Canadian Patents Database / Patent 2344908 Summary

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(12) Patent: (11) CA 2344908
(54) English Title: MODEL BASED FAULT DETECTION AND DIAGNOSIS METHODOLOGY FOR HVAC SUBSYSTEMS
(54) French Title: METHODOLOGIE DE DETECTION D'ANOMALIE ET DE DIAGNOSTIC PAR MODELES POUR LES SOUS-SYSTEMES CVAC
(51) International Patent Classification (IPC):
  • F24F 11/30 (2018.01)
  • F24F 11/38 (2018.01)
  • F25B 49/02 (2006.01)
(72) Inventors :
  • MCINTOSH, IAN B. D. (United States of America)
(73) Owners :
  • SIEMENS INDUSTRY, INC. (United States of America)
(71) Applicants :
  • SIEMENS BUILDING TECHNOLOGIES, INC. (United States of America)
(74) Agent: SMART & BIGGAR LLP
(74) Associate agent:
(45) Issued: 2010-06-15
(22) Filed Date: 2001-04-23
(41) Open to Public Inspection: 2002-01-20
Examination requested: 2006-03-22
(30) Availability of licence: N/A
(30) Language of filing: English

(30) Application Priority Data:
Application No. Country/Territory Date
09/619,877 United States of America 2000-07-20

English Abstract

A fault detection system for an HVAC system includes sensors for measuring performance of a condenser, compressor, evaporator and chiller. A thermodynamic preprocessor calculates characteristic quantities (CQ's) from measured inputs. A base-case lookup table stores a first set of CQ values generated by the preprocessor during a period of fault-free operation of the HVAC system, a first set of CQ values being generated for each of the measured input values. A set of base-case CQ values is interpolated from the first sets of CQ values stored in the lookup table for a given set of measured inputs. A fault is detected when a difference between actual CQ values and base-case CQ values exceeds a predetermined threshold value for at least one of the CQ values. A detected fault is classified based on which ones of the actual CQ values exceed the interpolated CQ values.


French Abstract

Un système de détection des anomalies d'un système de CVCA comprend des capteurs pour mesurer le rendement d'un condenseur, d'un compresseur, d'un évaporateur et d'un refroidisseur. Un préprocesseur thermodynamiques calcule les grandeurs caractéristiques (GC) à partir des intrants mesurés. Une table de correspondance des scénarios de référence contient un premier ensemble de valeurs GC générées par le préprocesseur pendant une période de fonctionnement sans défaillance du système de CVCA, un premier ensemble de valeurs GC étant généré pour chacune des valeurs d'entrée mesurée. Un ensemble de valeurs GC du scénario de base est interpolé d'après les premiers ensembles de valeurs GC stockés dans la table de correspondance pour un ensemble donné d'intrants mesurés. Une anomalie est détectée quand une différence entre les valeurs GC réelles et les valeurs GC du scénario de référence dépasse un seuil prédéterminé pour au moins une des valeurs GC. L'anomalie détectée est classée selon laquelle des valeurs GC dépasse les valeurs GC interpolées.


Note: Claims are shown in the official language in which they were submitted.


WHAT IS CLAIMED IS:
1. A fault detection system for an HVAC system including sensors for
measuring the performance of one or more of a condenser, a compressor, an
evaporator, and a chiller, said fault detection system comprising:
a processing means for generating characteristic quantities (CQ's) from a
plurality of measured inputs;
means for storing plural CQ values generated by said processing means in an
initial period in which fault-free operation of the HVAC system is assumed, CQ
values being generated for each of plural different measured input values;
means for producing interpolated base-case CQ values from said plural stored
CQ values for a given set of measured inputs;
means for detecting a fault when a difference between actual CQ values,
calculated by said processing means using said given set of measured inputs,
and said
interpolated base-case CQ values varies from a predetermined threshold value;
means for classifying a detected fault based on which ones of said actual CQ
values varies from said interpolated CQ values.
2. The fault detection system according to claim 1 wherein said
processing means uses measured inputs selected from the group comprising:
chilled
water supply temperature; chilled water return temperature; condenser water
supply
temperature; chilled water flow rate; condenser water flow rate; evaporator
saturation
43


temperature; condenser saturation temperature; and compressor discharge
temperature.
3. The fault detection system according to claim 1 wherein said
interpolator is a General Regression Neural Network.
4. The fault detection system according to claim 1 wherein said
interpolator is a Probabilistic Neural Network.
5. The fault detection system according to claim 1 wherein said
interpolator is a Back Propagation Network.
6. The fault detection system according to claim 1 wherein said CQ's are
selected from the group comprising: evaporator heat exchanger approach;
condenser
heat exchanger approach; chilled water temperature difference; condenser water
temperature; condenser conductance-area product; condenser conductance-area
product; isentropic efficiency; motor/transmission efficiency; overall
coefficient of
performance; and compressor coefficient of performance.
7. The fault detection system according to claim 6 wherein condenser
water flow rate abnormalities are diagnosed when one of said condenser
conductance-
44


area product, said condenser heat exchanger approach, and said condenser water
temperature difference exceeds said predetermined threshold value for said CQ.
8. The fault detection system according to claim 6 wherein chilled water
flow rate abnormalities are diagnosed when one of said evaporator conductance-
area
product, said evaporator heat exchanger approach, and said chilled water
temperature
difference exceeds said predetermined threshold value for said CQ.
9. The fault detection system according to claim 6 wherein chilled water
flow rate abnormalities are diagnosed when one of said evaporator conductance-
area
product, said evaporator heat exchanger approach, and said chilled water
temperature
difference exceeds said predetermined threshold value for said CQ.
10. The fault detection system according to claim 6 wherein evaporator
tube fouling is diagnosed when one of said evaporator conductance-area product
and
said evaporator heat exchanger approach exceeds said predetermined threshold
value
for said CQ.
11. The fault detection system according to claim 6 wherein condenser tube
fouling is diagnosed when one of said condenser conductance-area product and
said


condenser heat exchanger approach exceeds said predetermined threshold value
for
said CQ.
12. The fault detection system according to claim 6 wherein an internal
compressor internal fault is diagnosed when one of said compressor coefficient
of
performance, said isentropic efficiency, and compressor power draw exceeds
said
predetermined threshold value for said CQ.
13. The fault detection system according to claim 6 wherein a motor
transmission fault is diagnosed when one of said overall coefficient of
performance,
said motor/transmission efficiency, and motor power draw exceeds said
predetermined threshold value for said CQ.
14. The fault detection system according to claim 6 wherein said
predetermined threshold includes upper and lower critical value levels for
each said
characteristic quantity, said upper and lower critical value levels being
determined in
accordance with a rated precision error of a sensor used to acquire a selected
said
measured input used to calculate said characteristic quantities.
15. A method for detecting faults in a chiller subsystem of a facility cooling
system, comprising:
46


providing plural base data sets of measured inputs, each said set of measured
inputs including sensor data for a different base operating condition of the
chiller;
computing a set of characteristic quantities for each of said plural base data
sets;
storing said plural sets of characteristic quantities in a memory means;
providing a test data set for a test operating condition of the chiller;
computing a set of characteristic quantities from said test data set;
producing an interpolated base set of characteristic quantities from among
said
plural sets of characteristic quantities stored in said memory means using
said test
data set;
detecting a fault when at least one characteristic quantity exceeds a
predetermined threshold range;
diagnosing a fault in relation to which ones of said characteristic quantities
exceed said predetermined threshold range.
16. The method according to claim 15 wherein said predetermined
threshold range includes upper and lower critical value levels for each said
characteristic quantity, said upper and lower critical value levels being
determined in
accordance with a rated precision error of a sensor used to acquire data items
contained in said test data set.
47


17. The method according to claim 15 wherein said measured inputs are
selected from the group comprising: {chilled water supply temperature; chilled
water
return temperature; condenser water supply temperature; chilled water flow
rate;
condenser water flow rate; evaporator saturation temperature; condenser
saturation
temperature; and compressor discharge temperature.
18. The method according to claim 15 wherein said characteristic quantities
are selected from the group comprising: evaporator heat exchanger approach;
condenser heat exchanger approach; chilled water temperature difference;
condenser
water temperature; condenser conductance-area product; condenser conductance-
area
product; isentropic efficiency; motor/transmission efficiency; overall
coefficient of
performance; and compressor coefficient of performance.
19. The method according to claim 18 wherein condenser water flow rate
abnormalities are diagnosed when one of said condenser conductance-area
product,
said condenser heat exchanger approach, and said condenser water temperature
difference exceeds said predetermined threshold value for said CQ.
20. The method according to claim 18 wherein chilled water flow rate
abnormalities are diagnosed when one of said evaporator conductance-area
product,
48


said evaporator heat exchanger approach, and said chilled water temperature
difference exceeds said predetermined threshold value for said CQ.
21. The method according to claim 18 wherein water flow rate
abnormalities in said chilled water flow are detected when one of said
evaporator
conductance-area product, said evaporator heat exchanger approach, and said
chilled
water temperature difference exceeds said predetermined threshold value for
said CQ.
22. The method according to claim 18 wherein evaporator tube fouling is
diagnosed when one of said evaporator conductance-area product and said
evaporator
heat exchanger approach exceeds said predetermined threshold value for said
CQ.
23. The method according to claim 18 wherein condenser tube fouling is
diagnosed when one of said condenser conductance-area product and said
condenser
heat exchanger approach exceeds said predetermined threshold value for said
CQ.
24. The method according to claim 18 wherein an internal compressor
internal fault is diagnosed when one of said compressor coefficient of
performance,
said isentropic efficiency, and compressor power draw exceeds said
predetermined
threshold value for said CQ.
49




25. The method according to claim 18 wherein a motor transmission fault
is diagnosed when one of said overall coefficient of performance, said
motor/transmission efficiency, and motor power draw exceeds said predetermined
threshold value for said CQ.
26. A fault detection and diagnosis system for a HVAC system including
at least two chillers, each chiller being equipped with sensors for measuring
the
performance of one or more condenser, a compressor, an evaporator, and a
chiller,
said fault detection system comprising:
processing means for calculating characteristic quantities (CQ's) from a
plurality of measured inputs for each of the chillers, one chiller being
designated as
a base-case chiller;
means for detecting a fault when a difference between CQ values for the base
case chiller and CQ values for other ones of the chillers exceeds a
predetermined
threshold range for at least one of said CQ values;
a fault classifier for classifying a detected fault based on which ones of
said
actual CQ values exceed said threshold range.
27. The fault detection system according to claim 26 wherein said CQ's are
selected from the group comprising: evaporator heat exchanger approach;
condenser
heat exchanger approach; chilled water temperature difference; condenser water




temperature; condenser conductance-area product; condenser conductance-area
product; isentropic efficiency; motor/transmission efficiency; overall
coefficient of
performance; and compressor coefficient of performance.
28. The fault detection system according to claim 27 wherein condenser
water flow rate abnormalities are diagnosed when one of said condenser
conductance-
area product, said condenser heat exchanger approach, and said condenser water
temperature difference exceeds said predetermined threshold value for said CQ.
29. The fault detection system according to claim 27 wherein chilled water
flow rate abnormalities are diagnosed when one of said evaporator conductance-
area
product, said evaporator heat exchanger approach, and said chilled water
temperature
difference-exceeds said predetermined threshold value for said CQ.
30. The fault detection system according to claim 27 wherein chilled water
flow rate abnormalities are diagnosed when one of said evaporator conductance-
area
product, said evaporator heat exchanger approach, and said chilled water
temperature
difference exceeds said predetermined threshold value for said CQ.
31. The fault detection system according to claim 27 wherein evaporator
tube fouling is diagnosed when one of said evaporator conductance-area product
and
51




said evaporator heat exchanger approach exceeds said predetermined threshold
value
for said CQ.
32. The fault detection system according to claim 27 wherein condenser
tube fouling is diagnosed when one of said condenser conductance-area product
and
said condenser heat exchanger approach exceeds said predetermined threshold
value
for said CQ.
33. The fault detection system according to claim 27 wherein an internal
compressor internal fault is diagnosed when one of said compressor coefficient
of
performance, said isentropic efficiency, and compressor power draw exceeds
said
predetermined threshold value for said CQ.
34. The fault detection system according to claim 27 wherein a motor
transmission fault is diagnosed when one of said overall coefficient of
performance,
said motor/transmission efficiency, and motor power draw exceeds said
predetermined threshold value for said CQ.
35. The method according to claim 27 wherein said predetermined
threshold includes upper and lower critical value levels for each said
characteristic
quantity, said upper and lower critical value levels being determined in
accordance
52




with a rated precision error of a sensor used to acquire a selected said
measured input
used to calculate said characteristic quantities.
36. The fault detection system according to claim 26 further comprising:
means for producing interpolated base-case CQ values from base-case chiller
for a given set of measured inputs;
wherein said fault detecting means detects a fault when a difference between
said interpolated base-case CQ values and CQ values for other ones of the
chillers
exceeds a predetermined threshold range for at least one of said CQ values.
53

Note: Descriptions are shown in the official language in which they were submitted.

CA 02344908 2001-04-23
s
MODEL BASED FAULT DETECTION AND
DIAGNOSIS METHODOLOGY FOR HVAC SUBSYSTEMS
FIELD OF THE INVENTION
I The present invention relates to a method for detecting and diagnosing
faults
2 in HVAC subsystems. More specifically, the method of the present invention
detects and
3 diagnoses faults using a combination of a physical (thermodynamic) model and
a neural
4 network.
BACKGROUND OF THE INVENTION
6 Vapor-compression systems account for a very significant portion of energy
7 consumption in the industrial and commercial sectors. In large office
buildings for example,
8 it is estimated that 10% to 25% of the total electric consumption can be
attributed to cooling
9 systems alone. Furthermore, these percentages can be much greater if a
chiller subsystem
I


a CA 02344908 2001-04-23
1 is operating at low performance levels due to the presence of faults. The
extensive research
2 in Fault Detection and Diagnosis (FDD) in HVAC systems has been motivated by
concerns
3 that range from: the need to reduce power consumption and consequently
energy costs; to
S
4 improve comfort levels in buildings; to reduce wear on various HVAC
equipment; to reduce
the magnitude of greenhouse emissions; and the need to support building
operators in
6 decision-making for building optimization.
7 Conventional Energy Management and Control Systems (FMCS) are limited
8 in that they do not provide the operator with the tools to diagnose faults
in real-time. If
9 effective FDD tools are designed and implemented in EMC systems, then the
detection and
diagnosis of various types of faults can be done automatically.
11 A fault, in the context of HVAC applications, is defined as an
unsatisfactory
12 or unacceptable condition in the operation of a system or subsystem. A
condition is
13 unacceptable if it is a failure or if it causes one directly or indirectly
through a series of other
14 faults. There are various types of faults, some of which are more diff cult
to detect and
diagnose than others. The three basic fault categories, ranked in order of
severity, are
16 degradations, malfunctions and hard failures.
17 Degradation faults occur at a gradual rate but progressively worsen over a
18 period of time. Overall system performance is not drastically changed until
the degradation
19 has matured beyond a critical level. Examples of system performance
degradation faults are
the fouling of condenser or evaporator tubes, or the clogging of filters.
Notably, dirt and
21 grime builds up on the heat transfer surfaces of the heat exchangers over
time, which in turn
2


CA 02344908 2001-04-23
1 modifies the heat transfer coefficient of these devices. These faults
gradually result in
2 pressure drops, reduced flow rates, or higher temperature differences
between the two fluids.
3 Malfunction faults have more immediately noticeable end effects than
4 degradation faults, and tend to justify more immediate service. An example
of this category
of faults is valve leakage, sensor errors, controller breakdowns and damper
stoppages are
6 other examples. These faults usually result in failure to maintain the
desired set points in
7 temperature, pressure or flow rates.
8 Seized compressors andbroken fan belts are examples of "hard" or "complete"
9 failure faults. In such circumstances the impact of the fault is severe, and
justifies the most
immediate service. Building operators must often shut down entire subsystems
in order to
11 carry out the necessary repair.
12 vault Dc(cction and Diagnosis (FDD) is the initial step towards taking
13 corrective or preventative measures in an HVAC system. Detection and
diagnosis sometimes
14 overlap and other times are treated separately. Fault detection involves
the determination
1 S that a fault truly exists based on an observable quantity exceeding some
predetermined
16 threshold or criterion. The choice of this criterion or threshold is a
tradeoff between the
17 sensitivity of the FDD scheme and the likelihood of sounding a false alarm.
Fault diagnosis
18 is the subsequent step, that isolates the cause of the fault. Sometimes
this diagnostic step
19 does not immediately locate the root cause of the fault initially detected
but may involve a
series of steps that eventually converges on the cause at some later point in
the diagnosis.
3


a A CA 02344908 2001-04-23
1 Once the fault has been diagnosed, fault evaluation is performed to assess
2 whether the impact of the fault sufficient to require immediate service. In
general, service
3 should be performed whenever: (1) Comfort cannot be maintained; (2)
Equipment or
4 personal safety is compromised; (3) Environmental damage is done (e.g.
refrigerant leakage);
and (4) Service expense is justified by reduced energy costs.
6 There are several approaches to fault detection. Most, if not all, involve
7 observation of the differences between an "actual" quantity of the system
(or component)
8 during its operation and some "predicted" value of this same quantity. The
quantity under
9 scrutiny is sometimes the "raw" measured variables (e.g., temperatures,
pressures or humidity
ratios that have not been pre-processed) or it may be the result of some
preprocessing that
11 transforms the measured variables collectively {in specific subsets) or
individually. Some
12 examples of the quantities used for comparison in fault detection arc:
Measured cduipment
13 performance vs. Model-based prediction of equipment performance; Measured
equipment
14 performance vs. Common sense expectations of acceptable performance;
Measured
thermodynamic states (e.g., temperatures, pressures, humidity ratios, etc.)
vs. Model-based
16 predicted thermodynamic states; Calculated (estimated) physical parameters
for "current"
17 performance vs. Calculated (estimated) physical parameters for "baseline"
performance.
18 Similarly, there are different approaches to fault diagnosis. The result of
the
19 diagnosis is not binary as in fault detection (i.e., fault or no-fault) but
involves a selection
from a range of different possibilities. One such fault diagnosis approach is
a rule-based
21 diagnosis, which involves a set of rules such as directional change in
temperature deviations
4
,.


CA 02344908 2001-04-23
1 that may be unique to a given fault. Another approach involves comparison of
physical
2 parameters for "current" operation and "normal" operation. For example, the
conductance-
3 area product, UA of a condenser can be calculated from entering and leaving
temperatures,
4 and may be used to diagnose condenser fouling. Yet another approach uses a
pattern
recognition technique applied to "current" residuals and a matrix of
"expected" residual
6 changes associated with each fault.
7 Different kinds of models have been used in FDD methods. However,
8 conventional models are unsatisfactory as they have limited general
applicability. Notably,
9 conventional models require extensive, time consuming tuning in order to
tailor the model
to a specific implementation. As a result, such models are not readily
portable to different
11 HVAC implementations.
12 Accordingly, one object of the present invention is to provide an improved
13 model-based FDD methodology that uses a combination of a physical model and
a black-box
14 model which has general applicability to HVAC systems.
A further object of the present invention in this research is to develop an
16 improved model-based FDD methodology for determining when and where
problems occur
17 in a centrifugal chiller system, and that is suitable for online
implementation using day-to-
18 day operating field data.
19 Another object is to provide an improved model which compares
characteristic
quantities for everyday operating conditions to a predetermined baseline of a
chiller's normal
5


CA 02344908 2001-04-23
1 (fault-free) operation, and uses statistical hypotheses to determine if a
significant deviation
2 from this normal operation has occurred.
3 Yet another object is to provide a FDD method which accounts for sensor
4 errors to provide a more robust FDD methodology.
SUMMARY OF THE INVENTION
6 The above-listed objects are met or exceeded by the present fault detection
and
7 diagnosis system which includes sensors for measuring the performance of a
condenser, a
8 compressor, an evaporator, which are all components of the chiller
subsystem. The fault
9 detection system further includes a thermodynamic preprocessor, a base-case
lookup table,
an interpolator, a fault-detector and a fault classifier.
I1 The thermodynamic preprocessor, which is preferably a software roulinc
12 executed on a computer or the like, calculates characteristic quantities
(CQ's) from a plurality
13 of measured inputs. The base-case lookup table stores plural first sets of
CQ values
14 generated by the thermodynamic preprocessor in an initial period in which
fault-free
1 S operation of the HVAC system is assumed, a first set of CQ values being
generated for each
16 of plural different measured input values.
17 The interpolator generates a set of interpolates base-case CQ values for a
given
18 set of measured inputs from among the first sets of CQ values stored in the
base-case lookup
19 table that is stored in memory of the computer. The fault-detector, which
is preferably a
software routine executed on a computer or the like, detects a fault when a
difference
6


. , CA 02344908 2001-04-23
1 between actual CQ values, calculated by the thermodynamic preprocessor for a
given set of
2 measured inputs, and the interpolated set of base-case CQ values for the
given set of
3 measured inputs exceeds a predetermined threshold value for at least one of
the CQ values.
s
4 The fault classifier, which is preferably a software routine executed on a
computer or the
like, classifies a detected fault based on which ones of the actual CQ values
exceed the
6 interpolated CQ values.
7 BRIEF DESCRIPTION OF THE DRAWINGS
8 FIGURE. 1 is block diagram showing the overall structure of the model-based
9 fault detection and diagnosis system of the present invention;
FIG. 2 is a block diagram showing the inputs and some of the outputs of the
11 thermodynamic preprocessor of the present invention;
12 FIG. 3 is an EES (Engineering Equation Solver) procedure used to determine
13 the logarithmic mean temperature difference between the evaporator
saturation temperature
14 (T evap), the chilled water supply temperature (T chws), and the chilled
water return
temperature (T chwr);
16 FIG. 4 is an EES procedure used to determine the logarithmic mean
17 temperature difference between the condenser saturation temperature (T
cond), the
18 condenser water supply temperature (T~cws) and the condenser water return
temperature
19 (T cwr)
7

~ , CA 02344908 2001-04-23
1 FIG. 5 depicts equation sets for the condenser side input data used by the
EES
2 procedure of FIG. 4;
3 FIG. 6 are EES program statements for inputting data used in the equations
of
4 FIGS. 3 and 8;
FIG. 7 are EES program statements for inputting data used in the equations of
6 FIG. 9 and FIG. 10;
7 FIG. 8 are equations used to determine evaporator related characteristic
8 quantities;
9 FIG. 9 are equations used to determine compressor related characteristic
quantities;
11 FIG. 10 are equations used to determine condenser related characteristic
12 quantities;
13 FIG. 11 is an expansion valve equation used by the EES code in FIG.B;
14 FIG. 12 is a list of thermodynamic properties and conversion constants used
1 S by the EES code in FIGS. 8-10;
16 FIG. 13 is a schematic of a chiller showing the location of each of the
sensors
17 used to obtain the measured inputs;
18 FIG. 14 illustrates the structure of a single neuron unit;
19 FIG. 1 S shows the general architecture of a interconnected neural network
with
several neurons, 'links and layers;
8

CA 02344908 2001-04-23
1 FIGs. 16A-C are graphs illustrating how different smoothing parameters are
2 used in smoothing a trained GRNN
3 FIG. 17 shows the neural network architecture of the GRNN algorithm of the
4
4 present invention;
FIG. 18 shows the sensitivity of UA when used to detect flow rate reductions;
6 FIG. 19 shows the sensitivity of the entering/leaving water temperature
7 difference when used to detect flow rate reductions;
8 FIG. 20 shows the sensitivity of UA when used to fouling of the heat
9 exchanger tubes;
FIG. 21 shows the sensitivity of APPR when used to detect fouling of the heat
11 exchanger tubes;
I 2 FIG. 22 shows the sensitivity of ~~;se"l,.on;~ when used to detect
compressor faults;
13 FIG. 23 shows the sensitivity Of ~n~otor when detecting motor or
transmission
14 faults.
FIG. 24 shows the sensitivity of UA~, andAPPR~ when detecting fouling faults;
16 FIG. 25 shows the sensitivity of compressor isentropic efficiency when
17 detecting compressor faults;
18 FIG. 26 shows the sensitivity of motor efficiency when detecting motor
faults;
19 and
FIG. 27 is a chart summarizing the relationships illustrated in FIGs. 18-26.
9

~
, CA 02344908 2001-04-23
I DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS
2 The inventor of the present invention discovered that it is difficult to
detect and
3 diagnose a fault in a chiller subsystem from raw data collected from
sensors. Notably, it is
4 difficult to set a meaningful fault threshold which is applicable to
different chill subsystem
implementations, i.e., is not customized for a particular implementation. To
overcome this
6 problem, the inventor of the present invention utilize a series of
characteristic quantities
7 which are calculated from the raw data using well known thermodynamic
principles. The
8 use of such characteristic quantities eliminates the need to individually
tune the system of the
9 present invention for any given HVAC installation.
Importantly, the inventor of the present invention discovered that some
11 characteristic quantities (CQs) are more sensitive than others in detecting
certain faults.
12 Table I below, summarizes the outputs (characteristic quantities) and the
inputs used to
13 determine them.
14 The sensitivity of each CQ value to a given fault was determined by
simulating
various faults in a controlled experiment. Each fault condition in addition to
a base case (no-
16 fault) condition was simulated. Table II below pairs particular fault
conditions with potential
17 CQs that may be used for their detection. The inventor of the present
invention developed
18 a system for detecting and diagnosing faults utilizing the relationship
summarized in Table
19 II.


CA 02344908 2001-04-23
1 TABLEI
Raw Ou uts
Data Characteristic
uantities


2 1 T~nWSchilled water supplyAPPRe evaporator heat exchanger
approach


tem . F Y F


3 2 T~n,,,,,.chilled water returnAPPR~ condenser heat exchanger
approach


tem . F F


4 3 T~WS condenser water CHWTD chilled water temp. difference
(F)


su 1 tem . F


4 m~nW chilled water flowCWTD condenser water temp. difference
rate (F)


m


6 5 m~W condenser water UA~ condenser conductance-area
flow product


rate m Btu/hr-F


7 6 Te~a~,evaporator saturationUAe evaporator conductance-area
product


tem . F Btu/hr-F


8 7 T~nd condenser saturation~;Sentropicisentropic heat transfer
coefficient


tem . F Btu/hr-ftz-F


9 8 TZ compressor dischargemotor motor/transmission heat transfer


tem . F coefficient Btu/hr-ftz-F


9 P com ressor ower COPo,,~~a"overall coefficient of erfotmancc


11 _ ._________.._~_._~ _________-.._...._._._.~___CWPta~~,a~.
~~~nprcssor coefficient
10 orpcrformmcc_
_j



12


_
13 Fault Type Sensitive CQs


14 (1) Condenser water flow reductionUA~, APPR~, CWTD


(2) Chilled water flow reductionUA~, APPR~, CHWTD


16 (3) Evaporator tube fouling UAe and APPRe


17 (4) Condenser tube fouling UA~ and APPR


18 (5) Compressor internal faultsP, COP~o", and r~ue"rro
;~


19 (6) Motor/Transmission faultsP, COPo~erarr and
t~moto,


FIG. 1 is a block diagram of the overall structure of the model-based fault
21 detection and diagnosis methodology of the present invention. The
fundamental components
TABLE II
11


CA 02344908 2001-04-23
1 along with their links to other components and flow of information for fault
detection and
2 diagnosis are depicted.
3 There are two sources of data used in this methodology, operating CQ's
4 calculated on-the-fly, and base-case CQ's which are interpolated from data
stored in a look-
up table. As shown in FIG. l, measured inputs (and forcing functions), such as
the chilled
6 water supply temperature of the chiller and cooler water are fed to both the
thermodynamic
7 preprocessor 100 and the interpolator 102. The thermodynamic preprocessor
100 uses the
8 measured inputs to generate the characteristic quantities (CQs). The
operating CQ's are
9 compared with base-case CQ's which are interpolated from empirical data
stored in a base
case lookup table 104.
11 The look-up table 104 stores values of CQ's that were generated during a
12 period in which fault-free operation of the chiller is assumed. The
interpolator 102
13 interpolates normal (base-case) CQ's from the data stored in the look-up
table 104. The
14 process for obtaining the operating and base case CQ values will be
explained in detail
below.
16 The streams of operating and base-case CQ data are compared and errors
17 (residuals) are computed. The residuals are fed to the Fault Classifier
section 108 where the
18 appropriate statistical analyses are employed to determine the significance
of these errors.
19 If they are indeed statistically significant, a fault detection alarm is
produced. Otherwise,
monitoring continues with the assumption that the chiller process is operating
normally and
21 that no faults are present.
12

~
, CA 02344908 2001-04-23
1 ~ If faults are detected, the diagnostic classifier determines the type of
fault and
2 its likely location within the chiller. The CQs used in the methodology of
the present
3 invention were specifically selected to make isolating the fault location
easier. As noted
4 above, the inventor of the present invention discovered that certain CQs are
sensitive to some
faults and not to others. Thus, the careful selection of CQs makes it possible
to distinguish
6 between fault types.
7 Once faults are detected and diagnosed, they are subsequently evaluated to
8 determine the most suitable action to take. Fault Evaluation deals with
comfort, safety,
9 environment and economic issues. Action could be taken whenever: (1) comfort
cannot be
maintained, (2) equipment or personal safety is compromised, (3) environmental
damage
11 occurs (e.g. refrigerant leakage), or (4) reduced energy costs justify the
service expense.
12 In order to conduct on-line FDD using day-to-day operating data, it is
desirable
13 to have a model that can generate CQ's without the need for any manual
intervention. The
14 thermodynamic preprocessor 100 requires only eight measurement variables to
generate the
necessary CQ's immediately. See FIG. 2. Sets of CQ's are generated at two
different time
16 periods. As will be explained below, plural first sets of CQ variables
(base-case CQ's),
17 representative of normal (fault-free) system operating conditions, are
generated and stored
18 in the look-up table 104 during a preliminary period when a sufficiently
wide range of loads
19 is covered. A second set of CQ data (operating CQ's) is generated "on-the-
fly", i.e.,
generated contemporaneously with the receipt of the measured inputs to reflect
the existing
13


' . CA 02344908 2001-04-23
1 state of the system, after the preliminary period has expired, and is
compared with the first
2 set of CQ variables to determine if faults have occurred.
3 Both sets of CQ variables are generated in the same manner, the only
4
4 difference being the time period in which each set is generated. Namely, the
plural sets of
base-case CQ's are generated during a preliminary period in which fault-free
operation of the
6 chiller is carefully controlled. The operating set of CQ's are generated on-
the-fly after the
7 base-case CQ sets have been stored in the look-up table 104.
8 The thermodynamic preprocessor 100 generates the operating CQ's using a
9 thermodynamic data reduction program which was written in EES, an
Engineering Equation
Solver Software developed by S.A. Klein and F.L. Alvarado, F-Chart Software,
Inc. The
11 EES program is particularly suited for solving thermodynamic equations;
however, one of
12 ordinary skill in the art will appreciate that other programming languages
or software
13 packages may also be used.
14 The thermodynamic data reduction program consists of two procedures (FIGs.
3 and 4) and equation sets for the condenser side input data (FIG. 5),
evaporator side input
16 data (FIG. 6), compressor input data (FIG. 7), evaporator equations (FIG.
8), compressor
17 equations (FIG. 9), condenser equations (FIG. 10), expansion valve
equations (FIG. 11 ) and
18 for the thermodynamic properties and conversion constants (FIG. 12). It
should be noted that
19 the thermodynamic data reduction program uses general thermodynamic
principles, and is
not specifically tuned for a specific chiller subsystem. For this reason, the
CQ's have general
21 applicability to all chiller subsystems.
14


. CA 02344908 2001-04-23
4
A
1 A procedure statement heads each procedure (FIGs. 3 and 4), and identifies
the
2 name and arguments of the procedure. Thus, the statement "Procedure LMTD e(T
chws,
3 T chwr, T evap: LMTDe, T we, In arg e)" defines a procedure LMTD a and a
series of
4 arguments (inputs and outputs).
Table I, above, lists the input/outputs of the thermodynamic data reduction
6 program. Moreover, FIG. 13 is a schematic of a chiller showing the location
of each of the
7 sensors used to obtain the measured inputs.
8 The procedures (FIGS. 3 and 4) utilize function calls in which the function
9 max(x,y) returns the larger of the two arguments (x, y). Thus, max(0, -2)
returns "0".
Correspondingly, the function ln(x) returns the natural log of argument x.
11 The procedure LMTD a (FIG. 3) is used to determine the logarithmic mean
12 temperature difference between the evaporator saturation temperature (T
evap), the chilled
13 water supply temperature (T chws), and the chiller water return temperature
(T chwr). The
14 built-in EES error procedure halts the calculations if T evap is greater
than T chess or if
T chess is greater than T chwr.
16 The procedure, LMTD c (FIG. 4) is used to determine the logarithmic mean
17 temperature difference between the condenser saturation temperature (T
cond), the
18 condenser water supply temperature (T cws) and the condenser water return
temperature
19 (T cwr). The built-in EES error procedure halts the calculations if T cond
is less than
T cwr or if T cwr is less than T cws.


CA 02344908 2001-04-23
1 In developing the base case look-up table, measurement data for a wide
variety
2 of base-case operating conditions is coranpiled in a table which is accessed
by the
3 thermodynamic data reduction program. As will be appreciated by one of
ordinary skill in
s
4 the art, it is advantageous to collect measurement data for as wide a
variety of operating
conditions as possible in order to improve the accuracy of the interpolated CQ
values. For
6 example, it is desirable to calculate base-case CQ values for various times
of day for both
7 workdays and non-workdays, different weather conditions (ambient
temperature, sunlight,
8 winds, humidity), etc to fully reflect the wide range of heating/cooling
loads encountered by
9 the system.
In FIGS. 5-7, the statement lookup( x, y) "F" is used to access entry "x" in
the
11 lookup table and assign the returned value to variable "y". The units for
each variable
12 accessed from the lookup table is provided in the comments following each
lookup
13 statement. Thus, in the above example, the units are in degrees Fahrenheit.
14 As noted above, a first set of CQ variables, representative ofnormal (fault-
free)
system operating conditions, is generated during a preliminary period when a
sufficiently
16 wide range of loads is covered. It is assumed that sufficient data are
collected and that there
17 are no major unforeseen systematic changes during data collection.
18 Fault detection is performed by comparing each respective operating CQ
19 variable with a corresponding base-case CQ variable. Each set of CQ
variables stored in the
lookup table 104 'includes at least eight (of the nine) input and ten (of the
output variables
21 listed in Table I.
16


CA 02344908 2001-04-23
1 As will be appreciated by one of ordinary skill in the art, there are many
2 different combinations of input variables, whereas the look-up table only
contains a relatively
3 small subset of the possible combinations. Consequently, it is unlikely that
a given set of
4 eight measured inputs acquired on-the-fly will directly match one of the
sets of input
S variables stored in the look-up table 104. Accordingly, the interpolator 102
determines a set
6 of base-case CQ variables through a process of interpolation.
7 The inventor of the present invention investigated a number of different
8 interpolation schemes. Linear interpolation and regression against power
form relations were
9 found to be inaccurate and not general. According to a preferred embodiment,
Neural
Network architecture was found to be well suited to the interpolation.
However, one of
11 ordinary skill in the art will appreciate that other interpolation methods
may be substituted.
12 Neural network models are inspired by the human thought processes. Over 100
13 billion biological neurons are present in the human brain. The connections
between these
14 neurons are called synapses and when the brain learns their strength is
modified.
Analogously, artificial neural networks contain artificial neurons that are
16 connected via one-way information conduits called links. Weights are
associated with these
17 links that control the magnitude of the input signal entering the
artificial neuron. These link
18 weights simulate the physical and neuro-chemical characteristics of the
biological synapse.
19 Each artificial neuron in the network functions by first summing its scaled
inputs and then
applying a non-linear function to this sum to generate an output. FIG. 14
illustrates the
17


CA 02344908 2001-04-23
1 simple structure of a single neuron unit and FIG. 15 shows the general
architecture of a
2 interconnected network with several neurons, links and layers.
3 A layer is a grouping of neurons and are of three basic types. The input
layer
4 receives data from outside of the network, the output layer holds the
network's final
computational results and any layers in between these two are called hidden
layers.
6 A basic feature of neural networks is that they learn by example and are
never
7 programmed. Therefore, in order to train a network, it must be presented
with a data sample
8 that contains the inputs and their corresponding outputs. By an iterative
process, the network
9 gradually learns the input-output relationship and then, depending on the
application, is used
for prediction, correction or pattern recognition. Once trained, they can
resolve numeric
11 problems that are otherwise solved by conventional regression analysis. In
the network, the
12 inputs are equivalent to the independent variables used in regression, the
dependent variables
13 become the outputs and the observations are the sample patterns used for
training the
14 network.
Neural networks fall into the two basic categories of supervised networks and
16 unsupervised networks. Supervised networks are those that make predictions,
classify
17 patterns or make decisions based on a set of learned inputs and outputs.
Some examples of
18 these are the Probabilistic Neural Networks (PNN), the General Regression
Neural Networks
19 (GRNN) and Back Propagation Networks. These kinds of networks are trained
to make
predictions, classifications, or decisions when given a large enough number of
accurate
21 classifications or predictions from which it can learn.
18

CA 02344908 2001-04-23
1 Unsupervised networks, without being taught how to categorize, arc able to
2 classify a given number of input and output patterns into a specified number
of categories.
3 They do this by clustering the training patterns using their proximity in h
dimensional space
4 where n is the number of inputs. The network usually clusters the data into
the maximum
S number of categories it is presented with. Kohonen Networks are examples of
this network
6 type.
7 The GRNN is one type of neural network that is well suited to interpolation.
8 It is based on the estimation of a probability density function of a vector
random variable,
9 X, and a scalar random variable, Y. If the joint probability density
function of these variables
are both known then the conditional probability density function and the
expected value can
11 be computed. The estimated value of Y for a given X is presented in the
following general
12 regression equation:
f Yf(X~ ~dy
13 E~Y\X~= ~ (1)
,~.f(X~ ~dY
14 where -°°
~~'\X~ = conditional mean of Y on X
16 ,f (~; ~ = known joint continuous probability density function
19

CA 02344908 2001-04-23
1 The probability density function, f(X, Y), is estimated from sample
observations
2 of X and Y when it is unknown. For a non-parametric estimate of f(X, Y), the
Parzen
3 estimation f'(X, ~, is used by the GRNN. It is defined by the following
equation for the
4 observed sample observations, X~ and Y~ of the vector X and scalar Y
n
X~ - nl, ~ l ~fXfY 2
(2~) 2 Qn+~ n
G where
7 fx = exp - ~X X, )T (X X, )
2a~
8 and
~Y_Y,.~2
9 fr = exp - 2~ (4)
and
11 h = the number of sample observations
12 p = the dimension of the X vector

CA 02344908 2001-04-23
1 ~ = the standard deviation (or smoothing parameter)
2 An estimate for the desired mean of Yat any givenXis derived in Equation (5)
3 by combining Equations (1) and (2) and performing the integration after
first interchanging
4 the integration and summation operation.
Y~ ex - z
;_, 2cs
n D~ z
ex
-1 20'2
6 where, the scalar function D2 is given by:
D; =(X-X ) (X-X J (~)
8 The main algorithm of the GRNN model is expressed by Equations (S) and (C).
9 The estimate ~'(~ is a weighted average of all the observed samples, Y,.,
where each sample
is weighted in an exponential manner according to the Euclidean distance, D;,
from each X,..
11 This appropriate weighting is explained by the inversely proportional
relationship between
Dz Dz
12 the expression exp - 2~ andD;. That is, as D; increases, exp - 2~ decreases
and vice-
13 versa. An optimum value for the smoothing parameter, cr, is determined
using iterative or
21


CA 02344908 2001-04-23
1 genetic adaptive methods. Iterative methods such as the Holdout method and
the Wiggle
2 method are well known by those of ordinary skill in the art, and do not form
part of the
3 present invention. Larger values of o- improve smoothness of the regression
surface.
4 Notably, o- must be greater than 0 and can usually range from .O1 to 1 with
good results.
FIGs. 16A-C illustrates how different smoothing parameters are used in
6 smoothing a trained GRNN. In this example, one input value is used to
predict one output
7 value. As shown in the graph, of the forty input and output patterns only
input patterns 10,
8 20, and 30 produce an output value of 1.
9 FIG. 17 represents the neural network architecture of the GRNN algorithm of
Equations (5) and (6). The Euclidean distance, D;, is computed by the links
between the
11 input layer and the first hidden layer. Based on observed samples, X, and
smoothing
12 parameter, o; the expression,
z
D;
13 cxp 2o~
14 is computed. A node in the second hidden layer takes the sum of the
exponential values of
all samples. In other nodes of this same layer, the products of the
exponential values and the
16 corresponding observed Y,. for each sample observation are computed. The
node in the third
17 hidden layer computes the sum of all these product values, which is then
supplied to the
18 output node where the ratio between it and the previous sum is calculated.
22


CA 02344908 2001-04-23
1 The suitability of the GRNN, however, is attributed to several important
2 features and makes it convenient for online implementation. In the GRNN
network only a
3 single parameter is estimated. Unlike other networks, a once through, non-
iterative training
4 process with a highly parallel structure is involved. Compared to
conventional regression
analysis, the specification of the underlying regression function, bounds of
the independent
6 variables, initial convergence values and convergence criteria are not
required beforehand.
7 Additionally, the algorithm provides smooth transitions from one observed
value to another
8 even with sparse and noisy data in a multidimensional measurement space and
can be used
9 for any regression problem where an assumption of linearity is not
justified.
Since the thermodynamic and the GRNN models involve functions and
11 variables in a multi-dimensional space, it is important to know what the
truly independent
12 measurement variables are. Here, the term independent refers to those
variables that are
13 forcing functions of the chiller process. Information about variable
independence is
14 necessary for understanding the available data, achieving greater insight
to data
I S manipulation, accurately assigning inputs and outputs, and successfully
training the GRNN
IG model to be able to predict normal system behavior. For the chiller
process, only five
17 measurement variables may be considered independent. This number is
determined from
18 studying the various equations that define the chiller process more closely
in the discussion
19 that follows.
An overall energy balance of the entire refrigeration system is:
23

' , CA 02344908 2001-04-23
1 Paw~x = Q~ _ Q~
~r s m~CyT~ -T~) (g)
2
3 ~~t =~h2. _123)=m,~N2,~,~t (9)
4 The enthalpy difference, dla,.ef~ond, between the entering and leaving
refrigerant
in the condenser is a function of the condenser saturation temperature, T~o"d:
(10)
7 ~c~ry~ = mdnv~,~v~Tdn,r -TcJnu ) ( 1 1 )
= m~' ~ha W ) _ '~,~'>~ ( 12)
9 Here, the enthalpy difference, drJ~ef,e"ap, between the entering and leaving
I O refrigerant in the evaporator is a function of the evaporator saturation
temperature, Tevap:
11 ~~,~ -.f ~T~ ) ( 13 )
12 In addition to the above relations, the following relations for the chiller
Power
13 and Capacity, ~ are available from chiller manufacturers' experiments:
24

F ~ CA 02344908 2001-04-23
1 I'~"=.f~T~,~2'~,,~) (14)
2
(ls)
3 Equations (7) to (ls) fully define the thermodynamic data reduction model.
4 An inventory of the number of equations and the number of unknowns yield
nine and
fourteen, respectively. Thus the difference between these two numbers tells us
that five
G variables must be independently measured for these model equations to work.
7 The inventor of the present invention selected the following five
independent
8 measurement points condenser supply temperature, T~ws, the chilled water
supply
9 temperature, T~,,ws, the chilled water return temperature, T~hwr, the
condenser water flow rate,
m~t,, and the chilled water flow rate, m~,,,. However, the selection of other
independent
11 measurement points is contemplated and falls within the scope of the
present invention.
12 The condenser water supply temperature, T~ws and the chilled water return
13 temperature, T~hwr are the entering water temperatures to the chiller
subsystem. The chiller
14 subsystem has no direct effect on the magnitudes of these two water streams
until after they
cross the system boundary. Therefore, the temperatures, T~h",r and T~,~,S are
independent. They
16 represent only a part of the building load and heat rejection,
respectively. The flow rates,
17 m~,,, and m~", are additional required independent variables. The final
measurement
2s


CA 02344908 2001-04-23
1 variable, the chilled water supply temperature, T~,,x,S, is termed
independent because its
2 magnitude is associated with a control set-point.
3 The essence of the Fault Detection scheme relies on the determination that a
4 fault truly exists based on the deviation of a Characteristic Quantity (CQ)
exceeding some
S predetermined threshold or criterion. This deviation is measured from an
established base
6 case representing normal operating conditions.
7 As described above, the CQ values are computed from measurement data
8 gathered from sensors. Due to the many perturbations that exist, there is an
inevitable chain
9 of causes and effects that occur and form the basis for uncertainties in the
measurement data.
Some of the factors that may cause perturbations are power supply variance,
hysteresis,
11 precision and bias errors and general instability. These factors contribute
to the accuracy
12 ratings of the measurement sensors and therefore, are factors that
contribute to the
13 uncertainties in CQ deviations. These uncertainties in CQs determine the
threshold and thus
14 the sensitivity for fault detection. A tighter uncertainty region
corresponds to more sensitive
detection.
16 Numerous situations involve the computation of an important quantity, Y,
17 instead of its direct measurement. If such a quantity is determined from N
other directly
18 measured quantities Xl, XZ, . .., XN, a functional relation between these
quantities and Ymay
19 be expressed as ~'= f (X , X2,..., XN). This function f not only expresses
a physical law but
26

m ~ CA 02344908 2001-04-23
1 a measurement process. In fact, all the quantities that could contribute a
significant
2 uncertainty to the measurement results are accounted for by f.
3 Furthermore, if Yrepresents the estimate of the output quantity, Y, and x,,
x1,
4 . . ., xN are input estimates for the values of the N input measurements,
Xl, X2, . . ., XN, then ~'
can be expressed as Y=f(x,,x2,...,xN). In order to obtain the combined
standard
G uncertainty, u~(Y) of the computation result Y (an estimated standard
deviation of the
7 result), a first-order Taylor series approximation of Y= f (X , X2, ~.., XN)
is performed. This
8 gives the following expression known as the Law of Propagation of
Uncertainty:
vz
N ~ 2 N-1 N
2
- a (xi ) + 2~ ~ u(xixi ) ( 16)
i=1 ~i i=l j=i+1 ~i ~i
where
11 ~ - ~ = sensitivity coefficients
C~'i - O~i Xr=xr
27

CA 02344908 2001-04-23
1 u(xt ) = standard uncertainty associated with the input estimate, x;
2 u(xl,x~ ) = estimated covariance associated with x; and x~
3 If the individual measurement inputs are assumed uncorrelated and
4 random, then the covariance term is zero and u~(~ is simplified to:
Z 1/2
N
~a~ u(xt ) ( 17)
i=1 ~i
6 By manner of example, two CQs that are sensitive to tube fouling in the
7 condenser are its approach, APPR~, and conductance-area product, UA~. Using
the
8 simplified form of the uncertainty propagation law in Equation ( 17),
expressions can
9 be derived for the uncertainties in UA~ and APPR~.
Equation ( 18) that defines the conductance-area product of the
11 condenser, UA~, in terms of four independently measured variables and a
fluid
12 property, Cpw. This property is the specific heat of water and actually
varies with
13 temperature conditions but was assumed constant for this application since
the range
14 of variation is insignificant.
UA~ = w C~", ~ T~ T~'"5 ( 18)
T~, - T~r
28

CA 02344908 2001-04-23
1 The UA~ is a ,f ~Tws~~wr~~ond>>ncwO Equation (16) was used to express the
2 most probable uncertainty in UA~ as:
vz
dlA z c'~UA z d!A z dlA z
3 u~ (UA~ ) _ ~, ' ' u(T~ ) + ~" ' ' u(T~"~. ) + ~, ' ' u(T~ ) + m ° '
u(m~,, )
4 (19)
where,
~A~ - -riiCp", (20)
d~,g~ _- - rizCn",
(21 )
dlA~ -_ mC'p", (T~ - T~",. )
(22)
(T o,~r - T~ )(T ~r - T~"~ )
~A~ = C . ~ (T~~r - T~ 23
( )
and, 7~~(T~"s ), u(T~,. ), ~(T~,~ ) and t~(m~,,,) are the uncertainties of the
measured
11 condenser water supply temperature, condenser water return temperature,
refrigerant
12 condenser saturation temperature and the water mass flow rate,
respectively. These
13 uncertainties are equivalent to the accuracy of the measurement sensors.
29

' . CA 02344908 2001-04-23
1 The approach, APPR~ is a , f (T w,~ T ona~ ~ The most probable uncertainty
2 in APPR~ may be expressed as:
s
1/2
~'APPR a''APPR
3 u(A.PPR~ ) _ ~, ' ~ u(T'~,. ) + ~, ~ ' u(T'~"~ ) (24)
4 where
~PR~ --1 (25)
6 and
a4PPR
7 o~h ~ =-+-1 (26)
8 For the threshold analysis, only precision errors were used. The
9 following sensor precision errors, developed using time series analysis,
were used to
evaluate the effect of error on CQ:
11 Temperature sensor: t0.3°F and X0.6°F
12 Water flow rate sensor: t5%
13 Power kW-meter: ~ 10% and X20%

' . CA 02344908 2001-04-23
1 Fault detection thresholds are illustrated in FIGs. 18 - 24. Two types
2 of horizontal lines are used in defining these thresholds. The no fault line
represents
3 no flow rate reduction (or increase), no fouling and no inefficiencies in
either the
f
4 compressor or the motor. The critical line was determined by the following 3-
step
procedure:
6 1. Locate the points where the uncertainty boundaries intersect the no-fault
7 line.
8 2. Project these points vertically to the points that intersect the
0.0°F curve
9 (shown as black dots; for X0.3 °F, refer to FIG. 18).
3 Draw horizontal lines, parallel to the abscissa, through these intersection
11 points.
12 These horizontal lines are the critical lines and they define a region
13 (shown as hatched in FIG. 18) within which it is possible for faults to go
undetected.
14 Beyond these critical lines (or thresholds), however, faults may be
detected with more
1 S confidence despite the errors present in the measurement sensors.
16 For example, water flow rate abnormalities (reduction or increase) are
17 detected most effectively by deviations in the conductance-area product and
the
18 entering/leaving water temperature difference ofthe given heat exchanger
(evaporator
19 or condenser). FIG. 18 shows the sensitivity of UA when used to detect flow
rate
reductions. The heavy solid curve represents the UA deviation in the face of
no errors
21 in the temperature and flow sensors (i.e., 0.0°F/0% of full scale,
gpm). The dashed
31


CA 02344908 2001-04-23
1 and light solid curves represent the UA deviation due to errors of
X0.3°F/~S% gpm
2 and +0.6°F/~5% gpm, respectively.
3 Here, the critical lines are drawn using the uncertainty boundaries
4 defined by~0.6°F/~5% gpm. Based on a X0.6°F/~5% gpm precision
ofthese sensors,
it can be concluded that UA deviations between -23.6% and +23.6% would not
6 indicate a fault. Flow rate reductions greater than ~ 1.0% and flow rate
increases
7 greater than 68.0% would be needed to indicate faults.
8 FIG. 1 ~ is similar to FIG. 18 in terms of the precision errors represented
9 by the heavy, dashed and light curves. However, the characteristic quantity
used here
is the entering/leaving water temperature difference instead of the
conductance-area
11 product. It is apparent from the critical lines drawn in FIG. 19 that the
thresholds for
12 detecting flow rate seduction is IUWCr than those depicted in hlG. 18. In
others words,
13 this characteristic quantity has a greater fault detection sensitivity.
14 Quantitatively, here, it is concluded that water temperature difference
deviations beyond -14.3% and +14.3% are used to detect flow rate increases
greater
16 than 16.0% and flow rate reductions greater than 13.0%, respectively.
17 Fouling describes a decrease in the effectiveness of the heat exchanger
18 tubes, and is detected most effectively by deviations in the conductance-
area product
19 and the approach of the given device. FIG. 20 shows the sensitivity of UA
when used
to detect fouling. A fouling factor of 1 represents no fault and in this
study, is the
21 normalized equivalent of 0.00075 hr-ft2/BTU. A factor greater than 1 is an
increase
32


CA 02344908 2001-04-23
1 in fouling and lesser than 1 is a decrease. The critical lines indicate.
that UA
2 deviations beyond -19.6% and +19.6% are used to detect fouling factors
greater than
3 1.6 and lesser than 0.3, respectively.
4 FIG. 21 shows the sensitivity of the Approach when used to detect
fouling. The critical lines indicates thatAPPR deviations beyond-17.6% and +I
7.6%
6 are used to detect fouling factors lesser than 0.7 and greater than 1.3,
respectively.
7 These numbers, compared to those for UA, suggest that the APPR has lower
fouling
8 detection thresholds.
9 Some internal faults of the compressor are detected most effectively by
deviations in its isentropic efficiency, h;se",,.op;~. FIG. 22 shows the
sensitivity of
11 ~isentropic when used to detect faults. A compressor fault factor of one
represents no
12 fault. Ln contrast to the fouling factor, a compressor fault factor grcatcr
than one is
13 an improvement of the fault condition and a factor lesser than one is a
worsening of
14 the situation. The critical lines indicate that ~~;,senlrnpic dwiations
beyond -2.1 % and
+2.1 % are used to detect fault factors lesser than 0.8 and greater than 1.2
respectively.
16 Deviations in the motor efficiency, 77n~otor~ are used to detect faults in
the
17 motor or transmission housing. FIG. 23 shows the sensitivity of r~"~otor
when detecting
18 motor or transmission faults. The heavy solid curve represents the r~mo,or
deviation
19 when the kW-meter is error free (i.e., 0% of full scale, kW). The dashed
and light
solid curves represent the r~",otor deviation due to errors of ~ 10% kW and
~20% kW,
33

t CA 02344908 2001-04-23
1 respectively. A motor fault factor of one represents no fault and in this
study, is
2 equivalent to 0.85. However, factors greater than one represent worsening
conditions.
3 Here, the critical lines are drawn using the uncertainty boundaries
4 defined by the t20% kW error. Based on this kW-meter precision, it is
concluded
that ~,notor deviations less than -20% is used to detect fault factors greater
than 1.3.
6 Equations (27)-(29) below are similar in form to the previous three
7 relations for the condenser, and are used to model the chilled water
reduction. Here,
8 the chilled water flow rate reduction causes an increase in the return
temperature and
9 a decrease in the evaporator temperature for a given load and chilled water
set point
temperature.
factor,~~
11 'n~~,,, fit ='n~,",,~1- 100 ~ (27)
12 Q~~~ = j~clnvfindt C~~",(Thw~ ~lnws ~ 28
NEPASS ~ m
13 h~ =137 ~ ~TLJBE ~f ~t ~ De-'.a (29)
14 where, md,K,~~t , factor",e, NEPASS, NETUBE and De are the chilled water
flow due to
the flow reduction fault, the percentage reduction in chilled water flow rate,
the
16 number of evaporator tube passes, the number of evaporator tubes and the
diameter
17 of the evaporator tubes, respectively.
34

' , CA 02344908 2001-04-23
1 The CQs UAe, APPRe and CHWTD show the most significant deviations
2 from the no-fault base-case. The CQ relationships here are similar to those
used for
3 the condenser water reduction fault. CHWTD is the most sensitive and its
sensitivity
4 is unaffected by load changes.
Equations (30) and (31) are used to model condenser tube fouling.
6 There are some conflicting interpretations of the fouling factor in the
literature. Here,
7 fouling is defined as the resistance to heat transfer due to the tube
material as well as
8 to any thin film build-up on its surfaces. Equation (30) is used to simulate
an increase
9 (or decrease) of the level of fouling in the condenser tubes. It is apparent
from
Equation (31 ) that as fouling increases, UA~ decreases.
11 ~,I~~ - R '.f~orf~ (
UA~ = A~'
12 1 I (31)
- + + Rc, faul!
h~>r r~h~,o
13 where, factor f~ is the fault modification factor used to simulate degrees
of fouling in
14 condenser (unity --- normal) and R~ fQUrr is the resistance to heat
transfer associated with
1 S the condenser tube material, including the fouling factor for faulty
conditions.
16 The UA~, andAPPR~ are CQs that show the more significant deviations
17 from the rio-fault base-case. FIG. 24 shows how these CQs deviate from the
base-
18 case condition as the fault condition changes (i.e., as fouling increases
or decreases).

CA 02344908 2001-04-23
1 Similar to the flow reduction faults, the different CQs behave differently.
For
2 example, at a base-case load of 1500 tons, a 100% fouling increase by the
condenser
3 tubes corresponds to a 27% decrease and a 54% increase in UA~ and APPR~,
4 respectively. However, in contrast to condenser flow reduction faults, CWTD
shows
no significant change. This behavior is important in distinguishing the
fouling fault
6 from the flow reduction fault.
7 At 500 tons, the same fouling condition corresponds to a 29% decrease
8 and 63% increase in UA~ and APPR~, respectively. It is apparent from these
results
9 that the more sensitive CQ is the APPR~ and that varying loading conditions
affect it
more than they did UA~.
11 Similar trends exist for a SO% fouling decrease condition. CQs that
12 increased before decreased and vise-verse. Fouling decrease information is
as useful
13 as fouling increase information. Here, FDD may be used to check the
integrity of
14 scheduled maintenance, repair or cleaning of fouled tubes.
Equations (32) and (33) are used to model evaporator tube fouling.
16 1~,~~t =Re 'fa~oY'7.e (32)
UAe = 1 1 ei
- + + Re, fart
17 he>J rehe~~ (33)
36

' . CA 02344908 2001-04-23
1 where, factorfe, is the fault modification factor used to simulate degrees
of fouling in
2 the condenser (unity --- normal) and Re and Re four, are the resistance to
heat transfer
3 associated with the evaporator tube material, including the fouling factor
for normal
Y
4 and for faulty conditions, respectively.
The UAe andAPPRe are CQs that show the more significant deviations
6 from the no-fault base-case. The CQ relationships here are similar to those
used for
7 the condenser tube fouling fault. APPRe is more sensitive and its
sensitivity is slightly
8 affected by load changes.
9 The Weisner correlation, based on the measured performance of
centrifugal compressors, is used for modeling compressor internal faults. The
11 modification of the reference polytropic efficiency, r~,ejer~ in Equation
(34) is used to
12 simulate possible faulty conditions within the compressor.
13 ~~efe~jaul~ - ~rejer ~ faCtO~mp (34)
14 where, factor~omp and ~refe,faul~, are the compressor fault modification
factor, and the
IS modified r~refer due to faulty conditions, respectively.
1 G The compressor isentropic efficiency, 7~isentropic is the CQ that shows
the
17 most significant deviations from the no-fault base-case. FIG. 25
illustrates a greater
18 decrease in the isentropic efficiency as the compressor fault factor
decreases. Based
19 _ on the manner in which this fault is simulated, a decrease in the fault
factor means a
37

' , CA 02344908 2001-04-23
1 worsening compressor internal fault condition. Varying the loading
conditions does
2 not appear to have a great impact on the deviation of the isentropic
efficiency.
3 Whenever there is a fault in the motor or transmission system of the
4 compressor, the overall power draw {P) is expected to increase compared to
the actual
power used for the compression process. The modification factor in Equation
(35)
6 simulates this increase. Equation (35) defines the combined efficiency of
the motor
7 and transmission. An energy balance on the compressor establishes the
relation
8 between the states of the refrigerant at the suction and discharge
conditions.
9 Pf~ =P ~ fador"~". (35)
__ m,~ ~h~o"~
1 ~ ~7nnlor ' (36)
pf~r,r!
11 where, Pjaurt,.factormolar and ~~~mp are the compressor power consumption
due to
12 faulty conditions, the motor fault modification factor and the change in
refrigerant
13 enthalpy between compressor's suction and discharge states, respectively.
1~ The motor efficiency is the CQ that shows the most significant
deviation from the no-fault base-case. FIG. 26 shows that there is a greater
decrease
16 in the motor efficiency as the motor fault factor increases (i.e., as the
power draw
17 increases for a given loading condition). From this simulation, the motor
efficiency
18 deviation (not the motor efficiency) is independent of loading conditions.
That is, at
38

~
, CA 02344908 2001-04-23
1 any loading condition, the percentage deviations due to an increased fault
condition
2 will show the same magnitudes.
3 Refrigerant loss results from leakage in chiller subsystems, which has
4 the net effect of reducing overall system performance. It is often confirmed
by
bubbles present in the refrigerant (as seen from the liquid line sight glass),
frost
b formation, abnormal superheat and subcooling temperatures, and abnormal unit
7 operating pressures. A temperature difference between the low pressure
liquid line
8 temperature (entering the evaporator) and the suction line temperature
(entering the
9 compressor) was computed and used as a characteristic quantity to flag the
occurrence
of refrigerant leaks.
11 FIG. 27 summarizes the diagnostic patterns exhibited by the CQs
12 discussed earlier. The symbols "ra", "+" and "-" signify no significant
change, an
13 increase and a decrease compared to base-case values, respectively. In the
Type
14 column, "N", "F" and "N/A" stands for Normal, Fault and Not Applicable,
respectively. Two normal operating conditions called "Load change" and "Heat-
16 rejection change" are included. Respectively, these are quantities
representing a
17 change in the chiller load and a change in the amount of heat rejected by
the
18 condenser. They are modeled by varying the chilled water return and
condenser water
19 supply temperatures, respectively. These conditions are included to show
that it is
possible to distinguish them from fault conditions using the appropriate set
of CQs.
39

' CA 02344908 2001-04-23
Although, these CQs effectively isolate faults on the component level,
2 the diagnostic patterns help to further distinguish between the various
fault conditions
3 that may exist in a particular component. For example, the increase and
decrease in
4 the condenser approach, APPR~ and the condenser conductance-area product,
UA~,
respectively, would signify that there is a problem in the condenser and not
in the
6 other components of the chiller sub-system. However, this same CQ change
pattern
7 is both exhibited by an increase in condenser tube fouling and a decrease in
the
8 condenser water flow rate. 1'o distinguish between these two conditions,
FIG. 27
9 shows that the condenser water flow rate decrease is accompanied by an
increase in
the condenser water temperature difference, CWTD, whereas the condenser tube
11 fouling is not.
12 For most performance monitoring studies, the COPo,,e,~rr is a CQ that is
13 often used to determine significant performance changes. The COPo~erarr is
included
14 in FIG. 27 to illustrate that although it gives some indication of changes
within a
chiller sub-system, by itself, it is quite vague in isolating faulty
components or in
16 diagnosing faults within a given component. Therefore, it must be used in
17 combination with the other CQs, mentioned here, for useful diagnoses to be
made.
1 g According to one modification of the above-described FDD
19 methodology, the use of a base-case lookup table is eliminated. Notably,
the use of
parallel fault detection and diagnosis in which CQ residuals are derived from
21 l comparing a particular chiller to other identical (similar model,
capacity and run-time)

q a CA 02344908 2001-04-23
1 chillers within the same HVAC system eliminates the need for a lookup
.table. In
2 parallel FDD, the data from a designated chiller is considered to be the
base-case, and
3 faults are detected by comparing the data from another chiller with the data
from the
4 designated chiiler. In so doing, faults in a particular chiller or chillers
are determined
over a predetermined time period.
The parallel FDD methodology is substantially similar to the above
7 described (serial) FDD methodology in all relevant respects. As will be
appreciated
8 by one of ordinary skill in the art, in parallel FDD the CQ's from the
designated
9 chiller and the other chiller(s) are determined on-the-fly, and the
difference (residuals)
between the two streams of CQ's is used to detect faults in the previously
described
11 manner.
12 One of ordinary skill in the art will appreciate that both of the
above-


13 described embodiments of the present invention utilize CQ's and
thus do not require


14 modification or tuning of the thermodynamic model. Accordingly,
the FDD system


of the present invention may readily be included in an existing
facility management


16 system, provided that the appropriate sensors are already installed.
Use of the first


17 embodiment merely requires the collection and storing of base-case
CQ's whereas


18 base-case CQ's are calculated on the fly according to parallel
FDD.


19 Another benefit of the present invention relates to threshold used
to


detect the~occurrence of faults. Both of the above-described embodiments
detect of


21 ~ faults by comparing operating CQ's to base-case CQ's. A fault is detected
when one
41

CA 02344908 2001-04-23
1 or more CQ's exceed a predetermined threshold which is determined in
relation to the
2 sensor accuracy. Accordingly, the false detection of faults is minimized.
Yet another benefit of the claimed invention relates to the classification
t
4 of faults. Notably, the inventor of the present invention discovered that
certain CQ's
S are more sensitive than others to the occurrence of faults, and that faults
may
6 accurately be diagnosed by examination of which CQ's exceed the
predetermined
7 threshold.
While various embodiments of the present invention have been shown
and described, it should be understood that other modifications, substitutions
and
alternatives are apparent to one of ordinary skill in the art. Such
modifications,
11 substitutions and alternatives can be made without departing from the
spirit and scope
12 of'thc invention, which should be determined from the appended claims.
13 Various features of the invention are set forth in the appended claims.
42

A single figure which represents the drawing illustrating the invention.

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Admin Status

Title Date
Forecasted Issue Date 2010-06-15
(22) Filed 2001-04-23
(41) Open to Public Inspection 2002-01-20
Examination Requested 2006-03-22
(45) Issued 2010-06-15

Abandonment History

There is no abandonment history.

Payment History

Fee Type Anniversary Year Due Date Amount Paid Paid Date
Registration of a document - section 124 $100.00 2001-04-23
Application Fee $300.00 2001-04-23
Maintenance Fee - Application - New Act 2 2003-04-23 $100.00 2003-03-21
Maintenance Fee - Application - New Act 3 2004-04-23 $100.00 2004-03-31
Maintenance Fee - Application - New Act 4 2005-04-25 $100.00 2005-03-11
Maintenance Fee - Application - New Act 5 2006-04-24 $200.00 2006-03-10
Request for Examination $800.00 2006-03-22
Maintenance Fee - Application - New Act 6 2007-04-23 $200.00 2007-03-16
Maintenance Fee - Application - New Act 7 2008-04-23 $200.00 2008-03-20
Maintenance Fee - Application - New Act 8 2009-04-23 $200.00 2009-03-13
Expired 2019 - Filing an Amendment after allowance $400.00 2009-11-05
Final Fee $300.00 2010-01-04
Maintenance Fee - Application - New Act 9 2010-04-23 $200.00 2010-03-10
Registration of a document - section 124 $100.00 2010-07-09
Maintenance Fee - Patent - New Act 10 2011-04-25 $250.00 2011-03-09
Maintenance Fee - Patent - New Act 11 2012-04-23 $250.00 2012-03-07
Maintenance Fee - Patent - New Act 12 2013-04-23 $250.00 2013-03-06
Maintenance Fee - Patent - New Act 13 2014-04-23 $250.00 2014-03-11
Maintenance Fee - Patent - New Act 14 2015-04-23 $250.00 2015-03-09
Maintenance Fee - Patent - New Act 15 2016-04-25 $450.00 2016-03-08
Maintenance Fee - Patent - New Act 16 2017-04-24 $450.00 2017-03-15
Maintenance Fee - Patent - New Act 17 2018-04-23 $450.00 2018-04-03
Maintenance Fee - Patent - New Act 18 2019-04-23 $450.00 2019-03-06
Maintenance Fee - Patent - New Act 19 2020-04-23 $450.00 2020-04-01
Current owners on record shown in alphabetical order.
Current Owners on Record
SIEMENS INDUSTRY, INC.
Past owners on record shown in alphabetical order.
Past Owners on Record
MCINTOSH, IAN B. D.
SIEMENS BUILDING TECHNOLOGIES, INC.
Past Owners that do not appear in the "Owners on Record" listing will appear in other documentation within the application.

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Date
(yyyy-mm-dd)
Number of pages Size of Image (KB)
Description 2001-04-23 42 1,644
Representative Drawing 2001-12-28 1 62
Abstract 2001-04-23 1 37
Claims 2001-04-23 11 404
Drawings 2001-04-23 24 676
Cover Page 2002-01-11 1 90
Description 2009-11-05 44 1,746
Abstract 2009-01-28 1 25
Claims 2009-01-28 7 298
Representative Drawing 2010-05-17 1 64
Cover Page 2010-05-17 2 103
Correspondence 2010-04-06 1 14
Correspondence 2010-04-06 1 14
Assignment 2001-04-23 5 231
Assignment 2010-07-09 10 362
Prosecution-Amendment 2006-03-22 1 37
Prosecution-Amendment 2008-07-31 2 89
Prosecution-Amendment 2010-03-29 1 13
Prosecution-Amendment 2009-01-28 12 509
Correspondence 2009-09-29 2 74
Correspondence 2009-10-20 1 16
Correspondence 2009-10-20 1 18
Prosecution-Amendment 2009-11-05 4 185
Correspondence 2010-01-04 1 41
Correspondence 2010-02-10 3 57