PRELIMINARY RESULTS

The first test motor run to failure in this work did
not yield data from which signs of impending motor failure
could be identified. It did, however, provide valuable
information for refinement of experimental equipment
and procedures. Several changes resulting from this test
were the replacement of magnetic tape recording with
computer-based digital data acquisition, improvements in
organization and storage of motor and temperature data,
addition of a high common mode voltage (surge) rejection
circuit to the temperature data logger, revised bearing
lubrication practices, and improvement in motor to alter-
nator coupling.

Of particular interest was determination of the tem-
perature gradient from heat chamber ambient to motor
housing and bearings and, finally, to the motor windings.
This information guided selection of cable insulation
ratings, temperature ratings of other materials used in the
heat chamber, and maximum rating of the grease used in
the motor bearings. The temperature gradient observed
is shown in table 5.

Table 5.—Temperature gradient observed

Monitoring point Approximate

temperature, *C

Motor winding (in slot, near end) 215
Motor case (inside surface) 175-180
Bearing, load end (outer rac 175
Bearing, 170
Heat chamber ambient (center p) 145
‘Alternator bearing (end cap surface) ... 60

A second test motor was run at a winding temperature
of 215° C; it failed after approximately 900 h of operation,
reasonably approximating the life predicted by the afore-
mentioned 10° C rule. The ALN analysis of associated
motor terminal data, however, did not provide early warn-
ing of the failure. Conventional IR measurements, con-
ducted 15 h prior to failure, also gave no indication of the
impending insulation breakdown.

Measurements made after failure indicated low resist-
ance paths between all phase windings, and between all
windings and ground, The motor was then sent to Re-
liance Electric for analysis to determine the mode and
cause of failure. The following is a summary of their
findings.

¢ Time to failure correlated closely with Reliance
Electric projections based on their motor insulation mate-
rial tests.

© Many materials present in the motor were dis-
colored and/or embrittled by the high test temperatures.

¢ Failure occurred in the first coil downline from a
motor terminal, approximately 1 inch out from the edge of
the stator core.

¢ Failure began as a turn-to-turn short within the coil,
progressed to phase-to-phase with an adjacent coil, and
finally failed to ground (this progression was not a result
of high current during motor starting, since no attempt
was made to restart the motor after the initial failure).

¢ Immediate failure area showed signs of significant
burning,

¢ Insulation on winding wire had longitudinal and
circumferential cracking.

© Failure was caused by embrittlement and cracking of
winding wire insulation, as a result of high operating tem-
perature and normal operating vibration.

Ina discussion of these analysis results with a Reliance
Electric engineer, possible changes to accelerated life test-
ing procedures were suggested.

The failure of IR measurements to detect the break-
down (cracking) of insulation may be due to extremely dry
conditions inside the motor as a result of the high oper-
ating temperatures. Higher humidity inside the motor
during IR tests would likely reveal a progressive drop in
insulation integrity prior to complete failure. Although it
would be difficult to control humidity and condensation
conditions inside a motor during operation, increasing the
moisture present may also aid in detection of time-
dependent deterioration using ALN’s; this possibility will
be investigated further.

The location of the failure, on the first coil downline
from a motor terminal, suggests that a fast-rise-time volt-
age transient may have triggered the failure. Monitoring
of the laboratory three-phase power system confirmed
the presence of voltage transients as high as 670 V with
durations up to 100 ys. In order to avoid catastrophic
failure of weakened insulation systems in the future, a
surge suppression device will be installed to protect the
test motors.

It is also possible that a winding temperature of 215° C
is too severe to allow accelerated yet gradual deterioration
of motor insulation, or that the sudden and complete
failure of this motor was simply an anomaly,

At this writing, a third and fourth motor are under test,
both at winding temperatures of 205° C.


DETERIORATION CURRENT AND ALN INPUT PARAMETERS, pu

0.35

30

0.35

30

00

KEY
Negative sequence current angle, 10° deg
Simulated deterioration current
* Calculated deterioration current
Zero sequence current magnitude
Negative sequence current magnitude
Negative sequence current X-component

T
-_—-

|

1
0.10 0.20
SIMULATED DETERIORATION CURRENT, pu

0.30

Figure 6.—Relationship of ALN input parameters to calculated deterioration current,
for simulated A-b (top) and A-G (bottom) leakage on 150-hp motor running unloaded.



TABLES—Continued

Page

4, Root mean squares of residuals between actual and ALN-calculated deterioration values, for ALN pair
created using combined 50-, 100-, and 150-hp data . 8
5, Temperature gradient observed . 14

UNIT OF MEASURE ABBREVIATIONS USED IN THIS REPORT

A ampere mA milliampere
°C degree Celsius BS microsecond
deg degree pu __per unit
h hour v volt
hp horsepower WwW watt
kW kilowatt

DISCLAIMER

Reference to specific products does not imply endorsement by the U.S. Bureau of Mines,


CONCLUSIONS

‘The first stage in research to develop a practical motor
condition monitoring system has been completed. ALN’s
developed under this work can detect the presence and
severity of simulated insulation leakage independent of
leakage position and level, motor load, and motor temper-
ature. A single pair of ALN’s can yield accurate results
for motors of varying size, specifically 50-, 100-, and
150-hp motors.

The feasibility of ALN-based failure detection relies
upon verification of the relationship between gradual

insulation breakdown and ultimate complete motor failure.
The second stage of this research has been initiated to
establish and quantify this relationship through accelerated
life testing of motors. Laboratory experimentation induces
insulation failure by accelerated aging, while monitoring
terminal information for early signs of impending failure.
This work has not yet yielded conclusive results.

REFERENCES

1. Morley, L. A. and J. L. Kohler. Final Report—Evaluation of
Coal-Mine Electrical-System Safety (grant G0155003, PA State Univ).
BuMines OFR 160-81, 1981, 258 pp; NTIS PB 82-139338.

2. Kohler, J. L. A Decision-Theoretic Method for the Classification
of Incipient-Failure Patterns Which Are Characteristic of Deteriorating
Mine Power-System Components. Ph.D. Thesis, PA State Univ., Uni-
versity Park, PA, 1983, 141 pp.

3. Kohler, J. L., and F. C. Trutt. Prediction of Incipient Electrical-
Component Failures (contract J0338028, PA State Univ.). BuMines
OFR 35-88, 1987, 370 pp; NTIS PB 88-213590,

4. General Research Corp. (McLean, VA). PNEITR-4X User's
Guide and Technical Reference. Aug. 1986, 74 pp.

5. Motley, L. A. Mine Power Systems. BuMines IC 9258, 1990,
437 pp.

6. Institute of Electrical and Electronic Engineers. IEEE Standard
‘Test Procedure for Evaluation of Systems of Insulating Materials for
Random-Wound A C Electric Machinery. IEEE Std. 117-1974, 1974,
24 pp.

7. General Electric Technical Services Co. (Plainville, CT). Motor
‘Temperature Ratings and Construction Tech. Pam. E-19, 1974, 5 pp.


1.03196

-0.05990
0.01962
0.01665
0.26385
0.00974
0.02165
0.11437
0.16743
0.86996
0.32337

ecagecatads

one een neo

0.15622
0.00446
-0.03063
0.09912
0.22836
-0.01579
0.12995
0.00116
0.99587
0.02508
0.02515


3s KEY

ee wee ee A-B deterioration power

= we A-B deterioration current

ow 020 T r T T

raed

WwW 4
ro

w 2 6 penn VY YOO 4
ac

Ww q 4
Sw

2 i 12 L 1 L 1

S5 35 45 55 65 75 85
3S ° MOTOR WINDING TEMPERATURE, °C

Figure 12.—Effect of motor temperature change on ALN-calculated deterioration, for simulated A-B leakage on

100-hp motor running at full load,

ACCELERATED LIFE MOTOR TESTING

GENERAL

Failure detection techniques have been developed using
simulated deterioration conditions. This is based on the
assumption that in actual motor failure, insulation deg-
radation is gradual and of sufficient magnitude to allow
early detection,

To validate this assumption, ongoing work includes
accelerated life testing of motors. These tests attempt to
induce gradual insulation failure in laboratory motors to
confirm the presence of detectable insulation breakdown.
prior to motor failure. In these tests, typical mine equip-
ment motors are operated at above-normal temperatures
to accelerate winding insulation degradation. Motor elec-
trical parameters are recorded at regular intervals during
this deterioration process, and deterioration current and
power levels are calculated using ALN’s. The results are
analyzed to identify unique signatures present prior to
complete motor failure. Carefully controlled acceleration
of motor aging will permit the completion of this work in
a practical time frame, while allowing extrapolation of
results to motor life under normal operating conditions.

EXPERIMENTAL DESIGN

IEEE Standard 117-1974 (6) specifies procedures for
accelerated aging of motor insulation. This document
prescribes electrical, mechanical, and thermal stress for the
rapid degradation of insulation in both complete motors
and motorettes. The standard, however, is primarily con-
cerned with the determination of time to end of life for

motor insulation, while the objective of this research is to
induce gradual failure. Therefore, in this testing, only
thermal stress is imposed upon motor insulation systems.
This does not imply that thermal conditions are more sig-
nificant than others, but was done to allow better control
over the accelerated aging rate, as well as to avoid the
catastrophic failures that can occur as test specimens are
subjected to cycles of thermal, mechanical, and electrical
abuse. Since the purpose of these tests and this approach
to motor insulation aging are unique, little information for
experimental design and procedures is available in existing
standards. As a result, many aspects of the necessary lab-
oratory work require refinement as testing progresses.

Test motors are operated at full load while exposed
to elevated ambient temperatures, with expected motor
insulation life reduced by half for each 10° C rise above
maximum rated operating temperature. Using a normal
life expectancy of 40,000 h and class F insulation rated
at 155° C maximum, estimated motor insulation lives at
various temperatures are listed below (7).

Life, h

Temperature, °C

165
175
185
195
205
215
225



The extensive laboratory data collection necessary for
empirical modeling requires computer hardware and soft-
ware that enable researchers to sample instrumentation
signals, process the signals to obtain desired electrical
values, sort and select specific information, and create data
files for input to PNETTR. Early work in this program
employed custom written software on a VAX computer
cluster to meet these requirements. This system was later

supplemented by a desk-top-computer-based system that
streamlined the data-collection process and significantly
reduced the time necessary to model each motor.

Figure 3 shows the overall layout of the laboratory
power system equipment and data-acquisition hardware.
Figure 4 shows a motor-alternator set equipped with a
150-hp motor used for data generation,

DEVELOPMENT OF MOTOR FAILURE DETECTION ALN’s

GENERAL

Prior to data collection and empirical modeling of
electric motor operation, a number of basic decisions must
be made regarding the types of motors involved, power
system conditions, specific modeling techniques, and the
overall approach to monitoring. It is, therefore, necessary
to describe the major assumptions and options selected
that have focused efforts for this research, as well as the
logic behind the selections.

The components targeted are three-phase squirrel cage
induction motors, which are the most widely used electric
machines in mining operations (5). Consequently, deteri-
oration of brushes, slip rings, commutators, and similar
components absent in this type of motor was not consid-
ered. Additionally, mechanical deterioration such as bear-
ing wear and stator or rotor structural failure was not
included in this research.

Electrical failure often originates with the breakdown
of insulation and subsequent leakage of current through
an abnormal path, eventually resulting in a fault (1). Insu-
lation breakdown may be due to any of numerous dete-
rioration mechanisms arising from electrical, thermal,
mechanical, or environmental stresses. The development
of leakage current paths, however, is the common result of
all these processes, and is the effect that directly leads to
electrical failure of motors.

The process of insulation breakdown is used to quantify
progressive deterioration of induction motors. Deteriora-
tion severity is indicated by the level of current flowing
and power dissipated in an abnormal path. Such leakage
current can seldom be precisely located or directly meas-
ured in the field, but it is readily controlled and measured
in laboratory simulations. Therefore, laboratory simula-
tions were used in conjunction with PNETTR to establish
empirical relationships (ALN’s) between deterioration
current and power levels and readily measured electrical
values.

The laboratory hardware and software described pre-
viously were used to generate data for the modeling proc-
ess. Information such as voltage and current phasors for

each phase and deterioration characteristics was sampled
during steady-state motor operation, that is, a condition
under which terminal information is sufficiently stable to
allow reliable ALN analysis (further defined later in this
report). All electrical parameters for each sample were
derived from the voltage and current phasors, and were
candidate input variables to the modeling process, while
simulated deterioration levels were dependent variables for
the sample.

Data collection and modeling produced ALN’s, which
are continuous functions relating electrical parameters to
deterioration levels. This modeling approach was selected
because deterioration current and power levels have a
mathematical relationship to specific electrical features.
In addition, it does not require selection of a cutoff dete-
rioration level to define a good or bad condition. Further,
it provides trend information prior to failure, which will
be essential in using motor-condition monitoring systems
to accurately anticipate failures.

The ALN’s created with PNETTR should ideally deter-
mine deterioration presence and level independent of as
many variable power system factors as possible, such as
leakage level and location, motor load, winding temper-
ature, and bus voltage imbalance. ALN’s should also be
applicable to systems with motors different from those on
which the training data were generated, within reasonable
size and design limitations, This ALN "generality" is an
important concept for actual application of motor moni-
toring, in that a specific motor would not require extensive
laboratory testing to be successfully monitored in the field.
Further, ALN’s would be most useful if no "new condition"
baseline were needed for a motor to be monitored.

The following sections detail the procedures used for
empirical modeling with PNETTR, results of this mod-
cling, and a summary of important ALN characteristics.

EMPIRICAL MOTOR DATA

Independent variables for modeling include meas-
ured three-phase voltage and current values as well as
the calculated values of system impedance; symmetrical


INTRODUCTION

BACKGROUND

Previous U.S. Bureau of Mines research investigated
the benefits of using continuous monitoring techniques
to evaluate the condition of power system components.
Initial studies considered components such as ac and de
motors, power cables, transformers, rectifiers, and ca-
pacitor banks, and provided two important findings. Nu-
merous electrical-system-related accidents and significant
equipment downtime result from electrical component
failure, and time-dependent deterioration of the dielectric
properties of insulation accounts for the majority of such
failures. Therefore, it was suggested that early detection
of deterioration in mine electrical system components
could significantly improve system safety and reduce un-
scheduled equipment downtime. Additional results of this
research include the proposal of a methodology for early
deterioration detection, development of a computer-based
system to detect and analyze the electrical signatures of
component deterioration, identification of numerous char-
acteristics for electrical system deterioration, and con-
firmation of these characteristics by laboratory and field
data collection (1)?

In an independent research program, this work was
continued; the relationships between electric motor dete-
tioration mechanisms and their resulting electrical sig-
natures were analyzed, and a classification algorithm for
insulation deterioration on motor systems was developed
(2). Recent Bureau-funded efforts in this area addressed
the refinement of the above algorithm, produced several
computer models to simulate power system and induction
motor operation under deteriorated conditions, and iden-
tified deterioration characteristics of induction motor
windings (3).

The research programs just described produced infor-
mation that can be used to form a theoretical basis for
the detection of low-level insulation deterioration. Addi-
tionally, the responses of numerous electrical parameters
to various levels and types of cable and motor deteriora-
tion were documented. Upon completion of these various
programs, however, this information had not yet been suc-
cessfully implemented in a practical deterioration detection
system. Although some generalizations were made regard-
ing specific parameters, and a deterioration classification

“Italic numbers in parentheses refer to items in the list of references
preceding the appendix.

algorithm was developed for a limited number of cable-
connected motor system conditions, a method to detect the
state of electrical deterioration was not yet available. This
situation prompted the initiation of in-house research at
the Bureau to develop such a system for typical mine
equipment motors. The work was done in support of two
Bureau goals: to provide a safe work environment for
miners and to reduce the cost of coal production.

ADAPTIVE LEARNING NETWORK? APPROACH
TO FAILURE DETECTION

Information available from past research suggests that
mathematical relationships using measurable parameters
can be used to identify electrical insulation deterioration.
However, the identification of appropriate input infor-
mation and derivation of suitable relationships is a com-
plex task. Subject Bureau in-house research utilized an
empirical data-modeling approach to this problem, em-
ploying commercially available data-modeling software and
laboratory-generated power system information to create
the necessary relationship(s).

PNETTR-4X‘ (Polynomial NETwork TRainer version
4X), a modeling software package, forms polynomial net-
works called adaptive learning networks (ALN’s) from
training data (4). Input data are typically multiple sets of
measured or calculated quantities that represent a physical
process to be modeled. For this research, such data are
electrical parameters extracted from laboratory motors
undergoing simulated insulation deterioration. PNETTR
selected those parameters most significant for modeling
motor operation and created ALN’s relating the param-
eters to dependent variables, in this case, insulation
deterioration severity. Figure 1 illustrates the use of this
methodology to create the basis for an insulation deterio-
ration detection system.

This report describes the results of the first stage of
research to develop a practical on-line motor monitoring
system for carly detection of impending failure. The re-
port includes PNETTR software use, laboratory data col-
lection, development of ALN’s for simulated insulation
leakage detection, and initiation of a second stage, vali-
dation of ALN’s using accelerated life testing,

"Adaptive learning network" and "ALN" are registered trademarks
of the General Research Corp., McLean, VA.

‘*PNETTR-4X" is a registered trademark of General Research Corp.,
McLean, VA.


EARLY DETECTION OF INSULATION FAILURE IN ELECTRIC MOTORS

By Gerald T. Homce!

ABSTRACT

A system capable of monitoring mine electrical power systems and detecting impending component
failure could significantly improve power system safety and reduce unscheduled equipment downtime.
Such monitoring would require a method of evaluating electrical parameters, calculated from terminal
values, for indications of component deterioration. The U.S. Bureau of Mines has targeted electrical
failure of squirrel cage induction motors and examined the use of mathematical models to aid in this
evaluation. The initial stage of this work is complete, and has produced polynomial networks called
adaptive learning networks (ALN’s) that can detect and quantify winding insulation leakage simulated
on laboratory motors. In this modeling process, empirical data from laboratory motors were used to
select the electrical parameters most significant for assessing motor conditions, and mathematical
expressions relating these parameters to simulated deterioration were formed. ALN’s developed thus
far can process readily measured motor terminal information and quantify power and current levels of
laboratory-simulated leakage to within 1% and 3% of full load values, respectively, for motors in the
10- to 150-hp size range. The next stage of this research is validation of these ALN’s for actual motor
failures; this stage is currently underway, using accelerated life testing of laboratory motors.

*Blectrical engineer, Pittsburgh Research Center, U.S. Bureau of Mines, Pittsburgh, PA.


Motors used are Reliance Electric 20-hp, design B,
squirrel cage induction machines, with Mine Safety and
Health Administration (MSHA)-approved explosion-proof
enclosures. A metal chamber equipped with four 750-W
heating clements is used to elevate motor ambient tem-
perature, thereby raising internal insulation temperatures.
Figure 13 shows the motor-alternator set with the heat
chamber open. Thermocouples and a programmable data
logger are used to monitor temperatures in and around
test motors and to provide a control loop for the heating
elements. Thermocouple placements include chamber am-
bient, windings (near end of stator slots), and the outer
race of cach bearing. Instrumentation provides for moni-
toring of line-to-line voltages and line currents at motor
terminals. These signals are collected, processed, and
stored using a desk-top-computer data-acquisition system.
This system, based on a distributed processing network,
uses remote monitoring stations to sample, digitize, and
process motor terminal values, and then transmit voltage
and current phasors back to a central computer for stor-
age. Subsequent analysis uses the most appropriate avail-
able ALN. Figure 14 is an illustration of the complete lay-
out for this testing. Motor insulation condition is also
evaluated using insulation resistance (IR) measurements at
regular intervals during testing, and the results of this
conventional technique are compared with those of ALN
processing.

Variable

3- phase
transformer

power source

Motor power
instrumentation

Figure 13.—Motor-alternator set for accelerated aging of mo-
tor insulation, shown with heat chamber open. (Photo cred
R. Wayne Denlinger, engineering technician, Pittsburgh Research
Center.)

Motor

heat chamber Alternator

||)

Data-acquisition Temperature

computer system data storage
and analysis

Thermocouples

BRAAAA Aa
pV | Heaters
Heater a
control
— aa 2)
Temperature +6)
data logger
240-V @)
input Alternator
load bank

Figure 14.—Layout of laboratory equipment for motor insulation accelerated life testing.


x Exp( ) ne
Xe
X3
Xq 2 ( %s cm
Xs j
7
Xi )4 3 6 >‘,
a
Figure 2—Possible element connections for ALN. Each element (numbered block) Is a polynomial, with
up to three input variables. Blocks with parentheses are intermediate operations performed on individual in-
put varlables.

The use of empirical information for modeling motor
deterioration necessitates laboratory data collection under
well-controlled conditions. Laboratory facilities required
for such data collection include an appropriate power
system, test motors, instrumentation to monitor the sys-
tem, and some method to collect and organize the desired
information.

Collection of motor data for this research was conduct-
ed in the Bureau’s Mine Electrical Laboratory. Motor-
alternator sets powered by a motor test station and loaded
by a bank of resistors were used to generate the required

3-phase Motor-alternator set
power source
A
8 —
c vr
TN
Signal ~
conditioning
D
~w
~w
Ww
lation
Data- acquisition Pe evel Alternator
computer system terioratio load

Figure 3.—Layout of Mine Electrical Laboratory power system
equipment and data-acquisition hardware used for motor data

DATA COLLECTION

power system terminal information. A bank of resistors
was connected to the motor leads through a variable
transformer to simulate current leakage (insulation dete-
rioration) across various points on the motor system.
Window-type current transformers and isolation potential
transformers measured clectrical signals from the system

terminals and supplied this analog information to a data-

Figure 4.—Motor-alternator set (150 hp, 150 kW) used for
motor data generation. (Photo credit: R. Wayne Denlinger,
engineering technician, Pittsburgh Research Center.)


RI} 9450 REPORT OF INVESTIGATIONS/1993

Early Detection of Insulation Failure
in Electric Motors

By Gerald T. Homce

UNITED STATES DEPARTMENT OF THE INTERIOR

BUREAU OF MINES


Motor -
alternator
set

“:

Deterioration
A simulator

Figure 5.—Deterloration simulation for laboratory data col-
lection,

Simulations included current levels of 0.00 to 0.10 per
unit (pu)$ at 0.01-pu increments, as well as 0.15, 0.20, and
0.25 pu. Such levels represent 122 mA to 3.05 A on a
10-hp 460-V motor, and 1.73 to 43.25 A on a 150-hp 460-V
motor.

The lower limit of these leakage simulations was ulti-
mately dictated by practical lower limits of resolution for
the modeling techniques and data-collection hardware.

The upper limit was selected based on the lower trip
level commonly found on motor circuit protection devices.
The actual leakage current and power levels possible in a
motor prior to a complete failure are not yet known.
Undocumented reports, however, suggest that motors have
remained in service with electrical imbalances equivalent
to as much as 0.25 pu leakage current.

These simulations concentrate data at lower values,
since such values will likely be more important when de-
tecting early stages of insulation breakdown. All combina-
tions of deterioration path and level were simulated at
each of three different motor load levels, resulting in 822
distinct system conditions represented in the modeling of
each motor. For each of these system conditions, up to
four separate samples were taken, yielding data bases

Per unit (pu) is a method of quantifying electrical values wherein all
values are expressed in relation to preselected base quantities defined as
1.0 pu. In this specific case, bases are motor full load values of line to
neutral voltage, line current, and complex power. As an example, a line
current of 60 A on a motor with a full load current of 120 A would be
a line current of 0.50 pu. Note that per unit is a dimensionless quantity.

containing more than 3,300 observations for a single
motor. The use of several samples for each condition
creates a more realistic data base by introducing random
variations that are normally present in motor terminal
measurements. Typically, one or two of the observations
for each condition were used in training an ALN, and one
was reserved for evaluating the ALN. These were chosen
arbitrarily as the first or first and second from those
available, depending on the weight desired for the specific
condition,

PNETTR modeling of the empirical data just described
employed various user controls to optimize the resulting
ALN’s. These controls included elimination of inappro-
priate independent variables (such as variables that were
phase-dependent, or unstable, or to which a model was too
sensitive), limiting the number of ALN layers (to avoid
overly complex or sensitive models), and varying model
accuracy criteria.

The predicted squared error, a value used by PNETTR
to quantify model accuracy, was used as a relative in-
dication of model quality among different versions of the
same ALN, while the root mean square (rms) error for an
evaluation data set indicated accuracy in determining
deterioration level. The latter is the rms of residuals
between the actual and model-calculated deterioration
levels for a set of observations not used in training the
ALN. ALN evaluations were carried out using data for all
deterioration levels, and with data representing only 0.00-
to 0.10-pu levels.

In addition to collecting more data at lower deterio-
ration levels as previously described, observations for
deterioration levels of 0.00 to 0.10 pu were individually
given twice the weight of those for 0.15-, 0.20-, and 0.25-pu
levels (by including twice as many observations) to en-
hance model accuracy at the lower levels.

ALN development ultimately focused on 10-, 50-, 100-,
and 150-hp motors. Motors used for data generation were
Reliance Electric open construction design B machines
operating at 460 V, three-phase, with full load currents of
12.2, 62, 120, and 173 A, respectively. PNETTR modeling
selected the most appropriate electrical parameters from
their respective data bases, and created two optimized
ALN’s for each motor, one calculating simulated leakage
current and the other calculating power dissipated in the
leakage path. Table 3 shows the accuracies (rms errors)
for each pair of ALN’s, using evaluation data as indicated.

For further modeling, data from the 50-, 100-, and
150-hp motors were combined and a single pair of ALN’s
was created, applicable to this range of motor sizes. Ta-
ble 4 shows the accuracies (rms errors) for the ALN pair
created using the combined data just described. Evalua-
tion results are for 0.00 to 0.10 pu leakage, and are listed
for each motor individually.


components for voltage, current, and impedance; system
power; and power factor. For each of the complex values
used, four separate quantities exist in the data file:
rectangular x component, rectangular y component, polar
magnitude, and polar angle. This broadens the number of
candidate input variables from which PNETTR may
choose and reduces the chance of missing subtle pa-
rameter variations that may prove valuable for modeling.
Simulated leakage current levels were also measured for
each sample. Along with calculated deterioration power
values, they became the dependent variables in PNETTR
data files, Additional information recorded for each
sample includes motor stator temperature and the mag-
nitude of power output by the alternator, which directly
represents motor load level. Each sample taken repre-
sents one of many different conditions simulated for each
motor, and includes all of the above described information.
Tables 1 and 2 show an example of all information for one
sample of motor data, as used in PNETTR modeling.

Table 1.—Example of test condition information’

Parameter Value

Observation identification ..........., Time and date or se-
quential numbering.

Voltage at leakage path 1.732.

Current at leakage path 0.100.
Power at leakage path . 0.173.
Motor load . +P 0.8539.
Deterioration path grounding status . No ground
connection.
Deterioration path grounding code 1.
Deterioration path identification . At

Deterioration path code 1

‘This list and the one in table 2 will serve as one of many
observations that make up input data for PNETTR modeling.

FAILURE DETECTION ALN’s

Initial motor modeling under this program focused on
the effects of individual specific system conditions on ALN
accuracy. Typically, only one parameter, such as deteri-
oration type, location, and severity or motor loading, was
varied for each investigation. In subsequent testing, a wide
range of motor system conditions were used as input to
the modeling process, including the use of a single model
to evaluate several different motors. This preliminary
work established the feasibility of modeling simulated
insulation leakage on three-phase squirrel cage induction
motors using PNETTR, and formed the basis for further

development and refinement of ALN’s targeting motors
ranging from 10 to 150 hp.

‘Table 2.Electrical parameters!

Polar Polar Rectan- Rectan-

Parameters” magni- angle, gular gular

tude, pu_deg _x,pu__y, pu

Voltage A-n 0998 0.0 0.998 0.000
Voltage B-n 996 239.9 -500  -.862
Voltage C-n 995 1209 -511 854
Current A. 1.038 337.7 960-393
Current B . 1.077 2117-916 -.566
Current C . . 950 91.5 -.025 949
Impedance A . 962 223° 800364
Impedance B . 925 282 816 437
Impedance C . sees 10047 294918514.
Positive sequence voltage .. 996 3 996 005
Negative sequence voltage... 006 342.1 006 -,002
Zero sequence voltage . . 005 214.1 004-003
Positive sequence current... 1.020 333.7 914-452
Negative sequence current... 074 57.7 039.062
Zero sequence current ... 007 332.5 008-003

Positive sequence impedance 977 266 «874437
Negative sequence

impedance. 081
Zero sequence impedance... 704
Complex power A tees
Complex power B
Complex power C
Average complex power .

284.3 .020
2416 -.
1.035 223958302
1.073 28.2 «946507
945 294 «823464
. 1,016 26.5 909 453
All parameters listed here are derived from one sample of
terminal values measured on a 10-hp motor. This list and the one
in table 1 will serve as one of many observations that make up
input data for PNETTR modeling.
?Power factor A = 0.9254, power factor B = 0.8815, power fac-
tor C = 0.8713, and average power factor = 0.8928,

Data generated for this research simulate insulation
leakage across all possible combinations of seven locations
on motor windings. Connection points include phase con-
ductors (motor line terminals), midpoints of each delta-
connected winding within the motor, and the power system
neutral (G). A variable resistance placed between selected
points created leakage currents, as illustrated in figure 5,
including simulation of all the following paths:

A-B B-C CA
AG B-G CG
Aa B-a Ca
Ab B-b Cb
Ac B-c Cc
ab b-c ca

aG -bG cG


Laboratory-generated motor data PNETTR ‘
(known insulation deterioration software ALN s (mathematical models

(mathematical deterioration)

electrical parameters) modeling) terioration

-
|
|
|
1
' states and associated
1
!
!
Ll

ALN development

1
|
1
|
representing motor 1
i}
|
|

Equipment motor data

values for each motor
on a continuous miner)

Analysis of
motor data

Assessment of insulation
condition for each

continuous miner

r
'
!
' (e.g., electrical terminal
t
'
|
'

Motor condition monitoring system

|
|
|
motor on the I
I
|
|

Figure 1.—Methodology for creation of deterloration-detection ALN's,

PNETTR-4X MODELING SOFTWARE

PNETTR-4X is a data-modeling software package
marketed by General Research Corp. It can create a poly-
nomial network or ALN to represent a physical process
(4). PNETTR builds ALN’s selecting only those input var-
iables that are necessary for accurate modeling.

The data base required for modeling must include
quantities to be used for both dependent and independ-
ent variables. ALN’s are the mathematical functions that
relate some or all of the available independent variables
to one or more dependent variables. Although the over-
all modeling process is unsupervised, the user can signif-
icantly alter the final form of an ALN through numerous
control options that can restrict modeling flexibility,
limit use of data base information, and affect network
evaluations that are part of the modeling process. Note
that the term “adaptive learning network" indicates only
that the polynomial network adapts to the data base during
the modeling process. Once formed, a given ALN is fixed.

PNETTR assumes no particular model structure, but
creates series-parallel networks of polynomials and gen-
erates coefficients solely from a data base. The individual
elements of the networks are polynomials, each with up to

three different input values. The terms of all polynomials
generated by PNETTR are taken from the following gen-
eral expression:

Y = Ay + AVX, + AgX, + AgXs + AGX]X + ASX)X3
2 2 2 3
+ AgXQXq + AgXy + AgXz” + AgXs” + AyoXy

3 3
+ AnXy + ApX;-

Usually, only a few of the possible terms will be used in an
element, but both the networking of elements and the se-
lection of polynomial terms can range from quite simple
to extremely complex. Figure 2 illustrates several possible
clement connections within an ALN (4).

ALN’s have been used successfully in numerous fields
to model physical processes from observable variables.
Several examples are signal processing, pattern recogni-
tion, materials testing, geophysical sciences, process con-
trol, flight control, health sciences, and economics.


APPENDIX.—ALN IMPLEMENTATION DETAILS

The equations below outline the form of the ALN’s
developed using combined data from 50-, 100-, and 150-hp
motors.

Deterioration current ALN:
Xq = Ay + AQX, + AgXs + AGXYXs + ASX)?
2 3 3
+ AgXs” + AgXy” + AgXs',
and Yy = Ag + AyoX7 + AniX2 + AyX3
2 2
+ AygXpX + AygX7? + AysXp
2 3 3
+ AygX3 + AyX7 + AygX2 -
Deterioration power ALN:
Xg = Aig + AaoX + AgiXo + AgnXy + AgsXiX2
+ AygXiXq + AggXy? + AggX,”
+ AnXy + AX
and Y2 = Aag + AgoXg + AgiXs + AgoXeXo-

Where X, = negative sequence current polar mag-
nitude,

X__ = negative sequence current polar angle,
Xz = negative sequence current x-coordinate,
X4 = negative sequence current y-coordinate,
Xs = zero sequence current polar magnitude,
X, = three-phase average of real power,

X, = first-layer output for deterioration cur-
rent ALN,

x

first-layer output for deterioration pow-
er ALN,

Y, = deterioration current level,

¥, = deterioration power level,
and Ay-Ay, = coefficients.

In order to use the above equations, all input variables,
expressed in per unit or degrees, must be “scaled” accord-
ing to the following procedure (using X, as an example
input variable).

Xp actual - Xp mean

Xo scaled = 3 andard deviation”
Firstlayer output variables X, and X, do not need to be
scaled for use in the second layers.

‘The above scaling procedure is then reversed to derive
pet unit deterioration values from raw (scaled) Y, and Y,
ALN outputs. This is shown here (using Y, as an example
output variable).

Yo actual = [(Yp scaled) x (Yo standard deviation)
+ Yo mean.
The means and standard deviations are calculated from

ALN training data, and are listed below for independent
variables X, through X, Y,, and Y.

Variable Mean Standard deviation
X. 0.028191 0.022119
X 225.54 89.43
xX -0,00035328 0.023794
X -0,012098 0.023904
Xs 0.009097 0.013913
X 0.48197 0.32284
Y,. 0.071010 0.057294
Ypeeeeee 0.074930 0.070016

ALN coefficients are listed below.

A, = -0.18894
A, = 0.73500
A; = 0.06757
Ay = -0.19559
As = 0.13119
Ag = 0.18665
A, = -0.03381
A, = -0.01405
A, = -0.14007


CONTENTS

Page

Abstract ..... oe
Introduction ... . .
Background .......... Benen ween anaes
Adaptive learning network approach to failure detection .
PNETTR-4X modeling software ..........
Data collection .......
Development of motor failure detection ALN’s .
General .
Empirical motor data ...
Failure detection ALN’s .
Summary ....
Accelerated life motor testing
General ..........
Experimental design .
Preliminary results
Conclusions
References

BERaunnawunnne

ILLUSTRATIONS

1, Methodology for creation of deterioration-detection ALN’s .
2. Possible element connections for ALN ..... . . .
3. Layout of Mine Electrical Laboratory power system equipment and data-acquisition hardware used for

motor data generation .
4. Motor-alternator set used for motor data generation
5. Deterioration simulation for laboratory data collection . bene
6. Relationship of ALN input parameters to calculated deterioration current, for simulated

leakage on 150-hp motor running unloaded ...........00.0.005
7, Relationship of ALN input parameters to calculated deterioration power, for simulated A-b and 7a G

leakage on 150-hp motor running unloaded ........ 0.0... cee ce eee ce eee ee eee neteaeeeee 10
8. Deviation of ALN-calculated deterioration current from actual simulated deterioration current, for

Aw

aaAR

leakage on 150-hp motor running unloaded st
9. Deviation of ALN-calculated deterioration power from actual simulated deterioration power, for leakage
on 150-hp motor running unloaded.............. Deeb tent ee edeeeeet eat ne eaten eee ees . 2
10. Deviation of ALN-calculated deterioration current from actual simulated deterioration current, for A-G
leakage on 150-hp motor running with various loads... 2.20. ...0c0secceeeeeeeeeeeceuseuens R
11. Deviation of ALN-calculated deterioration power from actual simulated deterioration power, for A-G
leakage on 150-hp motor running with various loads we oR
12. Effect of motor temperature change on ALN-calculated deterioration, for simulated A-B leakage on
100-hp motor running at full load... 20... ese ec cc eeeeceueeceueeseneues B
13. Motor-alternator set for accelerated aging of motor insulation, shown with heat chamber open . 14
14. Layout of laboratory equipment for motor insulation accelerated life testing ........ . 15
TABLES
1, Example of test condition information ..... 6
2. Electrical parameters . .. . 6
3. Root mean squares of residuals between actual and ALN-calculated deterioration values, for motor

specific ALN’s ...........- bee eee eens eneene ceed eee e eee ee eneeeenaeees vee 8


KEY
Negative sequence current angle, 103 deg
Simulated deterioration power
* Calculated deterioration power
Real power 3-phase average
— Negative sequence current magnitude
Negative sequence current Y-component

0.40 1 : ; . 7

35
30
25
20

0.35

-30

DETERIORATION POWER AND ALN INPUT PARAMETERS, pu

5 1
0.00 0.05 0.10 Ols 0.20 025
SIMULATED DETERIORATION POWER, pu

Figure 7.—Relationship of ALN Input parameters to calculated deterioration power,
for simulated A-b (top) and A-G (bottom) leakage on 150-hp motor running unloaded,



015 020

on 02

0.05 0.10

005 010 Os 020 © 025

CALCULATED DETERIORATION DEVIATION, pu

ol 0.2

SIMULATED DETI

Figure 9.—Deviation of ALN-calculated deterioration

age on 150-hp motor running unloaded.

0.04 T T

04

0.04 T r

02

-02
motor

CALCULATED DETERIORATION DEVIATION, pu

03 0000 0025
ERIORATION POWER, pu

0.050

0100

power from actual simulated deterloration power, for leak-

0.04

No motor load

02

~02 L 1

0.04 ‘ T

CALCULATED DETERIORATION DEVIATION, pu

-.04
0.04 T T -02 1 1

02 0.04

00 02
-02F ---

Full motor load °°
04 L . - 02
0.0 Ol 02 03 ‘00 Ol 02 03

SIMULATED A-G DETERIORATION CURRENT, pu

SIMULATED A-G DETERIORATION POWER, pu _

Figure 10.—Deviation of ALN-calculated deterioration current
from actual simulated deterioration current, for A-G leakage on
150-hp motor running with various loads.

Figure 11.—Deviation of ALN-calculated deterioration power
from actual simulated deterioration power, for A-G leakage on
150-hp motor running with varlous loads.



Library of Congress Cataloging in Publication Data:

Homcee, Gerald T.
Early detection of insulation failure in electric motors / by Gerald T. Homce.

p. cm. — (Report of investigations; 9450)
Includes bibliographical references (p. 16).
Supt. of Docs. no.: 1 28:23.9450.
1. Squirrel cage motors—Reliability—Mathematical models. 2. Electric motors—

Insulation—Reliability. 3. Mining machinery—Electric driving—Reliability. I. Title.
Il. Series: Report of investigations (United States. Bureau of Mines); 9450.

TN23.U43  [TK2785] 622 s—de20 [621.46] 92-5710 CIP



Report of Investigations 9450

Early Detection of Insulation Failure
in Electric Motors

By Gerald T. Homce

UNITED STATES DEPARTMENT OF THE INTERIOR

BUREAU OF MINES


Comparing tables 3 and 4 shows the "all-motor" ALN
pair to be nearly as accurate as the motor-specific ALN’s.
Given the comparable performance, an ALN pair suitable
for a range of motor sizes would likely be more versatile
for actual motor monitoring. Therefore, further descrip-
tion and analysis will focus on the ALN’s generated from
combined 50-, 100-, and 150-hp data.

Table 3.—Root mean squares of residuals
between actual and ALN-calculated
deterioration values,

for motor-specific ALN’s

Motor, hp Current, pu__ Power, pu
EVALUATION DATA FOR ALL
LEAKAGE LEVELS
0.027 0.013
026 013
025 014
025 013

EVALUATION DATA FOR 0.000-
TO 0.10-pu LEVELS

0.019 0.012

019 012

019 014

019 013

Table 4.—Root mean squares of residuals

between actual and ALN-calculated
deterioration values, for ALN pair
created using combined 50-, 100-,
and 150-hp data

Data Current, pu Power, pu
50-hp 0.018 0.013
100-hp . 020 015
150-hp . 019 014

The exact form of these ALN’s is shown below.
Deterioration current ALN:

Xq = Ay + AgXy + AgXs + AGX]Xs + ASX,

+ AGX? + AgX + AGXs,

Ag + AyoX7 + AyX2 + AyoX3
2 2
+ AysXpX3 + AygXy + AysX2

2 3 3
+ AygXg + Ay7X7 + AygXy -

Deterioration power ALN:
Xg = Ayy + AgoXy + AyiXy + AgoXy + AzsXiXp

+ AygXiXq + AgsXy? + AggX”

3 3
+ AgX + ArgXy,
and Yq = Agg + AypXg + AgiXs + AgpXpXo-

Where  X, = negative sequence current polar mag-

nitude,
X, = negative sequence current polar angle,
X3 = negative sequence current x-coordinate,
X,4 = negative sequence current y-coordinate,

Xs = zero sequence current polar magnitude,
Xg = three-phase average of real power,

X; = first-layer output for deterioration cur-
rent ALN,

Xg = first-layer output for deterioration pow-

er ALN,
Y, = deterioration current level,
Y, = deterioration power level,

and Ay-Agy = coefficients.

The appendix contains full details for the use of the
above ALN, including coefficients and the scaling ex-
pressions required for variables.

Figures 6 and 7 illustrate the relationships between
input parameters selected by PNETTR and calculated
outputs, using the above ALN’s for two specific dete-
rioration conditions on the 150-hp motor running un-
loaded. These two cases are representative of similar plots
for other conditions, Note the importance of negative
sequence current components as variables in both ALN’s.
This is logical since negative sequence current is a direct
measurement of line current imbalance and, therefore,
indicates electrical imbalance present on a motor.


The rms values in table 4 indicate overall accuracy for
the ALN’s, but figures 8 and 9 illustrate in more detail
the influence of deterioration type and level on ALN
performance. These figures plot the deviation of ALN-
calculated leakage levels from known actual leakage levels
(laboratory simulations) for six different leakage paths on
the 150-hp motor running unloaded. The cases chosen are
representative of overall ALN performance.

Figures 10 and 11 are similar plots, showing the effect
of motor load level on ALN performance. The condition
represented is A-G leakage on the 150-hp motor, and
again, is generally representative of other conditions.

Winding temperature has a significant effect on con-
ventional insulation resistance testing techniques used to
assess motor electrical condition. The ALN’s developed
under this work, however, are not adversely affected by
motor temperature changes, since results are based on
parameters that represent motor electrical imbalance,
not actual winding insulation impedance values. Figure 12
illustrates this temperature independence by showing

ALN outputs calculated using data taken from a laboratory
100-hp motor, at full load, from 35° C to 85° C winding
temperature, while undergoing simulated A-B winding
leakage.

SUMMARY

The important findings of the foregoing discussion
follow:

© PNETTR modeling of motor systems can create
functions, ALN’s, that accurately detect and quantify sim-
ulated motor winding insulation leakage from measurable
electrical parameters,

© These ALN’s can be used to detect and quantify
simulated insulation leakage without regard for leakage
path location and severity, motor load level, or winding
temperature.

¢ A single ALN can accurately evaluate simulated
leakage on motors of several different sizes, specifically in
this research, 50-, 100-, and 150-hp motors.

0.08 r T
06
04
02
00

02

0.02

00

3
8

2
s

4
2
&

3
8

CALCULATED DETERIORATION DEVIATION, pu
g

a-G

02

SIMULATED DETERIORATION CURRENT, pu

03 00 on

Figure 8.—Deviation of ALN-calculated deterioration current from actual simulated deterioration current, for

leakage on 150-hp motor running unloaded,