the reader is directed to [14] through [16] for a detailed
description. Fig. 3 illustrates the network diagram of the
armature of the induction motor being modeled. For this
study, the motor is a 15-hp, six-pole induction machine for
which suitable design information was available for
configuring the deteriorating motor model. The deterioration
being modeled was a leakage path across eight turns in one
coil of an 18-coil-per-phase stator. Deterioration severity
was controlled by defining the resistance of the leakage path.
The magnitude of the positive sequence voltage was 440V
and the magnitude of the negative sequence voltage was
1.70V. Output of the deteriorating motor model most
relevant to this investigation are the six element currents
shown in Fig. 3.

Fig. 4 illustrates a typical example of the type of behavior
that was predicted by the motor model, and therefore, could
be expected in an application for situations in which the
negative-sequence current caused by an unbalanced supply
and the negative-sequence current caused
by the deterioration tend to cancel at low levels of
deterioration. Note that the ordinate in Fig. 4 corresponds to
decreasing resistance in the leakage path. The figure shows
very similar behavior to that observed in the experimental
effort.

CONCLUSIONS

The accuracy levels achieved in the system were
unremarkable, at nearly 1.5% with a 95% confidence interval.
The levels of deterioration simulated by shorting turns of the
stator winding were very small. In two of the motors the
worst defect resulted in less than 1% of the stator winding
being shorted; in the third motor, the worst defect shorted 3%
of the stator winding. Yet under the worst condition created,
there was no detectable change in the sound or performance
of the motors, and they were able to run for extended periods
without harm. Under these conditions, the predictor was a
reliable indicator of the presence of a turn-to-turn short in the
stator winding. Moreover, as the severity of the defect
increased, the standard deviation of the sample population
decreased. Although a predictive curve was not computed, it
is suggested that the correlation between the predictor and the
defect severity will increase for more defects. Finally, the
results of this study demonstrate the efficacy of the predictor
to identify and track’ turn-to-turn insulation deterioration in
stator windings well before such deterioration leads to a
catastrophic failure. The application of procedures to correct
for natural unbalance in a practical condition monitoring

system should further serve to enhance the utility of. this
predictor.

REFERENCES

(1) J.L. Kohler, “A Tentative Method for the Prediction of Mine Power
System Component Failures by Pattern Recognition Techniques,” M.S.
Thesis. 227p. March 1977.

(2) J.L Kohler, F.C. Trut,, and L.A. Morley, “Decision functions for

electric-power-system signals,” Electric Power Systems Research.

2:167-170, 1986.

J. Sottile, “An Experimental Analysis of Electrical Deterioration in

Three-Phase Induction Motors,” M.S. Thesis. p. 199, Dec 1986.

J, Sottile, J.L. Kobler, “Techniques for improved predictive

maintenance of industrial power systems,” [EEE Transactions on

Industry Applications. 1A-25(6):992-999, Nov/Dec 1989.

{5] G.B. Kliman, J. Stein, “Induction Motor Fault Detection Via Passive

Current Monitoring ~ A Brief Survey," Current Practices and Trends

in Mechanical Failure Prevention. H.C, Pusey, Editor, The Vibration

Institute. pp. 49-66, April 1990.

‘CEE. Santa-Cruz B,, “The Characterization of Incipient Failure Patterns

for Three-Phase Induction Motors and the Development of

Classification Methods for Predictive Maintenance Applications in the

Mining Industry,” Ph.D. Thesis. p. 245, May 1990.

(7) J.L. Kohler, J. Sottile, and F.C. Trutt, “Alternatives for assessing the
integrity of induction motors,” /EEE Transactions on Industry
Applications. 1A-28:1109-1117, SepvOct 1992.

(8) F.C. Trutt, C.E, Santa-Cruz B.,.J.L. Kohler, and J. Sottile, “Prediction

of electrical behavior in deteriorating induction machines.” IEEE

Transactions on Industry Applications, 1A-29(6): 1239-1243, Nov/Dec

1993.

J. Sottile, J.L. Kohler, “An on-line method to detect incipient failure
of turn insulation in random-wound motors,” IEEE Transactions on
Energy Conversion, EC-8(4):762-768, Dec 1993.

G.B. Kliman, W.J. Premerlani, R.A. Koeg!, D. Hoeweler, "A New
Approach to On-Line Tur Fault Detection in AC Motors,”
Conference Record 1996 IEEE Industry Applications Society Annual
Meeting, pp. 687-693, Oct. 1996.

R.W. Smeaton, Motor Application and Maintenance Handbook.
McGraw-Hill Book Co, New York. 1969.

ER. Kennedy, TJ. Fischbach. S. Ruiguang. P.M.Eller, and S.A.
Shulman, Guidelines for Air Sampling and Analytical Method
Development and Evaluation, DHHS (NIOSH) Publication No. 95-
117. p.2, May 1995,

Kennedy, Eugene R., Fischbach, Thomas J., Ruiguang Song, Eller
Peter M., and Stanley A. Shulman. Guidelines for Air Sampling and
Analytical Method Development and Evaluation, DHHS (NIOSH)
Publication No. 95-117. Technical Appendix A. May 1995.

S. Williamson, K. Mirzoian, "Analysis of cage induction motors with
stator winding faults," IEEE Trans. on Power Apparatus and Systems,
vol. 104, pp. 1838-1842, 1985.

F.C. Trutt, "Steady-state analysis of wound rotor induction machines
with simultaneous stator and/or rotor faults,” Electric Machines and
Power Systems, vol. 16, pp. 35-48, 1989,

JL. Kohler, F.C. Trutt, "Prediction of Incipient Electrical
‘Component Failures,” Final report on U.S. Bureau of Mines Contract
50338028, 370 pp., 1987. \

1)

4

(6)

91

0)

an
(12)

3)

4)

us)

[16]


4* slp (n-2) with a 95% confidence [13]. Using channel 1
as the example, with #=100, and sq/t =0.668, the resolution
will be 0.27% with a confidence of 95%. In other words, if
the mean value of the latest set of observations is at least
0.27% different than the reference mean, then the change is
statistically significant with a confidence of 95%.

The first three channels were connected to CTs and the last
three to PTs, which are inherently less noisy.

TABLE 2
. RELATIVE ACCURACY. IN PERCENT, FOR A 955 CONFIDENCE INTERVAL
Channel

12 3.4 _5_ _6
0.668 0.670 0.668 0.030 0.031 0.030

EXPERIMENT METHODOLOGY AND FINDINGS

The first series of tests was designed to determine the
relationship between small turn-to-turn defects and the
effective negative-sequence impedance predictor. The same
procedure was followed for each of the motors. First, the
instrumentation was energized and allowed to stabilize over a
30-min period. While this was in. process, the power
equipment was energized, and the load on the motor was
adjusted to the desired value for the test. The actual motor
loading is not that significant, but it is important that
sampling is done at the same motor load (speed) over time;
otherwise, unnecessary error is introduced: into the process.
Finally, the sampling parameters were set in the software, and
once initiated, a population of 30 samples was collected
automatically over a time period of approximately 40 s.
Once this test was completed, the next was begun.

Two taps were be shunted to create the desired
deterioration level, and then another population of 30 samples
were collected. This was repeated until all of the desired
deterioration levels had been simulated. The final test was a
repeat of the first one, with no taps shunted. The first and last
tests were always compared to ensure that the previous tests
had not damaged the motor or in any other way changed its
characteristics. The first and last tests were always ‘similar.
Typically, it took less than | hr to run a complete set of tests,
The complete set was repeated for a given motor several
more times, that same day, weeks, and months later. The
results were always nearly identical. This process was
repeated for each motor, and the data were analyzed as
described. .

Fig. | illustrates a typical result. Samples were taken for
the normal case, and seven levels of deterioration. The linear
regression line and the R® value of 0.76 present a reasonably
optimistic picture of the predictor’s ability to detect small
defects. Nonetheless, the first set of values representing the
lowest level of deterioration is problematic because it causes
a change in direction of the curve. This same behavior was

observed at low levels of deterioration in many, but not all, of
the tests. If this sample could be removed, for.a justifiable
cause, then the predictive curve is better and the R® value
increases to 0.95, as shown in Fig. 2.

As shown, at the very lowest levels, the addition of a turn
defect may actually improve the motor's apparent condition.
However, as the deterioration increases, a steady trend
becomes clear. The correlation coefficients for the three
motors before and after the lowest level were eliminated from
the data are shown in Table 3. The correlation is obviously
stronger when those samples from ‘the very small defects are
eliminated, This would seem to. present a problem for an
actual application in which this information would not be
known a priori. However, practically this: is of no
consequence because the most severe level of deterioration
that was created did not damage the motor; therefore. a
practical monitoring system would only need to wait until a
clear trend developed, with little risk that a failure would
occur before this happened.

TABLE 3

CORRELATION FACTORS BEFORE AND AFTER THE LOWEST LEVEL OF
DETERIORATION IS REMOVED FROM THE DATA SET

Motor Brand |
Condition OB Baldor Reliance
Before 0.84 0.33 081
Afier 093 0.76 0.90

This anomalous behavior of the effective negative-
sequence impedance ‘predictor at very low levels of
deterioration is a limitation in its application that needed to be
investigated. One explanation for this. behavior is presented
in the next section.

THEORETICAL ANALYSIS

It is known that minor construction imperfections exist in
most new motors, and most power systems have some low
level of voltage unbalance. These imperfections generally do
not affect the performance or life of the motor, although they
will create slight current unbalances- that produce a
measurable negative-sequence current. As a tum insulation
defect begins to develop, it can sometimes “cancel” the effect
of the construction, or voltage, imperfections and make the
motor currents more balanced than before the onset of
deterioration. As the level of deterioration becomes more
severe, however, the predictor should then continue on a
predictable path. ~ In order to demonstrate this observed
phenomenon, a computer model of a deteriorating motor was
used to validate this behavior.

The deteriorating motor model predicts motor currents as a
function of user-defined applied voltages, motor speed, and
type and location of stator winding deterioration. A
description of this model is beyond the scope of this paper;


120

100
z
2 80
a
3
© 6o y = -28.656x + 158.91
3 0.7627
a]
— 40
S
=z
20
C)
1 2 3 4 5
Predictor
Fig. 1. Behavior of the predictor as a function of the severity of the winding
defect, with all levels of deterioration included.
120
100
.
3 80
3
3
3 .
60
3
3 Y = -22.098x + 141.75
£ R? = 0.9513
E 40
z
20
0
oO 1 2 3 4 5

Predictor

Fig. 2. Behavior of the predictor as a function of the severity of the winding defect,
with the lowest level of deterioration excluded.’



CONDITION-BASED MAINTENANCE OF ELECTRICAL MACHINES

Jeffery L. Kohler, Ph.D., Senior Member
National Institute for Occupational Safety and Health
Pittsburgh Research Laboratory
P.O, Box 18070
Pittsburgh, PA 15236

Joseph Sottile, Ph.D., Member
University,of Kentucky
234A Mining and Mineral Resources Bldg.
Lexington, KY 40506-0107

Frederick C. Trutt, Ph.D., Fellow
University of Kentucky
453 Anderson Hall
Lexington, KY 40506-0046

Abstract-Twenty-five years ago, the former U.S. Bureau of
Mines funded a research project almed st developing the
enabling technology for incipient failure prediction In electric
Power system components as a means of reducing the injuries
and fatalities that sometimes occur when equipment
malfunctions. Over the ensuing years, interest in this has waxed
and waned, but recently interest has been growing for both
civilian and military applications. This paper addresses the
level of turn-toturn insulation deterioration that can be
resolved using an on-line monitoring technique. The detection
of turn-to-turn defects is especially important because they are
believed to represent the beginning stage of most motor winding
failures.

INTRODUCTION

The detection of incipient failures and the replacement of
the components just prior to failure would eliminate the
consequences of unexpected equipment failures. The
capability to do this has long been desired by industry;
however, it has been an illusive goal. Failure prediction of
mechanical components, particularly bearings, is an old art
form, with a history of 50 or more years. The desire to apply
predictive techniques to electrical equipment has been slower
to develop. The measuring and subsequent tracking of
insulation resistance was an early form ~-of predictive
maintenance, providing reasonably good indications of
deterioration of ground insulation systems. One of the first
instances of a continuous on-line monitoring system to detect
electrical deterioration was reported in [1]. That system and
approach was also notable because. it established a link
between improved personnel safety and the application. of on-
line monitoring for incipient failures.

Interest in this technology has waxed and waned in the two
decades since this work was started. As hardware and
software technologies advanced, it became more practical to

deploy powerful, yet inexpensive signal processing systems
to implement a predictive monitoring function. Industry
became hore interested in, developing a predictive
maintenance capability to reduce the costs associated with
downtime, as well as to avoid safety hazards associated with
unexpected and catastrophic failures. Much has been
reported in the literature (see [2] through [(9]).

More recently, the military has recognized the potential of
this technology to reduce costs and labor needed to maintain
equipment. In recent years, the term condition -based
maintenance (CBM) has been used to define the practice of
effecting maintenance actions, on monitored
information, One weakness shared by all CBM systems is an
inability to predict the remaining useful life of a failing
component with any degree of accuracy. This is an area of
increasing research, however.

This paper addresses a part of the original failure
Prediction technique described in [1] and focuses on the
ability of one predictor, the effective negative-sequence
impedance predictor, defined as the negative-sequence
voltage divided by the negative-sequence current, to detect
meaningfully.small levels of deterioration.

STUDY OBJECTIVES

Catastrophic electrical failure of motors is usually the
result of a line-to-line or line-to-ground fault; afterwards,
there is little evidence remaining to determine the origination
and cause of the failure, While motor insulation may fail
directly in a line-to-ground mode or even a phase-to-phase
mode, the field and laboratory experience’ of manufacturers,
plant operators, and researchers point to. the tum-to-tum


0.20

0.00

fa in

Va

Fig. 3. Network diagraim of the deteriorating motor model stator.

oe. :

0.0

1.0 2.0 ot) 4.0 5.0
Effective Negative Sequence impedance, Ohms,

Figure 4.. Effective negative-sequence impedance versus fault severity,


defect as the source of deterioration that ultimately evolves
into a catastrophic failure. These turn-to-turn defects result
from the cumulative damage from the dielectric stresses of
overvoltages and other adverse conditions. Over some period
of time, these defects can increase in size, i.e., fault energy
level, until a catastrophic fault involving another phase or
ground occurs. Thus, it would be advantageous to detect this
early stage of deterioration to allow for a timely prediction of
ent failure.

The purpose of this study has been to investigate the
performance of the effective negative-sequence impedance
predictor for the low levels of turn-to-turn deterioration that
may exist well before failure is imminent. A complicating
factor in this analysis is the small unbalance in either voltage
and/or machine structure that should exist in any real system.
While the effects of this natural unbalance are known to be
problematic, the current study was focused upon obtaining a
better understanding of the physical behavior of the effective
negative-sequence impedance predictor in the presence of
low level turn faults. In order to see these relationships most
clearly, no attempt was made to apply technologies that might
be used to correct for the natural unbalance, such as those
presented in [10].

EXPERIMENTAL APPARATUS.

The experimental apparatus was developed to allow the
study of electric motors under varying conditions of load and
deterioration. This apparatus can be divided into two groups
for the purposes of this description: the power equipment and
the instrumentation.

Power Equipment

The power equipment facility was constructed to provide a
platform for testing a three-phase motor under different load
conditions and consisted of a three-phase transformer, the
three-phase motor under test, a de generator, a lamp bank,
and protective devices, e.g., fuses and guards. A three-phase
transformer was used to convert the three-phase voltage in
the laboratory to the required nameplate voltage for the
motor. The motor and generator were rail-mounted on a test
fixture that allowed the motor to be changed easily. Belts
were used to connect the motors to the generator, and sheave
sizes were selected to achieve the desired generator speed for
a given motor. The load on the generator, and. therefore on
the motor, was adjusted by switching lamps on a load bank.
It was possible to vary the load on the motors up to their rated
full load. Three single-phase variacs were used to unbalance
the supply voltage for the studies in which a voltage
unbalance was desired.

A variety of low-voltage squirrel-cage induction motors of
different designs have been used in this research. Three
motors were used for the work reported here: one

manufactured by General Electric, one by Reliance, and one
by Baldor'. All were S-hp, 460-V, four-pole motors. This
size was chosen because there is evidence that they would be
representative of motors as large as several hundred
horsepower [11]. Different brands were included to eliminate
the possibility of a bias from the design of one manufacturer.
This mix of brands was based on their availability at a local
supply house. However, based on previous work, similar
results would be expected with a different mix of motors.

Deterioration of the turn-to-turn insulation was simulated
by placing taps on the wires of the stator winding and then
shorting these taps. This process occurred as follows.

First, the motor was disassembled and the rotor removed to
expose the stator windings. Five wires, located adjacent to
each other, were chosen and the insulation at a point on each
was carefully removed. A 36-in, 18-awg, stranded and
insulated wire was attached to the stator by braiding the
strands of the tap wire around the stator wire; it was then
soldered. After all of the connections had been completed.
electrical coating varnish was applied and allowed to dry.
Two additional coats were applied, and an insulation
resistance test was performed to verify that the resistance to
ground was greater than 1 MQ. The wires were placed back
into their original form and lashed to the coil from which they
were separated at the beginning of the procedure. The motor
was then reassembled.

The five tap wires were fastened onto posts on an
insulating board. The motor was then energized, and the
open-circuit voltage between each combination of the five
wires was recorded. This measured open-circuit voltage
between the taps, divided by the line-to-neutral value of the
measured voltage applied to the motor, is a measure of the
fraction of the phase winding that is involved. The defects
that could be simulated by shunting different tap wires. for
each motor are shown in Table 1. The placement of the taps
to achieve a specified level of deterioration is nearly
impossible, although over all of the combinations it was
possible to realize a useful range. In retrospect, it would have
been useful to simulate larger defects in all of the motors.

TABLE |
‘SIMULATED DETERIORATION OF THE STATOR WINDING IN THE TEST MOTOR

0.23 0.46 0.68 0.91

Baldor
Reliance 0.14 0,20 0.41 0.61 0.82
GE 0.44 0.88 131 1.74 218 261 3.05

Each motor was tested prior to disassembly, and the three-
phase voltage and current phasors at different loads were
recorded to establish the “normal” motor baseline. . These

‘ Mention of any company name or product does not constitute endorsement
by the National Institute for Occupational Safety and Health.


tests were repeated with the modified motors with all taps
open to verify that the modifications had not changed the
motors’ “normal” operating characteristics.

Instrumentation

An instrumentation system was developed to sample each
of the three-phase voltage and current waveforms and to
process and store the data. Absolute accuracy of the system
is less important than precision because the value of the
predictor at time t, as compared to time t-6, is more
significant than the true value of the predictor. In other
words, repeatability was more important than absolute
accuracy. Nonetheless, an effort was made to acquire the
Most accurate components that were commercially available
and to identify and reduce variability in the instrumentation
and overall data collection process. After the system was
assembled, extensive studies were conducted to understand
the nature of system variability, the results of this are
summarized later in this paper.

Active potential transformers (PTs) and active current
transformers (CTs) were used to sense each of the three-
phase voltage and current waveforms, respectively. The
stated accuracy of these sensors exceeded 0.1% in this
application. An eight-channel data-acquisition card, with
eight sample and hold circuits, had a stated accuracy of 2%.
A personal computer with a 486/166 processor was used to
run the data acquisition software, which was a custom -
developed Windows-based program. This program controlled
the sampling on the data acquisition card and the display and
storage of the sampled data. The channels to be sampled,
sampling parameters, and the number of samples to be
collected could be specified, as could the filenames for data
storage. After the experiment was completed, the raw data
files were imported into MATLAB for analysis.

First, a discrete Fourier transform (DFT) was performed,
and the 60-Hz component was extracted for each voltage or
current channel. A data file (matrix) was constructed in which
each row contained an identifier number and each of the
three-phase voltage and current phasors. Other variables
such as motor speed were also recorded and appended to the
end of the row. Depending on the nature of the experiment,
as many as several hundred samples may have been taken; as
a result, those files would have several hundred rows. Next,
the mean and standard deviation were computed for the
60-Hz phasors of the voltages and current for each
experiment. For convenience, the mean values were then
placed as one row of a new matrix that contained all of the
experiments. This row was appended with other information,
such as the experiment identifier number, motor number, and
fault parameters. A similar matrix was created for the
standard deviations. The mean values: in one row,
representing the three-phase voltage and current phasors were
used to compute features such as complex power, impedance,

and the symmetrical components. Additional analyses were
then performed using MaTLaB, MathCad, and other standard
packages to investigate pattern recognition issues of the
collected data. For the work presented in this paper, the most
important subsequent calculation was the effective negative
sequence impedance predictor. Over the course of the
experimental study, dozens. of experiments were conducted,
with several trials of each. Typically, sample sizes of the
computed phasors were 30, with some as large’as several
hundred, Many of the experiments were well behaved: the
mean achieved with a five-point sample was similar to that
obtained with a 30-point sample,

A series of tests was conducted to verify the correctness of
the signal processing algorithms and to establish reasonable
sampling parameters for the DFT. Once these were
completed, performance tests focused on accuracy and
Precision questions. Initially, the only input to the data
acquisition board was a signal generator into one channel.
Then the same signal was fed simultaneously into all eight
channels, and the results were compared. for agreement.
Next, the CTs and PTs were brought online, but all three
were placed on one phase only. Again, results were
compared for agreement. Afterward, the sensors were placed
at the proper locations, and more samples were collected and
studied. The results of these studies helped to define the
optimum sample criteria (i.e., sample rates, number of
samples, and etc.), to compute scale factors and to determine
the resolution of the system.

The resolution is defined as the smallest difference
between the means of two sets of measurements, which
represents a statistically significant difference with a
specified confidence level. In this application, one set of
measurements with its corresponding mean would represent
the “normal” or undeteriorated state for the motor and would
be considered the reference value. The other set of
measurements with its mean would be taken periodically and
the mean compared to the reference value to determine if
deterioration were underway. Of course, whether or not
deterioration is underway depends on whether the difference
between the two means is statistically significant. This was
addressed by computing precision.

A Statistical definition for the precision was obtained from
the foregoing test data as follows. The computed mean for
100 samples was taken as the population (true mean). The
Precision of the system (S) was computed: as the sample
standard deviation, sq, divided by the mean, 1 [12]. These
values are shown in Table 2 for each of the current (1-3) and
voltage (4-6) channels. The precision is an indicator of the
maximum achievable system resolution—albeit dependent on
the number of sample points. If two sets of data, of n/2
measurements each, are taken and. their means computed,
then it can be shown that the smallest difference between the
means that can be resolved is approximately