565
IEEE Transactions on Energy Conversion, Vol. 5,No. 3, September 1990
MEASUREMENTOF THE TORQUJiXSPEEDCHARACIERImCSOF
INDUClTON MOTORS USING AN IMPROVED NEW DIGITAL APPROACH
B. Szabados, SM,IEEE, J.H. Dableh, SM, IEEE,
R.D. Finday, SM,IEEE
G.M. Obermeyer, R.E. Draper
McMaster University, Power Research Laboratory,
Hamilton, Ontario, Canada, U S 4K1
Westinghouse Canada Inc., Motors Division,
Hamilton, Ontario, Canada, LAN 3K2
A new measurement technique for the determination of the
torque-speed characteristics of induction motors is presented. "his
technique is in compliance with the IEEE standard test procedure
for polyphase induction motors and generators. It is based on the
acceleration method which is performed under no load conditions.
A fast data acquisition system is used to record the speed and other
signals of interest. The data is then processed digitally with the objective of removing undesired extraneous signals,while preserving
the accuracy of the machine characteristics over the complete
range of speed. To achieve this objective, new and unique algorithms to perform adaptive window-size average filtering and
numerical differentiation have been developed. Experimental tests
performed on 1-hp,20-hp and 600-hp induction motors confirmed
that this approach has several advantages over other currently
available methods. It produces very accurate results over the full
dynamic speed range of the machine and permits precise correlation between the switchingtransients on the line with the variation
of the speed signal.
line drawn along the middle of the oscillations would indicate tha
the minimum torque of this motor is at least 160% of the rated
torque. This is in accordance with the specificationsfor this motor.
However, the user experienced difficulty bringing the machine to
full load speed. The motor stalled before it reached 600 rpm. The
user was led to believe that the lower envelope of the oscillation
should be considered the representative characteristic of the motor. Clearly, a new measurement technique was required. It must be
capable of producing a smooth torque-speed profile without distorting the actual characteristics of the machine. The problem was
to eliminate the need for interpolationof the digitally differentiated analog signal. The analog signal is itself very noisy due to
coupling noise, interference, etc. Hence differentiation is highly
suspect.
300
1
Torque (in % of rated torque)
A
KEYWORDS Induction Motors, Torque-Speed Characteristics,
Acceleration Method, Data Acquisition, Numerical Filtering,
Digital differentiation.
INTRODUCIION
A new measurement technique for the determination of the
torque-speed characteristics of induction motors was developed
recently in the Power Research Laboratory at McMaster University in response to a request from Westinghouse Canada Inc.,
Motors Division [l]. The need for this development became necessary when the accuracy of the torque-speed profiles for two, three
phases, 2@hp, induction motors was in question. Specifically,the
value of the minimum torque at an approximate speed of one third
rated speed, obtained from the graph shown in figure 1 was
doubted. The torque-speed charactesisticsillustratedin figure 1for
one of the motors was obtained using conventional equipment and
an analog differentiator. The oscillationsin the curve, particularly
in the region of minimum torque, obfuscated the actual torque. A
90 WM 142-0 EC
A paper recommended and approved
by t h e IEEE Rotating Machinery Committee d t h e
IEEE Power Engineering Society for presentation a t
t h e IEEE/PES 1 9 0 Winter Meeting, Atlanta, Georgia,
8, 1990. Manuscript submitted
February 4
August 30, 1989; made a v a i l a b l e for p r i n t i n g
December 6, 1989.
-
0
Figure 1:
600
1200
1800
Torque-Sped characteristic for a three phase
B h p squirrel cage induction motor obtained
using conventional analog di€ferentiator.
The new measurement method was designed to comply with the
IEEE standard test procedure for polyphase induction motors and
generators[2]. The IEEE standard 112-1978outlines four methods
to obtain data for a torque-speed curve. The selection of which
method to apply depends upon the characteristics of both the
machine and the testing facilities. The main criterion is that sufficient number of test points must be recorded to ensure that reliable
and accuratecurves, includingirregularities, can be produced from
the test data.
The technique presented in this paper is based on the acceleration
method described in section 4.9.2.2 of the IEEE standard mentioned above. According to this method the motor is startedwith no
load after it has been rotated manually in the reverse direction to
that expected when the motor is energized. The acceleration at
0885-8%9/90/0900-0565$01.00 0 1990 IEEE
566
each instant of time is determined by differentiating the speed
signal. The torque at the respective speed is given by the product of
the acceleration by the moment of inertia of the rotating part. If the
moment of inertia is not known the relative torque versus time is
obtained. The absolutevaluesof the torque need to be scaledfrom
the locked rotor or pull out torque measurements. Accurate measurement of the speed and determination of its first derivative, the
acceleration, are crucial.
Over the past two decades several methods to measure the velocity
of rotating shafts have been reported in the literature [3-81. Older
methods are based on analog principles; they generally suffer from
low resolution and severe noise contamination. Early versions of
digital methods were reported in the late sixties and early seventies
[3-51. In these versions the sampling period varied with the speed.
This had the disadvantage of having very slow readings at low
speeds and the necessity of processing the time information. Corrective methods to increase the number of pulses per revolution
and obtain fast readout were suggested [6,7]. However, these
methods required theuse of either a fairly complexsensing method
[6] or an accurate servomotor [7]. A recent digital method to obtain
angular velocitywas given by Christiansen [8]. None of these digital
methods can be used to obtain speed by differentiationbecause of
the coarseness of the quantization. Even if a fast clock is used, one
runs into either clock overrun or too many counts per intervals. An
intelligent device which switches between modes might conceivably be constructed, but the complexity outweighs the use as a conventional tool on an industrial test platform, and its proper functioning has yet to be proven.
The method presented in this paper uses a fast data acquisition
system to sample the output of a dc tachometer as well as other
parameters of interest, such as the line currents and voltages. The
collected data is then processed digitally to remove the noise,
perform dynamic average filtering to eliminate extraneous coupling vibrations and numerically differentiatethe resultant clean
speed signal to obtain the relative torque profile. The various
algorithms used to achieve this objective and typical results obtained from different induction motors are also presented in this
paperDEscRipIlON OF THE NEW DIGITAL,APPROACH
The data acquisition system used to perform the dynamic speed
measurements consists mainly of a conventional dc tachometer,an
analog-to-digital (A/D) converter and a personal computer. The
A/D converter has 8 differential channels and can be operated at
avariable sampling rate up to 40 kHz. An external pulse generator
is used to trigger the A/D converter and set its samplingrate. If the
time required for the tested machine to reach its maximum speed
is larger than 0.2 second, a sampling rate of 5 kHz per channel is
quite sufficient for the acceleration tests.
All of the tests performed during the development of this method
were conducted at full rated voltage of the respective machine and
without any loading, except for the small tachometer. The machine
was rotated, by hand for small machinesbelow 25-hp, in a direction
opposite to that of the expected operatingrotation,prior to energizing the machine. In the case of large machines, plugging was used
to achieve starting from a reverse rotation. This was done in
accordance with IEEE standard test procedure [2]. It had the
advantages of deliminating the zero-crossing of the speed curve
representing the sampled data. It also allowed the switching transient on the supply voltage waveform to disappear while the
machine was deccelerated from the imposed reversed rotation.
Therfore, the collected data in the speed range of zero to full speed
was not affected by the switchingtransients due to the supply bus.
Figure 2 illustrates typical raw speed data obtained during an
acceleration test of a 600-hp induction motor. This raw data was
collected at a sampling rate of 5000 samples per second. As can be
clearly seen, this data is contaminated with several undesired
signals. Such signals include tachometer commutator spikes and
modes of oscillationsexternal to the motor itself, specificallylarge
coupling oscillations.
I
Figure 2:
. < F -.
Typical unprocessed speed data collected during
acceleration of a 6Whp induction motor at a
sampling rate of 5 kHz
The first task of the data processingphase adopted in this measurement method involves the cleaning of the collected speed data to
eliminate all of the extraneous signalswithout distorting the actual
profile of the speedcurve and the oscillationsthat are caused by the
electromagnetic field in the machine operation. To achieve this
objective special adaptive average filtering algorithms have been
developed and used to preserve the accuracy of the data over the
full speed range between zero and synchronousspeed. This is done
in contrast to the normal averaging technique with a fixed bandwidth. Since the output waveform of the tachogenerator is proportional to speed, the amplitude of unwanted oscillations due to
uneven windingsare proportional to speed, while their frequency is
inversely proportional to the speed. Hence a fixed bandwidth
filtering would have to be tuned for a low speed, and thereby
removes the higher frequencies that are actually caused by the
electromagnetic fields of the machine in the upper range of speed.
In the proposed method an adaptive bandwidth is used. The
bandwidth is inverselyproportionalto speed,with a clampingvalue
at very low speed.
The next data processing task consists of performing numerical
differentiation of the cleaned speed curve. The success of this task
and the smoothness of the derivative curve depends on the cleanliness of the speed curve and the differentiating algorithm used.
This is outlined later in this paper. The final task is plotting and
presenting the results in curves as functions of time or as torque
versus speed characteristics as may be required by the user.
DATA PROCESSING AND TYPICAL
NUMERICALRESULTS
A number of software packages were developed to perform the
various data processing tasks mentioned in the previous section.
The preliminary preparation of the data consists of separatingthe
567
samples of the various channels of the A/D converter and identifying the monitored signals. A digital scope program is then used to
view each signal and to select the interval of interest to be stored for
further processing. Note that the A/D converter is activated prior
to energizing the motor and is kept runing for a shortwhile after the
motor is de-energizedto ensure complete coverageof the acceleration period. Thus the digital scope is very useful to view the data and
cut out any redundant segment. A typical speed signal extracted
from the collected data by using the spliting and digital scope
routines has been displayed in figure 2 above.
Filtering of the speed signal is performed very carefullyin a number
of steps to remove only the extraneous signals and preserve the
main signalover the complete speed range of the machine. The first
step is aimed at removing the noise spikes caused by the commutator brushes of the tachometer used and other sources of random
spike noise. This is achieved by applyingawindowingpeak removal
algorithm. This algorithm simply examines three data points at a
time (window size is 3 ) and checks if they are in a monotonic
ascending or descending order. If so, the central point is left intact.
Otherwise, the point in the middle is replaced by the average of the
two neighbouring points to prevent it from appearing as a spike.
Note that the median filter algorithm is also included in the “tool
kit”. In case several sample points cover the spike, a slidingwindow
of 10 samples using the median filter is applied as a more robust
spike removal algorithm. This algorithm is very slow though, and it
was found that the 3 point window (much faster) used above is
sufficient. If needed, it can be applied more than once, to lead to a
better result.
Applying this algorithm to the speed data shown in figure 2, with a
window around the top knee of the curve (figure 3 (a)) provides the
results illustrated in figure 3(b). The ripples remaining in this graph
are thought to be due to the uneven windings of the tachometer.
A special dynamic window averaging algorithm was used to filter
out the small ripples in the above signal. Since these ripples are
caused by the uneven windings of the tachometer, their frequency
depends on how fast the tachometer is rotated. Therefore, the size
of the averaging window ought to be adjusted dynamically as a
function of the speed. Note that the size of the window is inversely
proportional to the speed. However, to establish a referencefor the
size of the window one should “zoom-in”to the synchronous speed
portion of the curve, using the digital scope program. The width of
the repetitive pattern of the high frequency ripples can then be
found.The number of samples within this pattern establishes the
window size at synchronousspeed, see figure 3 (b). At half synchronous speed the window sizewould be double the original measured
size. Naturally, at low speeds the size of the window has to be
clamped to a maximum window size. Figure 3 (c) shows the results
of applying this algorithm to the data of figure 3 (b).
At this stage , the oscillation modes due to the flexible coupling
between the shafts of the motor and tachogenerator are still present
in the speed signal. These oscillations can be grouped into two
broad categories: a low frequency mode and a high frequency
mode. Each of these modes is filtered out seperately using a piecewise fiied window averaging algorithm.To ensurethat these modes
are identified properly and no other oscillations that are inherent
to the motor itself are removed, the speed data from a run down test
is examined. The data collected after the motor is de-energized, as
it deccelerates from synchronous-speedto zero, displays only the
modes of oscillation due to the mechanical parameters of the
machine itself. Oscillationsdue to electromagnetic fields are elimi-
Figure 3
Processhg of speed data (“zoom-in“
on upper knee)
a) Raw data
b) commutator spikes and random noise removed
c) Tachogeneratoruneven winding noise removed
d) couplingnoise removed
nated by this process leaving flexible coupling noise and other
mechanical modes. Therefore, the run down test data is used to
establish the appropriate size of the averaging window at various
subranges of the synchronous speed. These window sizes are then
used to filter the data of figure 3 (c). Figure 3 (d) show the results
of applying this algorithm after the high and low frequency modes
of coupling ocillations have been filtered.
The amount of random noise and extraneous signalsthat have been
removed from the raw speed data can be appreciatedby superimposing the raw data of figure 2 and the cleaned up signal, as shown
in figure 4.
At the completion of the above filtering, the speed signal can be fed
to the numerical differentiationalgorithm to derive the acceleration or relative torque profile of the machine. In the early stage of
development of this method, standard differentiation algorithms
were used. Namely, these were various polynomial fittings and
spline fittingswhich all introduced large parasitic oscillationsin the
derivative masking the real curve. Unless the speed data is sufficiently smoothed, the resultant derivative curve would display a
significant amount of distortion which is not representative of a
physically realizable system. This indicated that some additional
smoothing of the speed signal was needed. A mean square window
averaging algorithm was used to achieve this objective. This new
smoothed curve was then differentiated numerically. The algo-
568
NEW DIFFERENTIATlON AEORlTHM
speed (rpm)
The need to perform mean square window averaging in some cases
and the smoothing of the derived torque curve were considered to
be disadvantageous because of three main reasons:
- 4000
- 3000
a) The final degree of filtering is not determined until the
differentiation is executed.
b) The smoothing of the derivative curve results in small attenuations of the curve peaks: the amount of smoothing
versus the reduction of the peak value is left to the judgement of the operator.
c) The additional mean square averaging is very time consuming even on a powerful personal computer.
- 2000
- 1000
To circumvent these disadvantages and the inconvenience of having to execute these tasks, a novel differentiation algorithm that
treats sudden changes in the signal was developed. Basically, the
algorithm looks at thresholds of variations within a certain sensitivity region and adapts a window size dynamically. Within this
window the derivative is assumed to be continuous. The algorithm
starts at a sample point So. It searches subsequent samples until
the variation AS = I Sn - So I is larger than a preset tolerance t.It
is assumed that the slope I‘betweeen So and Sn, the new “breakpoint”, is linear and is given at sample (i) by: r i = i(Sn-So)/n + ro.
This assures continuity of the slopes at breakpoints, and does not
introduce high frequencycomponents due to quantization. This algorithm was implemented in the data processing of this method
with a high degree of success.The success of this new differentiation algorithm was demonstrated when the resulting derivative
curve showed that the amplitude of the lower frequencieswere not
affected by the tolerance parameter of the algorithm. Furthermore, the derivative curve stabilized quickly above a certain
tolerance level. Figure 6 shows the results of applying this algorithm to the speed data of figure 4 displayingthe final torqueversus
speed curve.
Figure 4 Speed data before and after digital filtering
Relative Torque
IA
- 100%
Time
Torque
(in % of pull out torque)
Figure 5: Relative torque versus time curve for a m h p motor
a) First method of differentiation
b) Smoothing of curve (a)
rithm was to take a certain window size and find a linear best fit to
the points within the window. The slope of this line is taken as the
best value for the slope at the center point of the window.
The resultant derivative curve still showed high frequency oscillations introduced by the algorithm, as shown in figure 5 (a). These
were easily removed by conventional digital filtering, but care had
to be taken because the amplitude of the lower frequencies were
also affected slightly. Thus, the resulting smoothed relative torque
versus time curve, shown in figure 5 (b), may be questionable in
spite of the fact that it was repetitive and agreed well with design
data. A new differentiation algorithm was developed to overcome
the problems.
Figure 6:
Torque versus speed curve produced by the new
differentiation algorithm.
The oscillations remaining in the final torque profile are attributable to the actual electromagnetic pulsation torques. They form an
integral part of the machine characteristics.
706Apeak
1
(a) Line Current
DI!XUSIONS
The appoach presented in this paper to determine the torquespeed characteristic of induction motors has been demonstratedto
produce excellent results. The capability of this method to produce
smooth curves by eliminating extemal disturbance signals, while
preserving the characteristic pulsation torques of the machine, has
undisputed advantages over other available methods. A typical indicator confirming that electromagneticallycaused oscillations of
the machine are preserved can easily be examined by focusing on
the speed or torque traces at synchronous speed. Figure 5 (b), for
example, shows the small variation in the torque as the machine
speed oscillates about synchronous speed under no load. Also the
rigour of this argument can be validated by examining the steady
state and run down segments of the speed data as they are subjected
to the numerical filtering algorithms.To illustrate this point typical
results and intermediateoutput of the various filtering algorithms
are presented in figure 7 (a) to (d) for a portion of the run down test
data.
!
Tie
Figure 8 Correlationbetweenthevariationsof the Line Current
(a) and the Torque @) of a W h p inductionmotor.
the rotor would be pulled into alignment with the stator by the air
gap field. This results in torque oscillationsdue to air gap reactance
variations. This phenomenon had not been detected with analog
methods.
The torque-speed characteristic of the 20-hp motor is shown in
firgure 9 to facilitate direct comparison and illustrate the merits of
the new digital approach.
Figure 7:
Filteringof run down test data
a) Rawdata
b) Commutatornoise removed
c) Uneven winding noise removed
d) High-frequencycoupling noise removed
Another advantage of the method is its ability to collect data
pertinent to other relevant variables in the system. For example, in
the tests performed on a 600-hpmotor the line current of one phase
was monitored. Figure 8 (a) shows the variation of this current over
the complete acceleration period.
It is interesting to compare the variations in the torque profile to
those in the starting current profile, as depicted in figure 8. Note
that the small oscillations in the torque near the starting of the
machine coincide very well with the oscillations seen on the envelope of the currentwaveform. Also,when the motor reaches about
two third of its synchronous speed, an unexpected torque oscillation occurs, without any signs of line current oscillation. This has
been explained bv the fact that the machine under test had no axial
stabili&ion, and as much as 10cmaxial travel was noticed. Hence
One possible limitation of the proposed method may arise in the
case of certain machines that may have electromagnetic torques in
the same range as the noise introduced by the tachogenerator or the
coupling transients. In this case, these actual torque pulsations are
eliminated in the filtering process. Although it is reasonable to
Torque (in % of rated torque)
600
1200 1800
Analog method
660
1~00
Digital method
1
Figure9 Comparison between d o g and proposed digital
method torque-speed characteristicsof a 2OHp motor
I
.,
570
assume that the noise bandwidth of the tachogenerator and flexible
couplingare outside the frequencyrange of the machine torque, the
authors are investigatingaspecial measure to circumvent this issue.
The new measure involves using two completely different tachogenerators with clearly different non overlapping noise bandwidth
and coupling characteristics.Applying the new measurement technique to the signalsof both tachogenerators clearlyreveals whether
this limitation applies or not for a specific test run.
CONCLUSION
A new digital technique to determine the torque-speed characteristics of induction motors, in accordance with the acceleration
method of the IEEE standard test procedure for polyphase induction motors and generators, has been presented. In contrast to
other currently available methods, this technique has the advantage of eliminating all extraneous noise and disturbance signals
while preserving the actual characteristics of the machine.
The digital data processing and algorithms used to realise the
advantages of this technique have also been discussed. The use of
these algorithms is not restricted to this application only. The
digital scope, the dynamicwindow averaging and the new differentiation algorithms provide unique and powferful tools to handle a
wide range of contaminated signals, such as those observed during
transient conditions. The validity of these algorithms have been
verified by investigating the data produced during the steady state
and run down operation of the machine. This investigationwas also
crucial in establishing the size of the various windows used, and to
systematically identify the disturbance signals.
The data acquisition system used to collect and store the desired
information offers a high degree of flexibility as far as the amount
of collected data and sampling rate are concerned. The availability
of accurate measurements of other variables in the system is very
beneficial in assessing the torque profile of the machine and for
trouble shooting tasks that may be required.
The approach described in this paper can be automated to a large
extent to form a standard test method in the manufacturing of
induction motors.
REFERENCES
[l] B. Szabados, J.H. Dableh, R.D. Findlay and D. Stafford, “A
New Approach For Measurement Of The Torque-Speed
Characteristics Of Induction Motors”,Paper Accepted For
Presentation At The Fourth International Conference On
Electrical Machines And Drives, September 13-15,1989, London, England.
[2] IEEE Standard 112-1978, “IEEE Standard Test Procedure
For Polyphase Induction Motors And Generators”,pp 7-30.
[3] G. Hoffman de Visme, “Digital Processing Unit For Evaluating Angular Acceleration”, Electron. Eng., 40,April 1968, pp
183-188.
[4] A. Dunworth, “Digital Instrumentation For Angular Velocity
And Acceleration”, IEEE Trans. Instrum. Meas., IM-18, June
1969, pp 132-138.
r ----r
[ S ] N.K. Sinha, B. Szabados and C.D. DiCenzo, “New High Precision Digital Tachometer”, Electron. Lett., 7, April 1971, pp
174-176.
[6] B. Habibullah, H. Singh, K.L. So0 and L.C. Ong, “A New
Digital Speed Transducer”, IEEE Trans. Ind. Electron. Contr.
Instr., IECI-25, No. 4, November 1978.
[7] C.D. DiCenzo, B. Szabados and N.K. Sinha, “Digital Measurment of Angular Velocity For Instrumentation And Control”,
IEEE Trans. Ind. Electron. Contr. Instr., IECI-23, No. 1,
February 1976.
[8] C.F. Christiansen,R. Battaiotto, D. Fernandes and E. Tacconi,
“Digital Measurment of Angular Velocity For Speed Control’’, IEEE Trans. on Ind. Electron., Vo1.36, No. 1, February
1989, pp 79-83.
Barna !jzabadq was born in Hungary
and received the DiplSme d‘IngCnieur
ENS1 from the Universitt de
Grenoble in 1967, and his Master’s
and Ph.D. degrees in Electrical Engineering from McMaster University,
Hamilton, Ontario, Canada, in 1969
and 1971, respectively.
He is presently Professor of Electrical and Computer Engineering
at McMaster University in the Power Research Laboratory. He is
working in the area of solid state converters, field representations
in electrical apparatus and electromagnetic interference, and local
area networking in factory environment. All these projects are
done in a tight cooperation with industrial partners, Westinghouse
Canada and General Motors. Dr. Szabadosis a Senior Member of
the IEEE, and holds positions in several committees. He is also a
member of several other international professional societies.
Joseph H. Dableh was born in north
Lebanon. He received his B.Sc.E. and
M.Sc.E. degrees in Electrical Engineering from the University of New
Brunswick in 1976 and 1978, respectively, He obtained his Ph.D. degree in
Electrical Engineering from McMaster University in 1986.
He worked as a Research Engineer at the Ontario Hydro Research
Division from 1978 to 1987. He has been at McMaster University
since 1987,where he is a part-time Assistant Professor of Electrical
and Computer Engineering, and a full-time Senior Research Engineer in the Power Research Laboratory since 1988. His research
interests include pulse power and electromagnetic metal forming
for assembly and rehabilitation of CANDU nuclear reactors, electromagnetic field computations in electric machines and power
apparatus, control and power systems. He holds several patents related to nuclear reactor repair and pipe-type cable systems. He is
a registered professional engineer in the Province of Ontario and a
Senior Member of IEEE.
RaM
o
lndD
F
.n
id
a
lv was
born in
Toronto, Canada, obtaining degrees
of B.kSc., M.kSc., and Ph.D. in 1963,
1965 and 1968 respectively, in electrical engineering from the University of
Toronto. Dr. Findlay is a registered
professional engineer in the Province
of Ontario,a Senior Member of IEEE,
and a member of ASEE.
His interestsinclude electromagnetic fieldsand losses in power apparatus, an area inwhich he holds one US patent.From 1967to 1981
he was affiliated with the University of New Brunswick, achieving
the rank of Professor in 1978. During 1972-3he was a project leader
at Canadian General Electric Co. He has been a Visiting Fellow at
the University of Southampton (U.K.) in 1979-80,at the Katholieke
Universiteit Leuven (Belgium) in 1988, and at the Commonwealth
Scientificand Industrial Research Organization (Australia), also in
1988. Dr. Findlay is a Professor and a member of the Power Research Laboratory of McMaster University, where he has held an
appointment since 1981, and during 1984-7 as Assistant Dean of
Engineering.
Robet E. Draper was born in Welland, Ontario, Canada. He
received a BSc of Applied Science in Electrical Engineering from
the University of Waterloo in 1985. From 1985 to 1987 he worked
in the field of application engineering for Union Gas Limited in
Chatham, Ontario.From 1987to present he is engaged in the design
of large induction motors for Westinghouse Canada in their Motor
Division in Hamilton, Ontario. He is a member of the Association
of Professional Engineers of Ontario.
F.M.Obermever graduatedfrom McMaster UniversitywithaBSc
in Electrical Engineering in 1977. From 1977to 82 he worked in the
steel and paper industries and joined Westinghouse in 1982. He is
presently engineering supervisor in the Motor Division and his
technical interests are rotating equipment. He is a member of the
Association of Professional Engineers of Ontario.