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BUREAU OF MINES
INFORMATION CIRCULAR/1989
Predicting the Failure of Electric Motors
By Gerald T. Homce
UNITED STATES DEPARTMENT OF THE INTERIOR
Information Circular 921 1
ii
Predicting the Failure of Electric Motors
By Gerald T. Homce
UNITED STATES DEPARTMENT OF THE INTERIOR
Donald Paul Hodel, Secretary
BUREAU OF MINES
T S Ary, Director
Library of Congress Cataloging in Publication Data:
Homce, Gerald T.
Predicting the failure of electric motors.
(Bureau of Mines information circular; 9211)
Includes bibliographical references.
Supt. of Docs, no.: I 28.27:9211.
1. Mining machinery— Electric driving— Reliability.
2. Failure
ime data analysis.
I. Title. II. Series: Information circular (United States. Bureau
of Mines); 9211.
TN295.U4 [TK4059.M55] 622 s
[622'.028]
88-600247
CONTENTS
Page
Abstract 1
Introduction 2
Background 2
Recent failure prediction research 2
Data collection 3
Failure prediction research results 3
General 3
Methodology 3
Theoretical analysis of algorithm 4
System specification 4
System structure 4
System operation 5
System utility 5
Model development 6
Cable-connected motor models 6
Induction motor deterioration model 6
Computer analysis 7
Cable deterioration 7
Motor deterioration 9
Experimental program 9
Deteriorating-motor experiments 10
Destructive testing 11
Electrically excited vibration 11
Theoretical study 11
Laboratory experiments 11
Practical applications of research results 12
Computer model use 12
Off-line failure prediction techniques 13
Summary 13
U.S. Navy submarine power system monitoring 14
General 14
Submarine data acquisition 14
Summary 15
Conclusions 15
ILLUSTRATIONS
Page
1. Cable-connected motor system for Bureau of Mines laboratory test program 3
2. Laboratory equipment for data collection from cable-connected motor system 3
3. Functional diagram of condition and performance monitoring system 5
4. Information flow in failure prediction algorithm 6
5. Effect of line-to-line cable insulation deterioration on feature complex power 8
6. Effect of line-to-line cable insulation deterioration on kappa transform values for complex power 8
7. Illustration of a nonlinear response as deterioration approaches fault level 8
8. Effect of winding-to-winding insulation deterioration within a motor, on feature current 9
9. Effect of winding-to-winding insulation deterioration within a motor, on kappa transform values
for current 9
10. Effect of winding-to-winding insulation deterioration within a motor, on motor efficiency 10
11. Effect of motor load on feature current, for a constant level of winding-to-winding
insulation deterioration 10
12. Line current values, I a , I b , and 1^ for sudden failure during destructive testing of motor 11
13. Equipment connections for submarine power system data acquisition 15
UNIT OF MEASURE ABBREVIATIONS USED IN THIS REPORT
A ampere
mA
milliampere
hp horsepower
pet
percent
Hz hertz
rpm
revolution per minute
kW kilowatt
PREDICTING THE FAILURE OF ELECTRIC MOTORS
By Gerald T. Homce 1
ABSTRACT
A system capable of monitoring a mine electrical power system to detect incipient electrical compo-
nent failure could significantly improve power system safety and availability. The U.S. Bureau of Mines
funded a contract with The Pennsylvania State University (Perm State) to establish the theoretical and
technical framework for such a system. This report briefly outlines the contract, reviewing related
Bureau and Penn State work prior to its award, and describes support work carried out by the Bureau.
The main focus of the report is on research efforts by Penn State and subsequent results. An existing
algorithm for incipient-failure detection and classification was studied, and recommendations are made
to improve its performance. In addition, mathematical models of cable-connected motor systems and
deteriorating motors were developed and implemented on computers. These models and laboratory
tests were used to study and document relationships between component deterioration and electrical
terminal effects. The project was supported by the U.S. Navy and the nature of its interest in the
application of failure prediction techniques is also included.
Electrical engineer, Pittsburgh Research Center, U.S. Bureau of Mines, Pittsburgh, PA.
INTRODUCTION
Monitoring and control technology is currently being
applied to many aspects of mining operations. Computer-
based systems can now aid mine operators in the manage-
ment of production, health and safety monitoring, process
and equipment control, and other activities. Another area
in which such systems can be applied is the monitoring of
mine electrical power systems for maintenance purposes.
U.S. Bureau of Mines research, through contract JO338028
with The Pennsylvania State University (Perm State),
focused on the development of a system to monitor the
performance of mine power systems and to allow detection
of component deterioration in very early stages.
A system capable of detecting incipient electrical
component failure would impact mining operations in two
areas. One benefit would be the implementation of
predictive maintenance programs to increase equipment
availability and thereby improve productivity. In addition,
such a system would enhance personnel safety. 2 Electrical
system deterioration, detected early, could often be cor-
rected before a substantial shock or fire hazard developed.
Furthermore, the need for emergency power system main-
tenance, which tends to be rushed and substandard, could
often be avoided.
This report details the progress of Bureau research in
the detection of incipient electrical component failure and
results to date. A brief review of past failure prediction
research and its contribution as a foundation for present
work is given. A description of contract JO338028 and an
overview of laboratory support work by the Bureau are
presented. The majority of the report covers research
efforts and results by Perm State. A section is included
that describes U.S. Navy involvement in this program.
BACKGROUND
Research in the detection of incipient electrical
component failure, conducted from 1974 to 1983, formed
the foundation for the development of an electrical power
system monitoring-failure prediction system. The Bureau
funded a research program at Perm State in 1974 (grant
GO155003), to develop a continuous monitoring system
that could predict electrical safety hazards on mine
electrical power systems. Ultimately, the program failed
to produce an operational monitoring system; it did,
however, yield valuable information for subsequent failure
prediction research. 3 Work continued at Penn State with
university research funds, and efforts focused on areas that
were unresolved at the close of the grant. 4 Early progress
included more clearly defining the problem, creation of a
system concept, identifying relationships between
terminal electrical characteristics and failure modes, and
development of a failure prediction algorithm to serve as
a basis for the proposed system. The primary results were
a working algorithm and identification of monitoring
system requirements. A number of points remained to be
addressed, however, such as algorithm performance
problems, further study of failure modes and their
electrical signatures, and internal failure of induction
motors.
At this stage, the Bureau examined the research per-
formed since 1974, and a decision was made to establish a
new program to further study the application of failure
prediction to mining electrical power systems. Conse-
quently, contract JO338028 was awarded to Penn State in
August 1983.
RECENT FAILURE PREDICTION RESEARCH
This contract specified research that would further
develop the basis for a power system performance moni-
toring-failure prediction system. The primary original
objective was the collection and analysis of power system
deterioration characteristics, to improve the existing failure
^Morley, L. A., F. C. Trutt, and J. L. Kohler. Final Report-Evalu-
ation of Coal-Mine Electrical-System Safety (grant G0155003, PA State
Univ.). BuMines OFR 160-81, 1981, 202 pp.; NTIS PB 82-139338.
'Work cited in footnote 2.
prediction algorithm. Contract modifications extended
research efforts to include mathematical modeling of
cable-connected motor systems and induction motors,
examination of electrically excited stator vibration effects,
and specification of the proposed monitoring system.
"Kohler, J. L. A Decision-Theoretic Method for the Classification of
Incipient-Failure Patterns Which Are Characteristics of Deteriorating
Mine Power-System Components. Unpublished Ph.D. Thesis, PA
State Univ., Mar. 1983, 141 pp.; available upon request from J. L.
Kohler, PA State Univ., University Park, PA.
DATA COLLECTION
Under the provisions of contract JO338028, a laboratory
power system and a data acquisition system were estab-
lished at the Bureau's Pittsburgh (PA) Research Center,
as per specifications from Penn State. The arrangement
was designed to generate data to support analytical work
for the contract, by sampling voltage and current phasors
from an operating three-phase cable-connected motor
system. More specifically, the collected information
covered areas such as-
Laboratory motor parameters,
Data acquisition system characteristics,
Validation of mathematical power system models,
and
3-phase power
Induction
motor
n) (m) (n)
Motor
alternator
set
5 or
10 hp
Fault
leakage -cur rent
simulator
17.5 kW
Single-phase
alternator
Variable load
bank
-vw-
-vw-
Figure 1.— Cable-connected motor system for Bureau of Mines
laboratory test program.
Various modes of power system deterioration for
evaluation of the prediction algorithm.
Specified experiments called for a squirrel-cage induc-
tion motor with deterioration simulated on the incoming
cable. The motor was directly coupled to a single-phase
alternator that in turn powered a variable load bank to
create motor load cycles. Faults external to the motor (on
the cable) were simulated by a variable load placed across
various phase and ground combinations. The overall
layout of the cable-motor system is shown in figure 1. The
Bureau's data acquisition equipment included signal con-
ditioning interfaces, a fiber optic signal transmission
system, and a data collection computer system. Informa-
tion was digitized, processed, and organized using Bureau
developed software. Figure 2 shows the general arrange-
ment of the Bureau's experimental apparatus for failure
prediction research.
To motor-
alternator set
Fiber optic
receivers
Voltage and current
sensing and
signal conditioning
Motor test
station
Laboratory
3-phase
power source
Fiber optic
transmitters
Laboratory
data
acquisition
computer
system
Data files
storage
Figure 2.— Laboratory equipment for data collection from cable-
connected motor system.
FAILURE PREDICTION RESEARCH RESULTS
GENERAL
The original objective of the contract, was refinement
of the existing failure prediction algorithm through analysis
of laboratory generated data. Subsequent modifications
broadened the program scope to include efforts that would
support this refinement, including extensive computer
modeling of power systems and motors undergoing dete-
rioration, examination of electrically excited vibration in
motors, and application of failure prediction techniques to
Navy submarine power system maintenance. This section
will describe the results of contractor work in all these
areas, with the exception of Navy applications, which are
discussed in a separate section.
METHODOLOGY
Initial work focused on identifying performance anom-
alies in the existing algorithm and determining the cause
of these anomalies. Project researchers have determined,
however, that nearly all anomalies are related to hardware
sampling and monitoring system sensitivity problems. 5
A revised sampling scheme eliminated most of the
hardware-related problems, but difficulties associated with
monitoring sensitivity required more extensive investi-
gation. This investigation called for more and different
power system data, and in response, additional laboratory
experimentation was carried out. Such an approach, how-
ever, proved too expensive and time-consuming for the
volume of data needed; therefore, computer modeling of
deteriorating power system components was undertaken to
generate terminal values needed for the work. The devel-
opment of appropriate models not only freed researchers
from the limitations of available laboratory equipment, but
decoupled the analysis from the hardware characteristics
of any particular measurement system.
In addition to mathematical modeling of deteriorating
power systems, other areas added, to the original contract
scope of work were specification of a proposed perfor-
mance and condition monitoring system and examination
of electrically excited motor vibration with respect to
failure prediction techniques. These modifications not only
further refined existing failure prediction theory, but also
generated independent and more immediately useful
results. Details of results for each area are presented in
the balance of this section.
THEORETICAL ANALYSIS OF ALGORITHM
A significant performance anomaly from past tests of
the failure prediction algorithm was a large standard
deviation among samples for identical cases of component
deterioration. A firm correlation was established between
this problem and the method used for sampling voltage
and current phasors. The sampling technique used prior
to this contract was unable to accurately trigger at a
predetermined target current level. The associated devia-
tion in sampling points resulted in unacceptable variations
in voltage and current values. Without a series of accu-
rately reproduced points from motor load cycles, the algo-
rithm had difficulty identifying changes in features (values
derived from voltage and current values) that were due
only to power system deterioration. A modified triggering
system rectified this situation by sampling a range of
values, up to nine cycles in duration. The data acquisition
software could then extract from this range the best match
to the desired target level.
Sampling trigger techniques are also a probable source
for another anomaly, asymmetry of algorithm performance
among different phases. Past results have indicated
decreasing prediction accuracy for phases A, B, and C,
respectively, and this may be attributable to sampling
schemes that monitor phase A for triggering. A solution
to this is the use of a sampling method that triggers from
'Pennsylvania State University. Prediction of Incipient Electrical
Component Failure. Ongoing BuMines contract J0338028; for inf.,
contact J. C. Cawley, TPO, Pittsburgh Research Center, BuMines,
Pittsburgh, PA.
some characteristic of motor loading, such as speed, that
does not rely on one particular phase. A suitable trig-
gering technique should also be independent of system
deterioration.
Early testing of the algorithm also revealed erratic
results for conductor degradation on a power system.
Tests using data representing this mode of deterioration
randomly exhibited either extremely good or extremely
poor classification accuracy. The electrical characteristics
of physical conductor degradation were, therefore,
addressed in a series of tests. These tests proved very dif-
ficult to complete because of temperature effects, which
often had more influence on cable resistance than reduc-
tion in conductor cross-sectional area. Test results,
however, along with computer simulation of conductor
degradation, sufficiently defined the deterioration to permit
decisions regarding further examination of the problem.
Analysis indicated that in No. 8 AWG cable and larger,
the incremental resistance change for severing of individual
strands was beyond the sensitivity of the existing moni-
toring system. In addition, cable resistance change reacts
exponentially to point reduction in conductor cross-sec-
tional area, with an eventual sharp increase in heating
effect influence and ultimate burn through of the cable.
These factors give conductor degradation a somewhat all
or nothing characteristic, and thus it was determined that
monitoring for such deterioration was beyond the capa-
bilities of the present failure prediction system.
SYSTEM SPECIFICATION
The on-line incipient failure prediction system proposed
by the contractor is based on a software approach that
utilizes simple, rugged sensors and microcomputers. 6 With
one microcomputer servicing multiple motors, and rela-
tively low hardware costs, motors as small as 5 hp could be
monitored economically. The difficulties and expense of
such an approach are primarily in the development of soft-
ware, which requires research into degradation mecha-
nisms for electrical components.
System Structure
The proposed system monitors power system bus vol-
tage, using simple voltage division circuits. While present
work monitors all three phases, future implementations
may only require one phase voltage value, depending on
sensitivity requirements. Line currents for each power sys-
tem component in question are monitored using current
transformers shunted with resistors to produce an output
voltage proportional to the primary current. Voltage and
current signals then undergo analog-to-digital conversion
for input to a microcomputer. Given the comparative
techniques employed by the failure prediction algorithm
software, the resolution of the monitoring system hardware
is more important than its absolute accuracy. Thus, rela-
tive accuracy and long-term stability are important factors
^ork cited in footnote 5.
in instrumentation selection. Based on results thus far, the
proposed monitoring system will be capable of detecting
deterioration levels approximately two orders of magnitude
lower than those that will typically affect power system
performance or safety. Figure 3 is a functional diagram of
the proposed system. 7
System Operation
The current state of the failure prediction algorithm
requires that voltage and current phasors be sampled from
a reproducible point during motor operation. Most of the
data generated for this program have used a specific
current level (from phase A) during motor loading for this
reproducible point. The 60-Hz components of these
phasors are then used to calculate a number of features
for the power system, including system impedance (real
and imaginary components), complex power, and power
factor. These, along with the original voltage and current
phasors, form patterns that are used to evaluate system
condition.
The first step is a yes-no check for deterioration based
on the presence of negative sequence current. If deterio-
ration is detected, feature values are preprocessed, which
involves comparing features from the sample under evalu-
ation to a reference feature set that represents normal
operation (no deterioration).
The initial preprocessing step is the use of interphase
distance (kappa) transforms. Earlier research has demon-
strated that the actual values of pattern features are too
variable within the patterns to be useful for classification
of system deterioration. Use of the kappa transform, how-
ever, reduces the variability while retaining characteristics
unique to the pattern and the type of deterioration it
represents. The kappa transform is, by definition, the
change over time of the difference between feature values
for two particular phases. 8 If Xa, Xb, and Xc are feature
values for phases a, b, and c for some class of deteri-
oration, and X'a, X'b, and X'c are the reference feature
values for the same power system, the kappa transforms
are
K(l) = (Xa - Xb) - (X'a - X'b),
K(2) = (Xb - Xc) - (X'b - X'c),
and K(3) = (Xc - Xa) - (X'c - X'a).
These values then undergo a statistical level of
significance test based on feature standard deviations,
wherein each feature is assigned a +1 (significant
increase), (no change), or -1 (significant decrease). The
significance test reduces the effects of power system noise
when attempting to classify mode and location of
deterioration, particularly at low levels. The resulting
collection of mathematically modified features forms a
Motors
r^
\ /*
Current
transformers
3 -phase bus
Voltage
signals-
\
I
\
.11 S2 1
Current
transformer
signals
Signal
conditioning
I
Ana log- to-digital
converter
Microprocessor
s
Figure 3.— Functional diagram of condition and performance
monitoring system.
pattern in N-dimensional space (where N is the number
of features). The pattern is mapped into a partitioned
decision space that can determine the type and location of
deterioration. Such a partitioned decision space is trained
using data from motor systems operating under known
deterioration conditions.
This has been only a brief description of the failure
prediction algorithm, but more detail, as well as the
FORTRAN source code, can be found in the work cited
in footnote 4. Figure 4 is an information flow diagram for
the algorithm. 9
System Utility
The hardware and software, installed as an on-line
failure prediction system, would be capable of monitoring
both power system condition and performance. 10 Perfor-
mance monitoring focuses on operational characteristics of
system components and, using the electrical features listed
earlier in this section, could be used to study system
Work cited in footnote 5.
8 Work cited in footnote 5.
^Vork cited in footnote 5.
10 Work cited in footnote 5.
Z Digitized 7
signals /~
Fast fourier
transform
Feature
extraction
/ Voltage and 7
/current phasors/
"Symmetrical
components
"System
impedance
Continue
sampling
"Complex power
( and power factor)
"Voltage and
current phasors
£
Performance
evaluation
Kappa transform and
significance test
Z Performance /
information /
Feature pattern
classification
Report
generation
/System-component/
/ condition /
Figure 4.— Information flow in failure prediction algorithm.
application problems, load characteristics, and efficiency.
Performance evaluation can in some cases reveal power
system component problems, but condition monitoring, by
definition, attempts to accurately detect incipient compo-
nent deterioration as early as possible and determine its
type and location. The failure prediction system proposed,
used as an on-line monitoring system, would be capable of
detecting-
Cable insulation deterioration (line-to-line or line-
to-ground),
Motor stator turn-to-turn leakage (wye or delta),
Motor stator to ground leakage,
Uniform insulation leakage, and
Shorted connections.
Although much of the basic research for a failure pre-
diction system is complete, information from recent work
must still be incorporated into existing techniques and
further research conducted before such a system is ready
for implementation. Areas that will further refine failure
prediction monitoring include improvement of sensing and
signal processing hardware, a better understanding of the
relationships between motor deterioration and electrical
signatures, modification of detection and classification
software, and inclusion of mechanical parameters such as
electrically excited vibration in motors.
MODEL DEVELOPMENT
Efforts to refine the failure prediction algorithm created
a need for an economical method to generate terminal
values (voltage and current phasors) for deteriorating elec-
trical components. This led to the theoretical development
and computer implementation of mathematical models for
cable-connected motor systems undergoing cable deterio-
ration and for squirrel-cage induction motors experiencing
stator insulation failure. 11 With these models, researchers
created a data base of electrical features with which to
evaluate patterns associated with incipient failures.
Cable-Connected Motor Models
Mathematical modeling of a cable-motor system must
include the positive and negative sequence impedance pre-
sented by the induction motor. This information is
obtained using a per-phase equivalent circuit for an induc-
tion motor, with parameters taken from manufacturer's
data or laboratory tests. In addition to motor circuit
equivalent impedances, other important information for a
cable-motor system includes cable impedance, fault type,
fault location, and fault impedance. Applied voltage is
assumed to be known, and motor speed can be assumed
or determined by an iterative solution for a given line
current level. With this information, symmetrical compo-
nent techniques can be employed to determine voltage and
current values for specific phases.
The three general cases modeled using this approach
were conductor degradation (increased impedance), line-
to-ground cable leakage, and line-to-line cable leakage.
The simulation of conductor degradation has had only
limited use, since early in the program the failure predic-
tion algorithm was found to be unsuitable for detecting
this type of deterioration. Line-to-ground and line-to-line
fault modeling, however, were implemented in computer
programs and used to evaluate failure conditions. Results
of their use are discussed in the "Computer Analysis" sec-
tion, and a more detailed description of their theory and
use can be obtained from the work cited in footnote 5.
Induction Motor Deterioration Model
Initial attempts to model the effects of internal deteri-
oration on an induction motor used a symmetrical com-
ponent solution of the motor system equivalent circuit.
In this circuit analysis, turn-to-turn leakage is represented
by a reduction in the number of turns in a faulty phase.
The solution also requires that stator phase windings be
represented by concentrated full-pitched coils, that deteri-
oration of insulation has progressed to a zero-resistance
state, and that the motor is a two-pole machine. These
assumptions, however, severely limit the utility and
u Work cited in footnote 5.
accuracy for evaluation of deterioration involving turn-to-
turn leakage.
Given these limitations, a more general analysis
approach was pursued, resulting in a mathematical model
able to predict terminal values for an induction motor
experiencing a wide variety of internal stator faults. The
overall approach for construction of this model involved
the following seven steps.
1. The airgap magnetic flux (including space harmon-
ics) produced by a single stator coil is theoretically
evaluated.
2. The portion of this flux that links a second stator
coil is then determined.
3. Using the results of steps 1 and 2, an expression for
the mutual impedance between an arbitrary pair of stator
coils is determined.
4. Considering a winding to be a series connected set
of coils (these series sets are defined by the specific fault
situation), the results of step 3 may be summed to give
expressions for the mutual impedance between stator
windings.
5. Similar approaches to steps 1 through 4 are then
utilized to obtain expressions for self and mutual imped-
ances relating to stator-rotor, rotor-rotor, and rotor-stator
interactions.
6. The effects of leakage impedances are then added to
the model, and Kirchhoff s voltage law is utilized to obtain
a set of N-equations having the N-winding currents as
unknown quantities.
7. The equations of step 6 are then solved to give the
winding and line currents, symmetrical components
current, input power, and effective power factor. Effi-
ciency is also estimated.
A detailed theoretical development of this model is
available from the work cited in footnote 5.
COMPUTER ANALYSIS
Models for cable-connected motor systems and deteri-
orating motors were implemented in FORTRAN programs
and validated using experimental laboratory data. In gen-
eral, model predictions and laboratory data agreed well,
with differences remaining below a few percent. The
validated models were then applied to the analysis of
relationships between power system conditions-characteris-
tics and calculated terminal features. 12
Cable Deterioration
The application of the cable-connected motor models
addressed the following three general topics:
Feature patterns resulting from cable deterioration
in a cable-motor system.
Level of sensitivity required (from a failure
prediction monitoring system) to detect given levels of
deterioration.
Effect of different component types and sizes on
feature patterns.
The results from an analysis of cable leakage and its
effects on specific electrical features are presented in
graphical form in the work cited in footnote 5, where the
reaction of numerous features and their respective kappa
values are graphed for line-to-line and line-to-ground cable
leakage of varying severity. Figures 5 and 6 are examples
of these plots, which represent the change in feature values
and kappa values, respectively, for complex power as line-
to-line deterioration level increases. Although this infor-
mation is not directly applicable to failure prediction in the
form presented, it serves to give a general feel for deterio-
ration effects on a power system. More importantly
though, the sum of all such feature reactions is the key to
the pattern recognition process used in the failure predic-
tion algorithm. As described earlier in the report, the
kappa values are more useful for pattern recognition than
the actual feature values.
The cable-connected motor system models were also
used to evaluate system sensitivity; that is, to determine the
best possible performance from specific monitoring hard-
ware or select hardware for a desired level of sensitivity.
In order to study sensitivity, deterioration levels and
their relation to power systems needed to be better
defined. Deterioration levels of interest are those that
would normally go undetected. Practical limits for these
are levels above which protective devices will operate or
levels that cause changes noticeable to human operators.
In the first case, this would commonly be above 125 pet
normal line current; in the second case, an estimate based
on practical experience is a negative sequence current
25 pet or more of normal line current. Past results indi-
cated that deterioration could be detected well below these
values, and in the sensitivity analysis they were used as
upper limits of deterioration. This definition of deterio-
ration level limits is further supported by a tendency for
features to be linear at low levels of deterioration, and
nonlinear as fault levels are approached. Figure 7 is an
example of this tendency for a feature value and the third
kappa value of current. 13
12 Work cited in footnote 5.
13 Work cited in footnote 5.
, 64
L 48
O o>
°-§ 32
Q- Q.
i o
1
1
i
i
i
i
1 T 1
/Phase A
-
Phase B
f Phase C
1 1 1 1 1 1 1 1 1
10
20
30
40
50
60
70 80 90 10
LEAKAGE CURRENT, pet of normal full load current
Figure 5.— Effect of line-to-line cable insulation deterioration on
feature complex power.
0.0049
0.0098 ' 0.0491
LEAKAGE CURRENT PER UNIT
0.0982
Figure 6.— Effect of line-to-line cable insulation deterioration on
kappa transform values for complex power.
In actual sensitivity analyses, kappa values for various
levels of cable deterioration were examined. Sensitivity
required to detect a given leakage impedance was deter-
mined by the resulting change in the kappa value of a
measured feature. For example, if a 1,250-ohm line-to-
line leakage impedance causes a line current kappa value
of 100 mA, then the monitoring system used must have
resolution capable of detecting 100-mA change. In addi-
tion, random (uncontrollable) fluctuations in measure-
ments due to sampling errors, temperature changes, etc.,
must be well below 100 mA. Another consideration when
dealing with deterioration involving ground is the influence
of any power system grounding impedance. Since a
grounding impedance is in series with any line-to-ground
leakage impedance, it influences the leakage current. The
influence is minimal when the leakage impedance is one or
two orders of magnitude larger than the grounding imped-
ance; when the grounding impedance is high (ground flow
current limited to <1 A, for example), it acts to mask
changes in measured features. In the latter situation, mon-
itoring resolution must, therefore, be better than would be
necessary on a system with a lower grounding impedance,
to detect comparable deterioration levels.
Random fluctuations in a power system and monitoring
system cause measurement changes that do not relate to
I0 1
I0 2 I0 3 I0 4 I0 5
LEAKAGE IMPEDANCE, Si
Figure 7.— Illustration of a nonlinear response as deterioration
approaches fault level.
load changes or deterioration. These factors, which
include conductor temperature, air temperature and
humidity, sampling errors, and supply voltage fluctuations,
have an increasing influence on failure prediction accuracy
as changes due to deterioration become smaller. Although
they are not controllable, most effects can be predicted
and accounted for, if necessary. A 5-pct supply voltage
imbalance, for example, causes a small but significant
change in kappa values. The effects of this imbalance,
however, can be subtracted from total system imbalances
to improve detection accuracy, if necessary.
The third topic analyzed using the cable-connected
motor models was the effect of different types and sizes of
power system components on feature kappa values. The
type and size of cable (assuming correct sizing for load)
does not affect feature values, since cable impedances are
typically small compared to leakage impedances. Thus the
performance of the failure prediction algorithm is
unaffected by cable type or size.
The effect of motor type and size, however, is not so
easily identified or defined. Motor parameters can vary
drastically, even for machines with identical horsepower
ratings; consequently, their impedances will also vary
widely. Since a leakage path is essentially in parallel with
motor impedance, motor characteristics affect the accuracy
and sensitivity of failure prediction techniques. Although
use of per-unit values in analysis reduces the apparent
variation for dissimilar motors, the effects are still signif-
icant and become more pronounced as deterioration levels
increase. Analysis of these variations presented an addi-
tional problem, since identical leakage paths cannot be
created on systems with different components. The evalu-
ation, therefore, was carried out using several different
criteria for deterioration levels, and results for each were
compared.
The analysis involved producing several feature kappa
values for three different cable-motor systems with similar
leakage paths. For the systems examined, sensitivity to
similar deterioration was different for different motors
by as much as 36 pet, but the overall feature patterns
remained the same in almost all cases. This suggests that
while sensitivity criteria may have to be situation specific,
the incipient failure classification process is independent of
motor type and size. Additionally, the criteria used for
defining the level of deterioration had little effect on fea-
ture variation among the different motor types.
I 2 3 4 5 6
LEAKAGE CURRENT PER UNIT
Figure 8.— Effect of winding-to-winding insulation deterioration
within a motor, on feature current.
Motor Deterioration
Analysis of computer-generated deteriorating motor
data was less extensive than that for cable-motor systems,
since the motor model had been available for a shorter
time and input data were not as readily available.
Terminal values and the resulting features were produced,
however, for a motor undergoing turn-to-turn stator dete-
rioration, both within one winding and between two
windings (phase to phase). The runs made were not com-
prehensive and the analysis was not exhaustive, but feature
reactions to various parameter changes can be shown by
selected graphs from the work cited in footnote 5.
Figures 8 and 9 are the feature values and kappa values,
respectively, of line current for a motor at 75 pet full load,
and various levels of winding to winding leakage.
Figure 10 shows motor efficiency for the same situation, as
deterioration increases. Line current for the same motor
and leakage path are shown in figure 11, but for a constant
deterioration level and varying motor load. Additional
cases examined were the effect on features of changing
leakage path location (winding to winding) while holding
load and deterioration level constant, and the behavior
exhibited by features during turn-to-turn leakage within the
same winding.
The information derived from analyses using the cable-
connected motor models and deteriorating motor model
is essential to application of the failure prediction
algorithm. The results better define the effects of
numerous power system and deterioration parameters on
terminal electrical features, and will allow more effective
1 2 3 4 5 6
LEAKAGE CURRENT PER UNIT
Figure 9.— Effect of winding-to-winding insulation deterioration
within a motor, on kappa transform values for current.
use of these features for incipient failure detection and
classification.
EXPERIMENTAL PROGRAM
The experimental program at Perm State supported
analytical work by aiding in the development of mathe-
matical deterioration models, validating the completed
models, and investigating various application issues. Most
10
I 2 3 4 5 6
LEAKAGE CURRENT PER UNIT
Figure 10.— Effect of winding-to-winding insulation deterioration
within a motor, on motor efficiency.
480 490 500 5I0 520 530 540 550 560 570 580 590
SPEED, rpm
Figure 11. -Effect of motor load on feature current, for a
constant level of winding-to-winding insulation deterioration.
work involved laboratory simulation of a deteriorating
induction motor and destructive testing of motors. 14
Deteriorating-Motor Experiments
An extensive data collection program was carried out to
document feature patterns associated with motor deterio-
ration and to develop a data base of patterns for use in
development of pattern classification functions. A nonde-
structive laboratory-simulation approach was used, utilizing
a Hampden Universal Machine, a deterioration simulator,
and a data acquisition system. Testing was organized to
simulate several different fault types at different stator
14 Work cited in footnote 5.
locations, in delta- and wye-connected motors. Research-
ers had direct control over test conditions relating to
motor-winding connections, deterioration simulation, and
motor loading. Research requirements were arranged into
logical test procedures defined by the following system
parameters:
Winding connection (delta or wye).
Deterioration type (phase-to-phase, phase-to-ground,
or within a single phase).
Deterioration location (within windings).
Motor load.
Deterioration level.
The resulting experimental procedure had a total of 322
test cases. The primary result of the tests was a large col-
lection of electrical feature patterns, but a number of
general comments can be made regarding the behavior
exhibited by the deteriorating motor.
Features that were more sensitive than others to change
in winding insulation degradation included the following:
Power factor at no-load conditions.
Line impedance.
Magnitude of line currents.
Zero and negative sequence currents.
Rotor double-frequency component.
Power factor exhibits a marked change with leakage
level increase, but the effect diminishes quickly when the
motor is loaded. The line currents also display increasing
imbalance as deterioration increases, but unlike power fac-
tor, their relative positions remain constant for motor load
changes. Similarly, zero and negative sequence current
increase proportionally to deterioration, while remaining
independent of motor load.
Although the connection between deterioration level
and negative sequence current was evident from test
results, the relationship was not consistent. Additional
analysis indicated that leakage current and negative
sequence current are directly related for constant leakage
path potential. This was then extended to suggest that a
direct relationship exists between negative sequence cur-
rent level and power consumed by a leakage path, for cur-
rents limited only by leakage impedance. Verification of
this is only preliminary, but such a correlation would allow
negative sequence information to be used as an indication
of deterioration severity. Double-frequency rotor currents
are related to negative sequence stator currents, and are
also extremely sensitive to system imbalance. They, how-
ever, can only be monitored on wound-rotor machines.
11
Destructive Testing
Accelerated life cycle testing of induction motors was
conducted to verify model predictions and laboratory dete-
rioration simulations. Test results are terminal value fea-
ture patterns from actual motor failures for comparison
to simulated or modeled values. The acceleration pro-
cesses, however, were not quantified, and so no insulation
life predictions are intended.
The method chosen for accelerated aging was a combi-
nation of electrical stressing, thermal stressing, and mois-
ture exposure. High dc voltage was placed across stator
windings for electrical stress; thermal stresses were created
by overloading the motor while restricting ventilation.
Moisture was introduced by a humidifier and by direct
spraying.
Three-phase voltage and current values were monitored
during testing, with phasors measured and digitized for
storage at regular intervals, and continuous magnetic
taping used to ensure recording of unexpected failures. In
addition, insulation resistance tests were conducted at
regular intervals for comparison to terminal value feature
patterns.
One specific test resulted in a motor failure on the
208th day of operation. This was a sudden failure which
produced feature patterns that closely match those for a
winding-to-winding leakage path simulated on the Hamp-
den Universal Machine. Although line currents exhibited
a sharp increase at the point of failure, the test motor
continued to run after the failure occurred. Line current
changes at failure are shown in figure 12. 15
In addition to the correlation between actual and
simulated failure, the destructive testing program helped
identify several monitoring implementation problems. The
foremost of these was random fluctuations in terminal
values when measured over a long period of time. The
factors that were most notable during testing were bus
voltage imbalances and temperature changes, which caused
impedances to vary.
Overall, the experimental program successfully sup-
ported failure prediction theory development and
mathematical modeling of motor deterioration. Additional
benefits included verification of laboratory simulations and
examination of monitoring implementation problems.
ELECTRICALLY EXCITED VIBRATION
One aspect of the failure prediction program was a pre-
liminary investigation of electrically excited vibration in
deteriorating motors. A theoretical study of electrically
excited vibration was made to determine the feasibility of
modeling its relationship to stator deterioration. Such a
model could be used to develop vibration monitoring tech-
niques as part of a failure prediction system. Limited
laboratory experiments were also carried out to observe
electrical-mechanical interactions for a deteriorating
motor.
200 300 400
TAPE COUNTER
500 600
Figure 12.— Line current values, l a , l b , and l c , for sudden failure
during destructive testing of motor.
Theoretical Study
This part of the investigation involved a literature
search and subsequent review of pertinent information on
electrically excited stator vibration. Researchers concluded
that it is feasible to construct an electrically excited stator
vibration model, and a general theoretical approach for
such modeling was outlined. An approximate analysis,
using the general approach outlined, was used to compare
a normal motor and one undergoing phase-to-phase stator
deterioration. The differences in the resulting frequency
spectra were significant and indicated that vibration moni-
toring may prove useful for incipient failure detection.
Laboratory Experiments
Experiments were conducted to differentiate between
electrically and mechanically induced motor vibration, and
to identify the electrically induced vibration due specifically
to motor deterioration. Several motors were fitted with
vibration transducers, with the outputs monitored on a
waveform analyzer. Vibration spectra were recorded for
the motor running with no deterioration, the motor rotat-
ing immediately after removing power, and the motor
running under single-phasing conditions. Subtracting the
vibration present after removing motor power from total
vibration leaves only electrically excited vibration, which
can then be used in comparison of deteriorated and non-
deteriorated cases. For the tests run, two important
results were noted. The change in vibration spectra due
solely to electrical imbalance during motor deterioration is
significant, but this change can be entirely different for
different motors.
15 Work cited in footnote 5.
12
In summary, this investigation has determined that
modeling of electrically excited vibration in deteriorating
induction motors is feasible. Furthermore, results from
laboratory experiments indicate that to continue research
in this area, such modeling will likely be necessary because
of the complex and machine specific relationships between
electrical deterioration and mechanical vibration. Further
investigation of this topic could enhance the capabilities of
incipient failure detection techniques by adding an
additional parameter with which to recognize motor
deterioration.
PRACTICAL APPLICATIONS OF RESEARCH
RESULTS
The theoretical and experimental work under this pro-
gram have thus far been described only as applied to
development of an on-line automated failure prediction
system. The concepts and tools resulting from these
efforts, however, have independent value and may be
immediately useful to maintenance engineers. The utility
of these items can be described under two categories:
(1) use of computer models for evaluation of power system
component behavior and (2) use of simplified feature
pattern analysis for manual incipient failure detection.
Computer Model Use
Analysis of power system components or branches can
be augmented by computer simulation of system condi-
tions. Examples of situations to which models could be
applied include
Examination of terminal feature patterns for fre-
quently encountered component failures,
Determining possible causes for observed component
problems, and
Analysis of effects on performance, for changes in
component characteristics or application.
The following are brief descriptions of the computer
models developed under this program to support failure
prediction research.
Cable- connected motor modeling first requires the use
of a program to determine positive and negative sequence
impedances. A program named SPEED is used if motor
line currents are available, and another called MOTOR Z
is employed if motor speed is known. The balance of
required input for either program is
Stator resistance,
Stator reactance,
Magnetizing branch resistance,
Magnetizing branch reactance,
Rotor resistance,
Rotor reactance,
Motor synchronous speed, and
Phase A to ground voltage.
Outputs in either case are positive and negative sequence
motor impedances, which are required input for the cable-
connected motor system modeling programs.
The modeling programs and the conditions they
simulate are
CASE 1-conductor degradation.
CASE 2-line-to-ground deterioration.
CASE 3-line-to-line deterioration.
Input parameters for the programs are
Phase A to ground voltage.
Motor horsepower rating.
Motor positive sequence impedance.
Motor negative sequence impedance.
Cable impedance.
Leakage (fault) impedance.
Leakage (fault) position.
Voltage base.
Impedance to ground (CASE 2 program only).
The models compute the voltage and current phasors that
would exist at the line side of the cable-connected motor
system in question. From these values, the programs
derive and output a number of features including current
(echo), complex power and its components, power factor,
complex impedance and its components, current symmet-
rical components, and kappa values for all of these fea-
tures (requires reference data set). Instructions for use
of these programs as well as their FORTRAN source code
are found in the work cited in footnote 5.
The deteriorating motor model developed under this
program, MTRMDL, simulates internal stator deterio-
ration, and so requires extensive motor design and deterio-
ration description data for input. Input information
13
selection requires a basic understanding of electric machin-
ery as well as the modeling techniques used, and includes
data describing
Network connections and impedances for the motor
and deterioration condition in question,
Complete physical and electrical characteristics of
the motor, and
Motor operating conditions.
The output of MTRMDL is voltage and current phasors
at the subject motor's terminals. The work cited in
footnote 5 contains the FORTRAN source code for
MTRMDL and instructions for program use, including
selection of input information.
Off-Line Failure Prediction Techniques
Failure prediction theory has not yet reached a point at
which it can support an on-line fully automated system for
incipient failure detection. Several aspects of this research,
however, are sufficiently developed to have some utility
for manual implementation. Although when considering
a nonautomated approach, definite guidelines are not
available to allow quick detection or classification of power
system deterioration, application of these manual
evaluation techniques can still provide more information
on component operating performance and condition than
is normally possible. To allow use of such performance
and condition monitoring on an interim basis, a program
called THREE-PHASE ANALYZER was derived from
the formal incipient failure detection algorithm.
The first step of off-line monitoring would be to
measure and record the necessary values from the system
under test. Any method used must accurately record the
voltage and current values while maintaining all phase rela-
tionships. Selection of a data acquisition system, however,
raises many of the questions brought forth earlier in this
report, such as required accuracy and resolution of
hardware, method of analog-to-digital conversion, sampling
point reproducibility, sample length, sampling method,
sampling speed, and random fluctuations in the power and
monitoring system. Although these factors are important
for acquisition of accurate and appropriate data, they are
situation specific and will not be covered here.
Input to the THREE-PHASE ANALYZER program
consists of the line-to-neutral voltage and line-current
phasors monitored at the terminals of the electrical com-
ponent in question. The program requires input of
phasors for a reference condition (no deterioration) as
well, in order to calculate feature value differences
(between reference and present case) and kappa values.
Reference data should come from samples at some known
condition, such as when a motor is new or recently rebuilt.
Output from the program consists of
Voltage phasors (echo),
Current phasors (echo),
Complex power and its components,
Power factor,
Complex impedance and its components,
Symmetrical components for all the above, and
Kappa values for all the above.
Further directions for use and the FORTRAN source code
listing of THREE-PHASE ANALYZER are in the work
cited in footnote 5.
Output from the THREE-PHASE ANALYZER
contains information (raw feature values for the test data)
useful for power system component performance evalua-
tion. Items such as voltage balance and phase relationship
can be used to check power supply quality, while current
levels, power consumed, and power factor describe motor
load level and general efficiency for the application. The
raw feature values are then subtracted from the reference
set to yield feature change values; interphase distance
transforms are applied to create kappa values. Using this
information, changes in system-component condition can
be detected. A notable rise in negative sequence current
for instance (not due to supply voltage imbalance), indi-
cates some sort of deterioration, and examination of other
feature changes can better define likely locations and
modes for the incipient failure. This sort of analysis would
be most productive if progressive deterioration can be
identified and documented, from point of first detection to
actual failure. Such trending information will be essential
for the eventual extension of incipient failure detection to
accurate failure prediction.
Computer modeling and manual failure prediction are
direct results of efforts to create an automated failure pre-
diction system. Although their practical applications are
limited, in appropriate situations they can be useful tools
for maintenance engineers in the evaluation of power
system component performance and condition.
SUMMARY
Research by Pcnn State under the failure prediction
program has focused on establishing a theoretical frame-
work for a feasible incipient failure prediction system.
Work to refine an existing failure prediction algorithm
exposed many aspects of electrical deterioration theory
14
that required more development. The necessary additional
analysis involved mathematical modeling of deteriorating
cable-connected motor systems and induction motors with
internal deterioration, and the computer implementation
of the models. Extensive laboratory testing was also con-
ducted to simulate deterioration conditions. In addition,
a theoretical and experimental examination of electrically
excited vibration determined its utility for deterioration
detection, and the feasibility of mathematically modeling
its effects.
Through these activities, researchers identified areas on
which to focus analysis, implemented computer models
and experimental programs to carry out the analysis and,
as a result, defined many relationships between terminal
electrical features and system component deterioration.
Additionally, they specified the proposed incipient failure
detection system, established the significance of electrically
excited vibration effects, and described immediate applica-
tions for the interim results of this program.
U.S. NAVY SUBMARINE POWER SYSTEM MONITORING
GENERAL
SUBMARINE DATA ACQUISITION
The Submarine Monitoring Maintenance Systems Office
of the U.S. Navy partially funded the failure prediction
program, under agreement N002485RAAZ001 with the
Bureau. The U.S. Navy is interested in the application of
failure prediction techniques to existing submarine power
system maintenance programs, and specifically requested-
An examination of electrically excited vibration in
induction motors, the feasibility of modeling its effects, and
evaluation of its utility for deterioration detection;
General specifications for an on-line monitoring
system, including a definition of its capabilities; and
Delivery of data collection hardware, analysis
software, and procedures documentation, for use as an off-
line performance-condition monitoring system.
Electrically excited vibration and on-line monitoring
system specifications were covered in the previous section.
The last item listed, however, is a deliverable that involves
measuring voltage and current phasors from a power
system component, processing these phasors using the
THREE-PHASE ANALYZER program, and manually
analyzing the results to evaluate performance and possibly
detect incipient deterioration. The purpose of such a
monitoring system is to give Navy engineers an interim
failure prediction method with which to judge the merits
and feasibility of expanding their monitoring techniques.
Use of the THREE-PHASE ANALYZER and manual
analysis of terminal feature values have been previously
discussed, but off-line monitoring also requires some
method of collecting information from a power system on-
board a nuclear-powered submarine. Voltage and current
values must be obtained in such a manner that identifica-
tion, time base, and sequence relationships remain intact.
In addition, original power system magnitude values must
be available from reproduction signals. Bureau personnel,
therefore, researched, designed, and constructed a portable
data collection system to meet these criteria.
Bureau engineers visited a nuclear-powered submarine
in order to attempt data collection from the power system,
and assess the requirements for an on-board data acquisi-
tion system. It was determined that beyond functional
requirements, any system devised must be reasonably
simple and safe to operate, small and light because of
physical constraints on-board a submarine, and self-
contained for convenience.
Basic components for the system are a data collection-
storage device, sensors and leads for connection to the
power system, and an interface unit to link the sensors and
leads to the recording device. The first two categories
were filled by commercially available items, while the
interface required custom design and construction to
address the unique characteristics and environment of a
submarine power system. A portable seven-channel FM
instrumentation tape recorder-reproducer was selected for
signal recording and storage. Clamp-on-type current
15
transformers are used to sense line currents, while direct
connections monitor line-to-line voltages. An interface
unit was designed to connect sensors and the recorder,
which isolates and reduces voltage inputs, monitors correct
phase rotation, shunts current transformer outputs to
create voltage signals, and amplifies these line current
signals as required for input to the recorder. Figure 13
illustrates the on-board data collection arrangement.
The complete system was tested by the Bureau, using a
power system that simulated distributed capacitance
grounding as would be the case for a submarine distribu-
tion system. Use of the system and data collection
procedures were completely documented, and the system
was demonstrated for Navy personnel.
SUMMARY
Results for the failure prediction program, including
monitoring system specifications, failure prediction con-
cepts-theories, and mathematical models for power system
component deterioration, have been delivered to the U.S.
Navy. In addition, procedures and hardware for off-line
power system performance-condition monitoring have been
demonstrated and delivered.
From power
system bus
Motor
contactor
case
Current '
probes
c£i
c<
-Voltage
leads
oA Va-B<>
oB Vb-C &
«C Vc-A°-
IA »
IB °"
Ic °"
Signal conditioning
interface
♦ * •
To motor under test
Instrumentation
tape recorder
Figure 13.— Equipment connections for submarine power
system data acquisition.
CONCLUSIONS
The original goal of this program was improvement of
the electrical component failure prediction algorithm
developed by Penn State. Research scope was expanded,
however, to further study the relationships between
component deterioration and electrical terminal effects.
The documentation of these relationships is the most
important result of this research, since it forms much of
the basis necessary for automated on-line failure
prediction. An example from this theoretical basis is the
predictable relationship between negative sequence current
level and the power consumed in a deterioration leakage
path.
The analysis techniques and tools developed to study
deteriorating electrical components are another significant
result of this program. Mathematical models of cable-
connected motor systems and deteriorating motors were
developed to examine the effects of various deterioration
conditions; but such models also have utility for electrical
system design and maintenance and, as such, are valuable
engineering tools independent of failure prediction
research.
With respect to the original program goal, results indi-
cate that the existing failure prediction algorithm does not
require modification, but the techniques used for imple-
mentation must be revised to improve its performance.
Monitoring sensitivity requirements should be thoroughly
addressed in the application of the algorithm, in order to
provide resolution adequate for low levels of deterioration.
Any factor that introduces random fluctuations into
measured values will adversely affect failure prediction
accuracy. In addition, data acquisition techniques must
provide reproducible sample points during motor opera-
tion, to ensure valid comparisons between reference and
test cases. A suitable method would trigger sampling
based on motor load, but not be influenced by system
deterioration.
Beyond these accomplishments, research into electri-
cally excited vibration in motor stators has confirmed the
feasibility of modeling the connection between internal
motor deterioration and stator vibration, and established
the value of vibration as a parameter for use in detecting
deterioration.
U.S. GOVERNMENT PRINTING OFFICE 611-012/00,036
INT.BU.OF MINES,PGH.,PA. 28846
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