of the network weights, radial distances, and
sigma
width parameter. The output of the MLP is based on the weighted sum of the
inputs and
an activation function. The sigmoid is the general type of activation function
form of a
neural network is shown in FIG. 1. In FIG. 1, the variables x 1 xd
are predictor
variables, wl wd (or dM) and w11 wM1 are weighting values, and y is the
output.
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[00065] For example, the response parameter (y) data may be linear or
nonlinear related to
the predictor (x) variables. As shown in the TBN plot in FIG. 11 for unit
Locomotive
Unit 2248, the relationship between the predictor (x), oil age, and the
response parameter
(y) TBN corresponds with a non-linear decreasing trend. In this example, it
may be
advantageous to utilize a higher order polynomial expression, neural network
(NN),
natural log transform of oil age days to better characterize the underlying
relationship
between TBN and oil age.
[00066] In FIG. 9, the relationship between the response parameter (y) and oil
age may be
linear. As shown in the Fe (iron) plot for unit Locomotive Unit 2248, the
relationship
between the predictor (x), oil age, and the response parameter (y) Fe (iron)
tends to
exhibit a linear increasing trend. As such, this data may be expressed with a
linear
polynomial function.
[00067] A "database," as used in this disclosure, means any combination of
software
and/or hardware, including at least one application and/or at least one
computer. The
database may include a structured collection of records or data organized
according to a
database model, such as, for example, but not limited to at least one of a
relational model,
a hierarchical model, a network model or the like. The database may include a
database
management system application (DBMS) as is known in the art. The at least one
application may include, but is not limited to, for example, an application
program that
can accept connections to service requests from clients by sending back
responses to the
clients. The database may be configured to run the at least one application,
often under
heavy workloads, unattended, for extended periods of time with minimal human
direction.
[00068] A "communication link," as used in this disclosure, means a wired
and/or wireless
medium that conveys data or information between at least two points. The wired
or
wireless medium may include, for example, a metallic conductor link, a radio
frequency
(RF) communication link, an Infrared (IR) communication link, an optical
communication link, or the like, without limitation. The RF communication link
may
include, for example, WiFi, WiMAX, IEEE 802.11, DECT, OG, 1G, 2G, 3G or 4G
cellular standards, Bluetooth, and the like.
14
CA 02899069 2015-07-30
,
[00069] A "network," as used in this disclosure means, but is not limited to,
for example,
at least one of a local area network (LAN), a wide area network (WAN), a
metropolitan
area network (MAN), a personal area network (PAN), a campus area network, a
corporate area network, a global area network (GAN), a broadband area network
(BAN),
a cellular network, the Internet, or the like, or any combination of the
foregoing, any of
which may be configured to communicate data via a wireless and/or a wired
communication medium. These networks may run a variety of protocols not
limited to
TCP/IP, IRC or HTTP.
[00070] The terms "including," "comprising," and variations thereof, as used
in this
disclosure, mean "including, but not limited to", unless expressly specified
otherwise.
[00071] The terms "a," "an," and "the," as used in this disclosure, means "one
or more",
unless expressly specified otherwise.
[00072] Devices that are in communication with each other need not be in
continuous
communication with each other, unless expressly specified otherwise. In
addition,
devices that are in communication with each other may communicate directly or
indirectly through one or more intermediaries.
[00073] Although process steps, method steps, algorithms, or the like, may be
described in
a sequential order, such processes, methods, and algorithms may be configured
to work in
alternate orders. In other words, any sequence or order of steps that may be
described
does not necessarily indicate a requirement that the steps be performed in
that order. The
steps of the processes, methods, or algorithms described herein may be
performed in any
order practical. Further, some steps may be performed simultaneously.
[00074] When a single device or article is described herein, it will be
readily apparent that
more than one device or article may be used in place of a single device or
article.
Similarly, where more than one device or article is described herein, it will
be readily
apparent that a single device or article may be used in place of the more than
one device
or article. The functionality or the features of a device may be alternatively
embodied by
one or more other devices which are not explicitly described as having such
functionality
or features.
CA 02899069 2015-07-30
[00075] A "computer-readable medium," as used in this disclosure, means any
medium
that participates in providing data (for example, instructions) which may be
read by a
computer. Such a medium may take many forms, including non-volatile media,
volatile
media, and transmission media. Non-volatile media may include, for example,
optical or
magnetic disks and other persistent memory. Volatile media may include dynamic
random access memory (DRAM). Transmission media may include coaxial cables,
copper wire, and fiber optics, including the wires that comprise a system bus
coupled to
the processor. Transmission media may include or convey acoustic waves, light
waves
and electromagnetic emissions, such as those generated during radio frequency
(RF) and
infrared (IR) data communications. Common forms of computer-readable media
include,
for example, a floppy disk, a flexible disk, hard disk, magnetic tape, any
other magnetic
medium, a CD-ROM, DVD, any other optical medium, punch cards, paper tape, any
other physical medium with patterns of holes, a RAM, a PROM, an EPROM, a FLASH-
EEPROM, any other memory chip or cartridge, a carrier wave as described
hereinafter, or
any other medium from which a computer can read. The computer-readable medium
may include a "Cloud," which includes a distribution of files across multiple
(e.g.,
thousands of) memory caches on multiple (e.g., thousands of) computers.
[00076] Various forms of computer readable media may be involved in carrying
sequences of instructions to a computer. For example, sequences of instruction
(i) may
be delivered from a RAM to a processor, (ii) may be carried over a wireless
transmission
medium, and/or (iii) may be formatted according to numerous formats, standards
or
protocols, including, for example, WiFi, WiMAX, IEEE 802.11, DECT, OG, 1G, 2G,
3G
or 4G cellular standards, Bluetooth, or the like.
[00077] FIG. 2A shows an example of a system 100 that determines the usability
of a
lubricant and when to replace the lubricant in, for example, an engine. The
system 100
comprises an analyzer 110, a computer 120, a server 130, and a database 140,
all of
which may be linked through a network 150 via communication links 160 or
directly via
the communication links 160. The analyzer 110 may be located on (or in) an
engine, in
an engine compartment of a vehicle, in a building, or the like. The computer
120 may be
located at, e.g., a customer site, such as, e.g., a customer shop, a customer
building, or the
16
CA 02899069 2015-07-30
like. The server 130 and/or database 140 may be located at a product provider
site, such
as, e.g., an engine lubricant distributor or supplier, an engine lubricant
retailer, or the like.
[00078] The
analyzer 110 may include, e.g., a spectral analyzer, a viscosity analyzer, an
acid analyzer, a solids analyzer, a flashpoint analyzer, an oxidation
analyzer, a nitration
analyzer, and the like. The analyzer 110 is configured to receive a sample of
an engine
lubricant that has been taken from a particular engine and analyze the sample
to identify
and measure one or more analysis parameters. For instance, the spectral
analyzer 110
may perform spectral analysis of the lubricant sample to determine the levels
(e.g., in
parts per million (ppm)) of analysis parameters. The analysis parameters (AP)
may
include, e.g., wear metals, contaminants, additives, and the like, that may be
present in
the lubricant. The analysis parameters may also include an indication and
concentration
of engine coolant in the lubricant. The spectral analyzer may include, e.g., a
Rotrode
Emission Spectrometer, an Inductively Coupled Plasma Spectrometer, or the
like. The
wear metals that may be identified and measured include, e.g., aluminum,
antimony,
chromium, copper, iron, lead, nickel, silver, tin, titanium, zinc, and the
like. The
additives that may be identified and measured include, e.g., antimony, boron,
calcium,
copper, magnesium, molybdenum, phosphorus, potassium, silicon, sodium, zinc,
and the
like. The contaminants that may be identified and measured include, e.g.,
zinc, boron,
potassium, silicon, sodium, soot, water, fuel, sludge, insolubles, and the
like. The
oxidation and nitration analyzers may provide information concerning
degradation of the
lubricant by measuring oxidation and nitration, respectively.
1000791 The viscosity analyzer may include, e.g., a viscometer that performs
viscosity
analysis to determine the effective grade of the lubricant. The viscosity
analyzer may
measure the lubricant at a temperature of, e.g. -35 C, -20 C, 0 C, 40 C, 100
C, or any
other temperature, as is known in the art. The viscosity analyzer may measure
the
effective viscosity of the lubricant by, e.g., measuring the time that it
takes the lubricant
to flow between two sensors that are provide on a conduit (e.g., a glass tube,
or the like)
that is maintained at a constant temperature. Alternatively (or additionally)
the viscosity
analyzer may measure, e.g., high temperature, high shear, dynamic, kinematic,
and the
like.
17
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[00080] An acid analyzer may measure the lubricant's Total Base Number (TBN)
by, e.g.,
mixing the lubricant with a diluent and titrating the mixture with, e.g.,
alcohol-
Hydrochloric acid (HC1) solution until all of the alkaline constituents that
are present in
the lubricant are neutralized. The acid analyzer may additionally (or
alternatively)
measure the lubricant's Total Acid Number (TAN). In this regard, the acid
analyzer may,
e.g., mix the engine lubricant with a diluent and, then, titrate the mixture
with, e.g.,
alcohol-potassium hydroxide (KOH) until all of the acids present in the engine
lubricant
have been neutralized. The TAN or TBN results may be reported in milligrams
of, e.g.,
KOH or HC1 per gram of engine lubricant.
[00081] The solids analyzer may perform an analysis of the solids in the
lubricant to
identify the particular solids and the concentration of the solids in the
lubricant. The
solids analyzer may include, e.g., a laser-based particle counter, infrared
analyzer, or the
like, that detects and measures the concentration of particles in a sample of
lubricant.
[00082] The flashpoint analyzer may analyze the lubricant to determine the
temperature at
which the vapors from the lubricant ignite. For instance, the flashpoint
analyzer may
slowly heat a sample of lubricant, keeping accurate measurements of the
temperature of
the sample. When the evaporated gases ignite or become ignitable, the
temperature of the
sample may be recorded as the flash point temperature of the particular
lubricant sample.
[00083] The analyzer 110 may include a transceiver (not shown) that is
configured to send
and receive data and instructions over the communication link 160. For
instance, the
analyzer 110 may be configured to send data from the engine or the engine
compartment
of the vehicle to the customer computer 120 and/or the server 130 or database
140. The
analyzer 110 may be configured to directly sample an engine lubricant in an
engine and
provide analysis data in substantial real-time, which may be sent to the
customer
computer 120 and/or the server 130 (or database 140).
[00084] Alternatively, the analyzer 110 may be located at a remote laboratory,
where
samples (e.g., 4oz, 8oz, or the like) of engine lubricant may be received at
the laboratory
for testing via messenger, mail, or the like. The results of the analysis may
be sent by the
analyzer 110 to the customer computer 120 and/or the server 130 via the
communication
links 160. For instance, after a sample of the engine lubricant has been
analyzed by the
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CA 02899069 2015-07-30
analyzer 110, the engine lubricant analysis results may be sent to the
database 140, where
the results may be associated with and stored in, e.g., a database record (or
file) that is
associated with a particular engine, a particular engine type, a particular
vehicle, a
particular engine manufacturer, a particular vehicle manufacturer, a
particular entity (e.g.,
a person, a company, an institution, or the like), or the like. The database
record may
include historical information, including past lubricant analysis results for
the associated
engine and/or vehicle. It is noted that the database 140 may be located
internally in the
server 130.
[00085] FIG. 2B shows a representation of a determiner module 170 that may be
included
in the server 130 to carry out an aspect of the disclosure. The determiner 170
may
include software and/or hardware. The determiner 170 may include a central
processing
unit (CPU) and a memory. The determiner 170 is configured to receive and
compare a
measured analysis parameter value AP to an analysis parameter threshold value
APTH.
The determiner 170 determines a lubricant discard (or drain) interval (LDI)
based on the
comparison of the measured analysis parameter value AP to the analysis
parameter
threshold value APTH. The determiner 170 may provide an output that indicates
whether
the LDI interval may be extended, or not, or whether it needs to be shortened.
[00086] According to an embodiment of the disclosure, the determiner 170 is
configured
to receive and compare each of a plurality of measured analysis parameter
values, API,
..., APR, to the analysis parameter threshold value APTH for a particular
analysis
parameter in a particular engine, where the analysis parameter values API,
..., APR
include the measured levels or concentrations of the particular analysis
parameter AP in n
samples of engine lubricant that were taken over n separate dates, where n is
a positive
integer that is greater than, or equal to 1. The determiner 170 may include
artificial
intelligence, such as, e.g., a neural network, fuzzy logic, or the like, that
performs linear
regression, non-linear regression, logistic regression, or the like, on the
plurality of
analysis parameter values APi, ..., APR for each analysis parameter. The
determiner 170
may implement, e.g., "if-then" methodologies to predict future AP values. For
example,
the determiner 170 may determine an LDI for a given engine by determining if
AP(soot)
> 45 at day 150, then the determiner 170 may predict that the soot critical
value will be
19
CA 02899069 2015-07-30
exceeded at day 276; or, if AP(VIS100C) > 16.5 and AP(TAN) > 3.8 at day 150,
then the
critical values for TAN or VIS100C will be exceeded, thereby making it
necessary to set
the LDI at a point sooner than 276 days, such as, e.g., at 184 days. The
determiner 170 is
configured to monitor and predict when an AP value (e.g., level,
concentration, or the
like) of the analysis parameter will likely exceed the associated threshold
value AP-rn by
using, e.g., linear regression, non-linear regression, logistic regression, or
the like.
1000871 The determiner 170 is configured to repeat the process for m different
analysis
parameters, where m is equal to or greater than 1, and where m corresponds to
the number
of different analysis parameters that are identified and measured in n samples
of engine
lubricant that are taken from and analyzed for a particular engine. That is,
the determiner
170 performs, e.g., a linear regression, Neural Network Analysis, or Partial
Least Squares
Analysis, and the like for each of the values AP(1)1, AP(1),,
AP(m)i, AP(m)n,
while comparing each of the values AP(1)1, AP(1)n, AP(m)i,
AP(m)n to
respective threshold values AP(1)TH
AP(m)Tn. As noted earlier, the analysis
parameter value AP may include, for example, a level, an amount, a
concentration, or the
like, of a wear metal, an additive, a contaminant, or the like, in a sample of
engine
lubricant. The determiner 170 predicts an occurrence (e.g., a time, a day, a
date, or the
like) when a future analysis parameter value APn+i is expected to exceed (or
fall under)
the associated threshold value APTH for the associated analysis parameter. The
determiner 170 may then set the LDI based on the predicted occurrence. For
instance, the
determiner 170 may set an LDI on a date that is well before, or just prior to
when the
future value APn+/ is expected to exceed (or fall under) the associated
threshold value
APni.
1000881 The determiner 170 may be configured to perform different prediction
methodologies for different analysis parameters. For instance, the determiner
170 may
implement linear extrapolation to predict future values for iron or soot, but
implement
logarithmic prediction (non-linear prediction) to predict future values for
lead.
[00089] FIG. 3 shows an example of a lubricant analysis process 200 for
analyzing a
sample of an engine lubricant. Referring to FIGS. 2A-2B and 3, the process 200
begins
when a sample of engine lubricant is received at the analyzer 110 from a
source (Step
CA 02899069 2015-07-30
210). The source may include, e.g., an engine, an individual, a company (e.g.,
railroad
company, trucking company, shipping company, rental car company, or the like),
an
institution (e.g., a school, a hospital, or the like), an agency (e.g., a
government agency,
or the like), or the like. In the instance where the analyzer 110 (shown in
FIG. 2A) is
located on (or in) the engine, or in the engine compartment near the engine,
the source
may be the engine itself, and the analyzer 110 may be placed, e.g., in the
lubricant flow
path, between the engine and an external lubricant filter (e.g., an engine oil
filter) or an
external lubricant cooler (e.g., an engine oil cooler).
1000901 After the sample of the engine lubricant is received (Step 210) from a
particular
engine, the lubricant sample may be analyzed by the analyzer 110 to identify
and
measure the types and concentrations of the wear metals, the additives, the
contaminants,
and the like, that are present in the lubricant. The analyzer 110 may further
measure
TBN, TAN, viscosity, flashpoint, and the like, of the lubricant.
1000911 The results of the analysis may be compiled and reproduced in an
analysis report
for the analyzed sample of engine lubricant (Step 230). The analysis report
may then be
sent to the customer computer 120 and/or the server 130 (Step 240). The report
may be
sent to the database 140, where the report may be associated with and stored
in a record
for the particular engine. Alternatively, the analysis report may be displayed
directly on,
e.g., an on-board-display (not shown) of a vehicle (Step 240). The lubricant
analysis
report may be include, e.g., raw data, tabulated data, or the like, for the
identified and
measured analysis parameters, including, e.g., wear metals, additives,
contaminants,
TBN, TAN, viscosity, flashpoint, and the like. The lubricant analysis report
may be
generated and produced in human readable form (e.g., a printout, a display, an
audio file,
a video file, a multimedia file, or the like), so as to be readable by a
human, or the report
may be provided in a machine-readable format, so that the report may be
received and
processed by the customer computer 120, the server 130, and/or the database
140 without
any human intervention.
[00092] According to an aspect of the disclosure, a computer readable medium
is provided
that contains a computer program, which when executed in, for example, the
analyzer
110, which may include a computer (not shown), causes the process 200 in FIG.
3 to be
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CA 02899069 2015-07-30
carried out. The computer program may be tangibly embodied in the computer
readable
medium, which may comprise a code segment or a code section for each of the
steps 210
through 240.
[00093] FIG. 4 shows an example of an engine lubricant discard interval
determination
process 300 for determining the usability of an engine lubricant and
establishing an
engine lubricant discard interval for a particular engine.
1000941 According to an embodiment of the disclosure, the process 300 may be
carried out
by the customer computer 120 or the server 130. The results of the process 300
may be
stored in the database 140. Alternatively, according to another embodiment of
the
disclosure, the process 300 may be carried out in its entirety by the analyzer
110.
[00095] Referring to FIG. 4, initially, engine data and a lubricant
analysis report are
received by, e.g., the server 130 (or customer computer 120) for a particular
engine or a
particular vehicle (Step 310). The engine data may include, e.g., the year in
which the
engine was manufactured, the engine type, the engine manufacturer, the engine
displacement, the place of manufacture of the engine, the engine serial
number, the
vehicle serial number in which the engine is installed, and the like. The
lubricant
analysis report may be received from, e.g., the analyzer 110 (Step 240 in FIG.
3) and the
report may include analysis parameter values AP(1)n, AP(m)n.
[00096] The server 130 may query its internal data storage 135 (shown in FIG.
5) or the
database 140 to determine if a record exists for the particular engine
identified by the
received engine data (Step 320). If it is determined that a record does exist
for the
particular engine (YES at Step 320), then the identified record is retrieved
from storage
135 (or 140) (Step 340). The retrieved record may include a plurality of
historical values
for each of the measured analysis parameters, e.g., values AP(1)i,..., AP(1)n-
/,...,
AP(m)i,..., AP(m)n-t.
[00097] If it is determined that record does not exist for the particular
engine (NO at Step
320), then a record is created in the local data storage 135 (FIG. 5) and/or
the database
140 (FIG. 2A) (Step 330). The created record may include a plurality of fields
for the
particular engine, including, e.g., a customer name (e.g., a railroad company,
a trucking
company, a shipping company, or the like), a customer address (e.g., an email
address, a
22
CA 02899069 2015-07-30
geographic address, a telephone number, a point of contact name, or the like),
the year in
which the engine was manufactured, the engine type, the engine manufacturer,
the engine
displacement, the place of manufacture of the engine, the engine serial
number, the last
service date for the engine, the details of the last service, the date that
the engine was put
into operation, the number of hours on the engine, the number of miles on the
engine, the
vehicle serial number in which the engine is installed, and the like. The
fields of the
record may be populated with the data received in the engine data (Step 310).
The
created record may further include OEM recommendations (e.g., recommendations
600,
700, shown in FIGS. 6, 7, respectively), industry recommendations, trade group
recommendations, standards body recommendations, individual recommendations,
or the
like, which may include threshold values for one or more analysis parameters,
APTH(m).
[00098] The received lubricant sample data may be processed by the server 130
(e.g., the
determiner 170, shown in FIG. 2B) and the analysis parameter values for the
particular
engine, AP(1)n, . . AP(m)n, along with the historical values, AP(1)1,...,
AP(m)1,..., AP(m)n-i, may be compared against the associated analysis
parameter
threshold values APTH(1),..., APTH(m) (Step 350). Further, a lubricant discard
interval
LDI may be determined by performing an analysis on the values AP(1)1,...,
AP(1)n,...,
AP(m)n, to predict when a value of the future analysis parameter values
AP(l)+i,..., AP(m)n+] will exceed (or fall under) an associated threshold
value
APTH(1)...APTH(m) (Step 360). The LDI may include, e.g., a time, a day, a
number of
days, a date, a number of engine hours, or the like. The record for the
particular engine
may be updated to include the LDI information and the received analysis
parameter
values AP(1)n,
AP(m)n, as well as the predicted values AP(1)n+/,..., AP(m)n+ (Step
370). The generated LDI data may be sent to the customer computer 120 (or the
server
130) and/or the database 140 (Step 380).
1000991 According to an aspect of the disclosure, a computer readable medium
is provided
that contains a computer program, which when executed in, for example, the
server 130
(or the computer 120), causes the process 300 in FIG. 4 to be carried out. The
computer
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CA 02899069 2015-07-30
program may be tangibly embodied in the computer readable medium, which may
comprise a code segment or a code section for each of the steps 310 through
380.
[000100] FIG. 5 shows an example of an implementation of the system 100 (shown
in
FIGS. 2A-2B). In this example, the locomotive unit 2248 may be in the shop for
its
scheduled 184 day service. The service technician, using the computer 120, may
request
an LDI for the unit 2248 to determine whether it is necessary to replace the
engine
lubricant at the 184 day point, or if the unit 2248 may continue to run for
another 92 days
without replacing the engine lubricant. In this regard, the server 130 may
query its
internal data storage 135 (or database 140, where it is provided internal to
the server 130)
for historical data for the unit 2248. If the historical data is stored in the
remote database
140, then the database 140 may be periodically queried to obtain the most up
to date
information associated with the unit 2248. The determiner 170 may then process
the
retrieved historical data for the unit 2248 to generate predicted analysis
parameter values
AP(1), AP(2), AP(3), AP(4), and AP(5) for the set analysis parameters at 276
days,
including (1) soot, (2) lead (Pb), (3) viscosity 100C, (4) TAN, and (5) TBN.
It is noted
that other (additional or alternative) analysis parameters may be set, as one
of ordinary
skill in the art will recognize, without departing from the scope or spirit of
the disclosure.
As seen in FIG. 5, the predicted analysis parameter value AP(3)n+/ for
viscosity 100C
may be at an unacceptable level at 276 days, but the predicted value for
AP(3)n+/ TAN is
at an acceptable level, thereby making it necessary to replace the lubricant
before the 276
days, preferably at, e.g., 184 days while the unit 2248 is in the shop.
[000101] FIG. 6 shows an example of General Electric (GE) OEM recommendations
600
for a GE locomotive engine that may be retrieved from the database 140. As
seen, the
recommendations 600 include a list of analysis parameters AP, ranging from
copper (Cu)
to TBN. In this instance, m = 24. Each of the analysis parameters AP has an
associated
"Critical" threshold value APTH-c, an associated "Abnormal" threshold value
APTH-A, and
an associated "Marginal" threshold value APTH-m. The recommendations 600 also
include a "Problems" column that provides a suggested cause if a particular
analysis
parameter exceeds anyone of the three identified threshold values.
24
CA 02899069 2015-07-30
[000102] FIG. 7 shows an example of Electro-Motive Diesel (EMD) OEM
recommendations 700 for an EMD locomotive engine that may be retrieved from
the
database 140. As seen, the recommendations 700 include a list of analysis
parameters AP
similar to those in FIG. 6, ranging from silver (Ag) to TBN. In this instance,
m = 25. As
discussed earlier with regard to the recommendations 600, each of the analysis
parameters in the recommendations 700 has an associated "Critical" threshold
value
APTH-c, an associated "Abnormal" threshold value APTH-A, and an associated
"Marginal"
threshold value APri-i-m. Like the recommendations 600, the recommendations
700 also
include a "Problem" column that suggests causes when a particular analysis
parameter is
beyond anyone of the three identified threshold values.
[000103] In the recommendations 600 (or 700), should a particular analysis
parameter go
beyond (exceed or be less than) the recommended "Marginal" threshold value,
but have a
value less extreme than the "Abnormal" threshold value, then the
recommendations
recommend that the unit (or engine) be "shopped" during the next inspection
and the
indicated problem (in the "Problem" column) be investigated. If the particular
analysis
parameter is beyond (exceed or be less than) the recommended "Abnormal"
threshold
value, but does not go beyond (exceed or be less than) the "Critical"
threshold value, then
the recommendations recommend that the particular unit (or engine) be sent to
the shop
immediately for service, and that the associated problem in the "Problem"
column be
investigated. If the particular analysis parameter goes beyond (exceed or be
less than) the
recommended "Critical" threshold value, then the recommendations recommend
that the
particular unit (or engine) be shut down immediately and the unit be serviced,
beginning
with an investigation of the associated problem identified in the "Problem"
column.
[000104] FIG. 8 shows an example of historical data 400 that may be retrieved
from the
database 140 for a particular engine (e.g., locomotive unit 2248), where n =
25 and m = 1.
In this example, the historical data may include four columns of data,
including: a
TAKEN column that includes the dates on which a lubricant sample was taken
from the
unit 2248; a TESTED column that includes the respective dates on which the
taken
lubricant samples were tested; a UNIT column that identifies the engine (e.g.,
unit 2248);
and an analysis parameter column that identifies a particular analysis
parameter (Fe), the
CA 02899069 2015-07-30
. .
wear metal iron, and includes n analysis parameter values, from the earliest
recorded
value, AP(1)] = 2(ppm), to the last recorded value, AP(1)25 = 4(ppm). As seen,
the
values AP(1)/ ... AP(1)25 range from a low of 2(ppm) to a high of 18(ppm).
[000105] FIG. 9 shows an example of eight scatter plot charts that may be
generated by the
server 130 for the iron (Fe) versus oil-age for the locomotive unit 2248.
Specifically, the
scatter plot charts include seven charts (1 to 7) that show iron
concentrations in the
engine oil measured at various times for seven past lubricant discard
intervals (LDI), and
one chart (8) that includes AP(Fe) values for iron for the current LDI
interval. As seen in
the charts, the iron levels Fe versus oil-age tend to be linear. Thus, when
oil changes
have been identified, then the oil age can be calculated.
[000106] FIG. 10 shows an example of eight scatter plot charts of soot versus
oil-age for
the locomotive unit 2248. Specifically, the scatter plot charts include seven
charts (1 to 7)
that show soot concentrations in the engine oil measured at various times for
seven past
lubricant discard intervals (LDI), and one chart (8) that includes soot values
for the
current LDI interval. As seen in the charts, soot levels appear to also be an
indicator of
oil-age. The data indicates a linear relationship between oil age and soot.
[000107] FIG. 11 shows an example of eight scatter plot charts of TBN versus
oil-age for
the locomotive unit 2248. Specifically, the scatter plot charts include six
charts (2 to 7)
that show TBN levels in the engine oil measured at various times for six past
lubricant
discard intervals (LDI), one chart (1) for which no historical data is
available, and one
chart (8) that includes TBN levels for the current period. As seen in the
charts, the
relationship between oil age and TBN levels may be linear and/or non-linear.
[000108] FIG. 12 shows an example of a scatter plot chart of soot versus oil-
age for a
locomotive unit, with the data for seven (1 to 7) oil change intervals
superimposed along
with the soot level data during the current oil change interval (8). As seen
in the chart, a
data point 1110 appears to be an outlier or unusual result data. According to
principles of
the disclosure, the system 100 (shown in FIGS. 2A-2B) is configured to detect
and filter
out outlier data, such as, e.g., the data point 1110.
[000109] FIG. 13 shows an example of eight scatter plot charts of iron (Fe)
versus oil-age
for the locomotive unit 2248. FIG. 13 is similar to FIG. 9, except that FIG.
13 further
26
CA 02899069 2015-07-30
includes a predictor line 1210 that predicts the Fe levels in the engine oil
during the
period from about 140 days to about 276 days, where the predictor line 1210
may be
generated by the determiner 170.
10001101 FIG. 14 shows an example of eight scatter plot charts of soot versus
oil-age for
the locomotive unit 2248. FIG. 14 is similar to FIG. 10, except that FIG. 14
further
includes a predictor line 1310 that predicts the soot levels in the engine oil
during the
period from about 140 days to about 276 days, where the predictor line 1310
may be
generated by the determiner 170.
10001111 FIG. 15 shows an example of a matrix scatter plot chart for another
locomotive
unit 8866. As seen in the chart, ten analysis parameters, including Fe, Pb,
Cu, V100C,
OXI, NIT, SOOT, TAN, TBN, PI, are measured and plotted for six separate oil
changes,
n = 6.
10001121 FIG. 16 shows an example of a process 500 for setting a maintenance
schedule for
one or more engines. Referring to FIG. 2A, the database 140 may be queried to
retrieve
the LDI data for all (or less than all) of the engines that belong to a
particular customer
(Step 510). The engines identified in the retrieved data may then be
categorized based on
the LDI data into one or more LDI categories ¨ e.g., engines that require
maintenance
every 92 days, engines that require maintenance every 184 days, engines that
require
maintenance every 276 days, and the like (Step 520). A maintenance schedule
may be
generated (or updated) for each of the identified engines (Step 530). The
maintenance
schedule may include a listing of engines that are selected for extended
lubricant discard
intervals (e.g., LDI = 276 days). The maintenance schedule may include a
listing of
engines that are selected for shortened lubricant discard intervals (e.g., LDI
= 92 days).
The maintenance schedule may include a calendar that identifies the scheduled
LDI date
for each of the identified engines. The generated maintenance schedule may
then be sent
to, e.g., the customer computer 120 (Step 540).
[000113] According to an aspect of the disclosure, a computer readable medium
is provided
that contains a computer program, which when executed in, for example, the
server 130
(or the computer 120), causes the process 500 in FIG. 16 to be carried out.
The computer
27
CA 02899069 2015-07-30
program may be tangibly embodied in the computer readable medium, which may
comprise a code segment or a code section for each of the steps 510 through
540.
[000114] The following examples provide illustration of the uses of the system
and
methods described herein to determine a drain interval for an engine. The
examples are
for illustrative purposes only and are not intended to limit the scope of the
claimed
invention.
EXAMPLE 1
[0001] In
the following example a partial least squares analysis of data was used to
determine when an analysis parameter value would be expected to exceed a
threshold
value. In the examples, t + X days are arbitrarily selected to illustrate how
the analysis is
conducted using the claimed system and methods. In the following tables, the
values are
of each parameter are determined by t + 120 days by interpolating between
existing data
points for each of the measured parameters in a historical record for an
engine. For
example, Table 1 shows the interpolated values for iron for a particular
engine a t + 120
days, where t is the estimated oil age in days. Because there is some lag time
between
when a sample is obtained and the analysis of the sample is determined, the
samples are
not collected on the last day of a service interval. In other words, the
analysis must be
received before a selected service interval so that a decision can be made by
the time the
service interval date arrives. An example of the decision lag time of t = 30
days is
illustrated in Figure 17.
28
CA 02899069 2015-07-30
,
Table 1
Sample Date Ln(UnitAgeDays+1) OilAgeDays (t) Fe
Interpolated Fe Interpolated Fe
No. ppmw @ t + 30 Days
@ t+ 120 Days
PPmw
PPmw
1 07-22-04 4.65 0 5 I _Ip. 7.86
v 16.57
2 09-23-04 5.12 63 11 r- 14.59
20.76
3 10-12-04 5.23 82 13 15.99
21.88
4 10-15-04 5.25 85 13 J61i
22.06
10-15-04 5.25 85 14 16.21 22.06
6 12-22-04 5.55 153 19 20.76
27.89
7 03-17-05 5.84 238 24 27.33
8 04-22-05 5.94 274 28
9 08-06-05 6.18 0 7 9.00
17.52
08-28-05 6.23 22 9 8.54 19.08
11 09-11-05 6.26 36 9 8.14
22.00
12 10-16-05 6.32 71 8 15.09
19.41
13 10-28-05 , 6.34 83 14 17.83
20.95
14 11-12-05 6.37 98 14 17.17
22.87
11-23-05 6.39 109 18 18.46
,
16 12-16-05 6.42 132 17 19.64
17 01-09-06 6.46 156 22 18.77
18 01-11-06 6.47 158 20 19.03
19 02-02-06 6.50 180 18 21.85
03-13-06 6.56 219 23
21 02-15-06 6.52 0 6 7.55
16.83
22 03-01-06 6.54 14 6 9.46
17.00
23 03-14-06 6.56 27 7 10.55
18.47
24 03-25-06 6.57 38 9 11.55
19.26
04-07-06 6.59 51 10 12.00 19.74
26 04-18-06 6.61 62 11 12.00
20.30
27 04-29-06 6.62 73 12 14.13
21.11
28 05-10-06 6.64 84 12 16.33
21.93
29 05-21-06 6.65 95 12 17.00
24.18
06-05-06 6.67 110 16 17.53
31 06-17-06 6.68 122 17 19.04
32 07-01-06 6.70 136 17 19.56
33 07-16-06 6.72 151 19 20.22
34 08-12-06 6.75 178 20 22.65
09-08-06 6.78 205 22
36 09-19-06 6.80 216 24
29
CA 02899069 2015-07-30
. .
37 08-28-06 6.77 0 7 10.00
13.70
38 09-10-06 6.79 13 11 10.20
15.67
39 09-23-06 6.80 26 10 10.72
16.00
40 10-05-06 6.81 38 10 11.20
16.00
41 12-19-06 6.89 113 13 16.00
42 12-29-06 6.90 123 14 16.00
43 01-10-07 6.91 135 16 16.00
44 02-23-07 6.96 179 16
45 09-30-07 7.15 0 5 6.88
15.50
46 11-17-07 7.18 48 8 10.33
14.93
47 11-29-07 7.19 60 7 11.53
18.00
48 12-11-07 7.20 72 9 12.92
17.00
49 12-20-07 7.21 81 11 15.00
16.00
50 01-06-08 7.22 98 12 15.94
16.00
51 01-19-08 7.23 111 15 13.43
18.20
52 02-06-08 7.24 129 16 14.29
53 02-20-08 7.25 143 13 16.09
54 03-03-08 7.26 155 14 17.58
55 03-17-08 7.27 169 15 16.22
56 03-28-08 7.28 180 18 16.00
57 04-09-08 7.29 192 17 16.40
58 04-18-08 7.29 201 16 18.20
59 05-07-08 7.31 220 16
60 05-22-08 7.32 235 19
61 05-01-08 7.30 0 5 7.00
9.81
62 05-13-08 7.31 12 5 7.00
10.70
63 05-28-08 7.32 27 7 7.42
11.58
64 06-12-08 7.33 42 7 7.83
12.58
65 07-18-08 7.35 78 8 8.97
66 08-18-08 7.37 109 9 11.16
67 09-14-08 7.39 136 11 12.92
68 10-03-08 7.40 155 12 13.00
69 10-15-08 7.41 167 13
70 11-05-08 7.42 188 13
[0002] As shown in the foregoing table, the expected iron content
at t = 30 days can be
obtained from a data set for Sample Nos. 1 and 2 by linear interpolation
between the iron
content at t = 0 days and the iron content at t = 63 days. Likewise, the
expected iron
content at 120 days can be determined by linear interpolation between Sample
Nos. 5 and
CA 02899069 2015-07-30
6 using the observed iron content for the same data set at t = 85 and t = 153
days. Based
on the foregoing analysis, FIG. 17 is a scatter plot for iron versus oil age
with predicted
values of iron at 184 days (point A) and at 276 days (point B).
[0003] Since the iron content loss rate in the used oil may change over
time due to a
number of factors such as engine age and interaction with other variables, a
partial least
squares analysis, regression analysis, or Neural Network analysis, and the
like of the
interpolated values for each set of data is used to predict the iron content.
The same
analysis may be conducted for any and/or all selected parameter such as iron,
chromium,
lead, tin, aluminum, nickel, silver, silicon, boron, sodium, magnesium,
calcium barium,
phosphorus, zinc, molybdenum, potassium, viscosity at 100 C, oxides,
nitrates, sulfides,
fuel, water, glycol, soot, total acid number (TAN), total base number (TBN),
and the like.
The input parameters may also include other non-used oil analysis parameters
such as oil
pressure, unit/vehicle age, fuel consumption, megawatt hours produced, total
miles, total
hours, and the like.
EXAMPLE 2
[0004] The foregoing analysis can also be done for non-linear data such as
viscosity.
Table 2 is a table of data for viscosity at 100 C at t + 30 days and t + 120
days.
31
CA 02899069 2015-07-30
. ..
Table 2
Sample Date Ln(UnitAgeDays+1) OilAgeDays V @ Interpolated V
Interpolated V @
No. (t) 100 C @100 C @ t + 30
100 C @ t + 120
Days Days
1 07-22-04 4.65 0 15.3 15.62
16.54
2 09-23-04 5.12 63 15.97 16.17
16.89
3 10-12-04 5.23 82 16.21 16.43
16.83
4 10-15-04 5.25 85 16.23 16.47
16.82
10-15-04 5.25 85 16.06 16.47 16.82
6 12-22-04 5.55 153 17 16.89
16.99
7 03-17-05 5.84 238 16.7 16.95
8 04-22-05 5.94 274 17
9 08-06-05 6.18 0 15.2415.53
16.63
08-28-05 6.23 22 15.35 ......-----jr 15.63 16.94
11 09-11-05 6.26 36 15.66 15.61
16.93
12 10-16-05 6.32 71 15.6 16.26
17.32
13 10-28-05 6.34 83 16.08 ..45
17.65
14 11-12-05 6.37 98 16.23 16.84
18.07
11-23-05 6.39 , 109 16.34 16.94
16 12-16-05 6.42 132 16.95 17.35
17 01-09-06 6.46 156 16.93 17.18
18 01-11-06 6.47 158 17.42 17.23
19 02-02-06 6.50 180 17.01 17.85
03-13-06 6.56 219 18.1
21 02-15-06 6.52 0 15.28 15.43
16.91
22 03-01-06 6.54 14 15.32 , 15.71
17.16
23 03-14-06 6.56 27 15.37 15.90
17.43
24 03-25-06 6.57 38 15.59 16.04
17.54
04-07-06 6.59 51 15.86 16.21 17.58
26 04-18-06 6.61 62 15.93 16.37
17.66
27 04-29-06 6.62 73 16.14 16.61
17.82
28 05-10-06 6.64 84 16.23 16.83
17.99
29 05-21-06 6.65 95 16.42 16.99
18.18
06-05-06 6.67 110 16.78 17.29
31 06-17-06 6.68 122 16.93 17.52
32 07-01-06 6.70 136 17.2 17.56
33 07-16-06 6.72 151 17.52 17.64
34 08-12-06 6.75 178 17.6 18.05
09-08-06 6.78 205 18
36 09-19-06 6.80 216 18.2
32
. CA 02899069 2015-07-30
r
37 08-28-06 6.77 0 _ 15.24 15.38 16.17
_
38 09-10-06 6.79 13 , 15.37 15.53 16.29
39 09-23-06 6.80 26 15.32 15.64 16.40
40 10-05-06 6.81 38 _ 15.49 15.74 16.49
_
41 12-19-06 6.89 113 , 16.12 , 16.37
_
42 12-29-06 6.90 123 16.19 16.45 .
_
43 01-10-07 6.91 135 16.31 16.54
, ,
44 02-23-07 6.96 179 _ 16.65
45 09-30-07 7.15 0 15.09 15.25 16.14
_
46 11-17-07 7.18 48 15.35 14.96 17.00
_
47 11-29-07 7.19 60 15.36 15.28 17.49
48 12-11-07 7.20 72 15.13 15.76 17.46
49 12-20-07 7.21 81 14.88 16.06 17.45
_
50 01-06-08 7.22 98 15.63 16.20 17.58
51 01-19-08 7.23 111 16.06 16.33 17.83
52 02-06-08 7.24 129 , 16.21 17.25
53 02-20-08 7.25 143 16.35 17.16
54 03-03-08 7.26 155 , 17.36 17.48
55 03-17-08 7.27 169 16.97 17.45
56 03-28-08 7.28 180 17.49 17.52
57 04-09-08 7.29 192 17.46 17.64
58 04-18-08 7.29 201 17.45 17.83
59 05-07-08 7.31 220 17.6
60 05-22-08 7.32 235 17.91
61 05-01-08 7.30 0 15.22 15.39 15.96
. _
62 05-13-08 7.31 12 15.24 15.52 16.04
63 05-28-08 7.32 27 15.36 15.60 16.31
64 06-12-08 7.33 42 15.52 , 15.67 16.58
65 07-18-08 7.35 78 15.7 15.88 16.70
66 08-18-08 , 7.37 109 15.89 16.14 17.00
67 09-14-08 7.39 136 16.07 16.63 17.15
68 10-03-08 7.40 _ 155 16.49 16.61
69 10-15-08 7.41 167 16.64 16.69
70 11-05-08 7.42 188 16.6 16.90
71 12-02-08 7.44 215 16.87
72 12-17-08 7.45 230 17.01
73 01-11-09 7.46 255 17.14
33
CA 02899069 2015-07-30
[0005] FIG. 18 is a scatter plot of observed versus predicted value of
viscosity at 100 C
at t + 120 days using a partial least squares model with a 3 latent variable
model. FIG.
19 is a graphical representation of the amount of variation explained for a 3
latent
variable model using the partial least squares model for predicting the
viscosity at 100 C
at t + 120 days for the data. FIG. 20 is a partial least squares model
coefficient chart
summary of the three latent variable model depicted in FIG. 19 showing the
relative
importance/effects of various parameter used for predicting the viscosity at
100 C at t +
120 days. FIGS. 21 and 22 are scatter plots of the multivariate data that was
analyzed to
predict the interpolated iron content and soot content at t + 30 and t + 120
days in the oil.
The matrix plots also illustrate the multicollinear relationships of the
interpolated t + 30
and t + 120 iron and soot content output parameters with respect to the
multiple analysis
input parameters.
[0006] FIG. 23 is a scatter plot of viscosity data at 100 C (KV100) for a
multilayer
perceptron neural network prediction model showing observed versus predicted
interpolated viscosity data at 100 C at t + 120 days. Multiple analysis input
parameters
were selected to develop the 'Interpolated 100 C Viscosity at t + 120 days'
Neural
Network prediction model.
[0007] According to a further aspect of the disclosure, a marker may be
added to the
lubricant. The marker may produce a measurable change once the lubricant
becomes
spent. The marker may be measurable by, e.g., visible spectrum analysis,
infrared
analysis, color change, or the like.
[0008] While the disclosure has been described in terms of exemplary
embodiments,
those skilled in the art will recognize that the disclosure can be practiced
with
modifications in the spirit and scope of the appended claims. These examples
are merely
illustrative and are not meant to be an exhaustive list of all possible
designs,
embodiments, applications or modifications of the disclosure.
34
