Rotor fault and location detector for induction motors

A method and apparatus for detecting rotor faults in an induction motor. Such induction motors have a stator and a rotor comprising a multiplicity of rotor bars. The flux around the rotor bars is detecte at a predefined flux detection point in the motor, generally by using a coil wound around one of the stator teeth. A synchronization signal is generated once per revolution of the rotor when a predefined position on the rotor is closest to a stator reference point, usually the flux detection point. The detected flux signal is filtered to reject signals in a predefined frequency band around the frequency at which the rotor bars pass the flux detection point, and then it is synchronously time averaged. Averaging requires that corresponding portions of the flux signal, for a series of rotor revolutions, be added, and the synchronization signal is used as a reference for matching corresponding portions of the flux signal. Statistically significant peaks in the resulting time averaged signal indicate the presence of a rotor bar fault, and the location of such peaks corresponds to the location of rotor bar faults with respect to the predefined position on the rotor.

The present invention relates generally to induction motors, and 
particularly to a system and method for detecting the presence and 
location of rotor bar faults in induction motors. 
BACKGROUND OF THE INVENTION 
The present invention monitors an induction motor while it is operational, 
and detects and locates rotor bar faults in real time without interfering 
with the operation of the motor. 
The prior art includes numerous methods of detecting "cage faults". Most 
require one or more of the following actions: disassembly of the motor, 
motor shut down, and/or special connection of instrumentation inside the 
motor. For instance, the growler method uses an electromagnet coupled to 
the rotor surface which emits a loud noise when it spans an open rotor 
bar. This requires disassembly of the motor. 
Single phase testing requires disconnecting one phase of the motor's power 
supply, and monitoring the current drawn while exciting the remaining 
terminals at low voltage and rotating the rotor slowly, by hand. If there 
is a broken bar the current drawn will vary with rotor position. While 
sensitivity is good--a broken bar is usually clearly evidenced by a 
current variation of over five percent--the motor must be taken out of 
service and one phase disconnected. Further, the low voltage power 
requirement is considered to be a safety hazard by many utility companies. 
Unlike prior art systems, the present invention unambiguously detects rotor 
faults while the motor is running. Furthermore, it identifies the location 
of the rotor bar with the fault. The ability to locate the bar or bars at 
fault is of value because it facilitates fault identification and repair. 
As will be described in more detail below, the present invention works by 
using a coil on a stator tooth, inside the motor, to measure the flux 
around (actually, in the air gap above) each rotor bar while the motor is 
running. By synchronizing the measurement process with rotation of the 
rotor, the flux measurements for each rotor bar can be separately 
identified. The underlying theory of operation is that the measured flux 
will be markedly different for normal rotor bars and for rotor bars with 
faults. 
The main disadvantage of the present invention is that it requires the 
installation of a coil on a stator tooth inside the motor. Clearly, for 
new or rewound motors this is not a problem, because the coil can be 
installed when the motor is being assembled. For motors which have already 
been assembled, and for those already in operation, the disassembly 
required for installing a stator tooth coil obviously requires an 
interruption in service. On the other hand, only one interruption is 
required in the life of the motor because the fault monitoring process 
itself is performed during normal motor operation. 
It is therefore a primary object of the present invention to provide a 
system and method for detecting and identifying the location of rotor bar 
faults in induction motors. 
SUMMARY OF THE INVENTION 
In summary, the present invention is a method and apparatus for detecting 
rotor faults in an induction motor. Such induction motors have a stator 
and a rotor comprising a multiplicity of rotor bars. The flux around 
(actually, in the air gap above) the rotor bars is detected at a 
predefined flux detection point in the motor, generally by using a coil 
wound around one of the stator teeth. A synchronization signal is 
generated once per revolution of the rotor when a predefined position on 
the rotor is at a selected reference position on the stator, usually a 
point closest to the flux detection point. 
The detected flux signal is filtered to reject signals in a predefined 
frequency band related to the frequency at which the rotor bars pass the 
flux detection point, and then it is synchronously time averaged. 
Averaging requires that corresponding portions of the flux signal, for a 
series of rotor revolutions, be added, and the synchronization signal is 
used as a reference for matching corresponding portions of the flux 
signal. Statistically significant peaks in the resulting time averaged 
signal indicate the presence of a rotor bar fault, and the location of 
such peaks corresponds to the location of rotor bar faults with respect to 
the predefined position on the rotor.

DESCRIPTION OF THE PREFERRED EMBODIMENT 
Referring to FIG. 1, an induction motor 20 is monitored by a rotor fault 
detection system 30 while driving its usual motor load 22 via the motor 
shaft 24. 
As shown in FIG. 2, which is a section view corresponding to line 2--2 in 
FIG. 1, the motor has a stator 26 and a rotor 28. The stator 26 has 
numerous stator teeth (which are generally parallel to the axis of the 
motor's shaft 24), including one stator tooth 27 which has been wrapped 
with a flux detection coil 32. The coil 32 is made up of several turns of 
fine wire (e.g., #33 wire). The motor also has a rotor 28 which includes 
numerous rotor bars 29 running generally parallel to axis of the motor's 
shaft. 
For those not skilled in the art of induction motor design, several items 
of basic information regarding induction motors are noted. First, the 
stator is wired to an a.c. power supply so that it generates a rotating 
magnetic field that causes the rotor to turn. Unlike some other types of 
motors, the speed of the motor--i.e., the rate at which the rotor 
turns--depends on the size of the load being driven by the motor. This 
rate is equal to the nominal motor speed (i.e., the rate at which the 
stator field rotates) minus a factor called the slip rate, which is 
typically a very small fraction of the nominal motor speed. The important 
fact for purposes of the present invention is that the exact rate of the 
rotor is not known unless it is measured. 
Second, the stator's magnetic field induces current in the rotor bars 29. 
As the rotor spins, the current in the rotor bars generates magnetic 
fields which, in turn, induce a signal in the coil 32. 
The purpose of the coil 32 is to measure the magnetic flux field associated 
with each rotor bar. The flux associated with open and high resistance 
rotor bars (which are open or partially open) will have a different 
pattern than the flux associated with normal rotor bars because the 
current flow through the broken rotor bar will be blocked, creating an 
imbalance between the stator's rotating field and rotor's field at the 
defective rotor bar or bars. 
Referring to FIG. 1, the signal in the flux coil 32 is buffered by a 
differential amplifier 42 and digitally sampled using a twelve bit analog 
to digital converter (ADC) 34. The converter 34 includes a second circuit 
for sampling the REV signal--so that the flux data can be correlated with 
rotor position. Since the REV signal is either on or off, a simple 
comparator can be used to generate a binary 0 or 1 signal to indicate 
whether the REV signal is on or off. 
The ADC 34 samples the flux signal each time a SAMPLE pulse is received, 
and then automatically begins conversion of the sample into a digital 
value. When it has completed conversion of the flux signal, the ADC 34 
generates an "end of conversion" signal EOC. The EOC signal is used as a 
hardware interrupt signal by the computer 36, which responds by reading in 
the digital flux signal, and the binary REV signal. Both of these data 
items are stored serially in a buffer memory in the computer for analysis 
after the data is collected. 
The signal in the flux coil 32 is digitally sampled at a frequency 
sufficient to separately measure the flux associated with each of the 
rotor bars 29 in the motor. Typically, the flux coil signal will be 
sampled at least four or five times during the time it takes one rotor bar 
to pass the coil 32. These measurements are taken for a period of time, 
such as ninety revolutions of the rotor, so that time averaged data can be 
used for detecting rotor faults. 
The present invention locates the rotor bar or bars which have faults by 
synchronizing data collection with a predetermined reference location on 
the rotor 28. To do this, an encoder or tachometer 38 is coupled to the 
motor shaft. In one embodiment, the encoder 38 is a standard shaft encoder 
that produces two signals: (1) a REV signal which is a pulse that is 
generated at a predetermined rotor position; and (2) a second signal SP 
which comprises a specified number of pulses per revolution of the rotor. 
In this particular embodiment, the encoder generates 2500 pulses per 
revolution. Since we will typically need around 200 pulses per revolution 
to control the A/D converter, the SP signal is divided down by a divider 
circuit 40, which outputs a specified number of SAMPLE pulses per 
revolution. 
In a second embodiment, the encoder 38 is a tachometer which generates only 
the REV signal. The pulse generator 40 in the second embodiment includes 
an oscillator, controlled by a phase locked loop, which generates a SAMPLE 
signal at a specified multiple of the frequency of the REV signal. 
Example. The following describes the data collection technique used in a 
test of the invention. The motor was a 50 horse power motor with 
thirty-six rotor bars. Five data samples were taken for each rotor bar as 
it passed the flux detection coil 32. Furthermore, the motor was operating 
at a nominal speed of 1800 rpm (i.e., thirty revolutions per second) 
during the tests. Due to normal motor slip the actual motor speed was 
slightly less than 1800 rpm, but this differential can be ignored for most 
purposes of this analysis because of the way flux signal is sampled. 
Thus, the number of data points collected per revolution of the rotor was 
EQU samples per revolution=5*36=180 
The amount of data collected per second was: 
EQU samples per second=30*180=5400 
which is well within the rate that can be handled by ordinary A/D 
converters and desk top personal computers. 
Actually, as noted above, two data items are sampled and stored with each 
SAMPLE pulse: the flux signal, and also the REV signal. 
Using a memory buffer that could hold up to 16384 data samples, data was 
collected for ninety motor revolutions. When the data collection routine 
was started, the collected data was discarded until the first REV signal 
was received, so that the collected data would start at a known position 
on the rotor. Then ninety revolutions of data were collected. 
Analysis of the collected data was performed as follows. First the data was 
filtered using a commercially available software signal processing 
program. Two filtering steps were performed: spectral components below 100 
Hz were rejected by a high pass filtering step to eliminate the effects of 
the motor's sixty hertz power supply, and a spectral band (700 to 1400 Hz) 
associated with the rate at which rotor bars pass the flux detection coil 
(36 bars/rev*30 rev/sec=1080 Hz) was removed by a band rejection filtering 
step. 
Second, the data was rectified by taking the absolute value of every 
filtered data sample, to eliminate phase shift oscillation at the slip 
frequency. Third, the data was synchronously time averaged using the 
stored REV signal to indicate the beginning of each data subset (i.e., the 
data for each revolution of the rotor). See Appendix 1 for an example of 
an algorithm for synchronous time averaging. Fourth, the resulting data 
was plotted. 
FIGS. 4a-4c show the REV signal, the raw flux data for approximately one 
revolution, and the time averaged data, respectively, for a motor with no 
known rotor faults. While there are many peaks and valleys in the averaged 
data, these are due to signal noise and normal variations in the 
resistivities of rotor bars. 
FIGS. 5a-5b show the raw flux data and the time averaged data, 
respectively, for a motor in which a hole was drilled radially in the end 
ring at one end of one rotor bar. This was done to demonstrate the 
sensitivity of the invention to partially open rotor bars. When installing 
the rotor in the motor, the encoder was coupled to the rotor so the 
partially open rotor bar would appear at approximately one half revolution 
in the synchronous averaging analysis. As can be seen in FIG. 5a, the 
effects of the partially open bar are very pronounced and appear in the 
predicted location. Tests of this rotor using the "growler" method failed 
to detect the partially open rotor bar. 
FIGS. 6a-6b show the raw flux data and the time averaged data, 
respectively, for a motor in which more holes were drilled in the same 
rotor bar, which was partially drilled as described above, to produce a 
truly open rotor bar. While the raw data in FIG. 6a does show a 
discontinuity, the time averaged data in FIG. 6b clearly shows the effect 
of the open rotor bar. FIG. 6c shows the same analysis results as FIG. 6b, 
using a scale compressed to show the peaks. 
While the four step signal analysis procedure described above is preferred, 
some steps are more important than others. The rejection of the rotor bar 
frequency (1080 Hz in the example), and the synchronous time averaging are 
essential for reliable detection of open rotor bars. The rectification 
step is believed to be required for reliable detection of high resistance 
or partially open rotor bars. The rejection of the power supply frequency 
(60 Hz in the example) improves sensitivity, provides better signal to 
noise ratio, and decreases the false alarm rate. 
ALTERNATE EMBODIMENTS 
While the output in the preferred embodiment is a plot of the time averaged 
flux data, in other embodiments the computer could be programmed to 
automatically mathematically analyze the time averaged flux data and 
thereby detect the presence of peaks which indicate the presence of an 
open or high resistance rotor bar. Such automated detection could be 
performed using a fairly simple statistical analysis--such as detecting 
the presence of data more than three or four standard deviations above the 
mean data value. When the computer analysis detects a suspected fault, it 
could then perform any predefined task--such as ringing an alarm, and/or 
printing a plot such as the one shown in the Figures herein, or any other 
action designed to communicate the presence of a suspected fault. To help 
eliminate false alarms, the test could be repeated when a suspected fault 
is detected, and an alarm generated only if the analysis once again finds 
a suspected fault. An example of an automated fault detection method is 
shown in Appendix 2. 
Referring to FIG. 7, in other embodiments, especially in motors in which 
the invention is incorporated into the motor when it is built, an analog 
circuit could be coupled to the flux detection coil to reduce the data 
processing required by the computer 36. As shown in FIG. 7, the analog 
circuit would preferably include a high pass filter to reject the power 
supply frequency, a reject filter to reject the rotor bar frequency, and a 
rectifier to compensate for phase shifts. The output of the analog circuit 
could be automatically sampled and synchronously time averaged as the data 
was collected--thereby reducing the data storage requirements of the 
invention and/or allowing time averaging over a larger period of time. 
With the memory requirements reduced in this way, the computer portion of 
the invention could be easily reduced to a dedicated microprocessor 
incorporated into the motor 20. The microprocessor would be programmed to 
periodically test the motor, and to sound an alarm when a suspected fault 
is detected. At such times, a printer could be coupled to the 
microprocessor so that a plot similar to the ones in FIGS. 5b and 6c could 
be generated before further investigation of the motor was undertaken. An 
embodiment of the invention along the lines of the design shown in FIG. 7 
could be produced at extremely low cost. 
An example of an automated fault detection method using the apparatus in 
FIG. 7 is shown in Appendix 2. 
While the present invention has been described with reference to a few 
specific embodiments, the description is illustrative of the invention and 
is not to be construed as limiting the invention. Various modifications 
may occur to those skilled in the art without departing from the true 
spirit and scope of the invention as defined by the appended claims. 
For instance, as will be understood by those skilled in the art, there are 
a number of devices which could perform the same signal pick up function 
as the coil used in the preferred embodiment. Such flux detection devices 
include hall effect sensors and magnetoresistance sensors. 
APPENDIX 1 
______________________________________ 
Synchronous Time Averaging Example for First Embodiment 
______________________________________ 
The filtered data was organized in two arrays: 
DATA: d1 d2 d3 d4 . . . 
dx1 dx2 . . . 
dy1 dy2 . . . 
REV: 1 0 0 0 . . . 
1 0 . . . 
10. . . 
where each nonzero REV value indicates the beginning of a new 
data subset. An average was computed by adding corresponding 
data items using the following algorithm: 
i = 1 
j = 1 
Do Until End Of DATA 
If REV(j) = 1 Then i = 1 
Ave(i) = DATA(j) + Ave(i) 
Increment i and j 
End 
As will be understood by those skilled in the art, it was not 
necessary to divide the summed data to obtain an "average" 
because the data is essentially unitless and thus the scaling 
of the data is arbitrary. 
______________________________________ 
APPENDIX 2 
______________________________________ 
Synchronous Time Averaging Example for Second Embodiment 
______________________________________ 
** Note: data prefiltered by analog circuitry. 
Begin execution here once every X hours 
failtest = 0 
REPT: i = 1 *** index for rotor position 
loop = 1 *** revolution counter 
maxloop = 91 
setflag = .F. 
Wait Until REV = 0 
*** data collection begins 
Wait Until REV = 1 
*** at REV up-transition 
Do Until loop = maxloop 
If REV = 0 Then setflag = .T. 
If (REV = 1 .and. setflag) 
i = 1 
loop = loop + 1 
setflag = .F. 
End 
AVE(i) = AVE(i) + datum from flux detector 
i = i + 1 
End ** end of loop 
Compute: Mean = average AVE values 
.sup. Max = maximum AVE value 
.sup. Sigma = standard deviation of AVE values 
.sup. Alarm = Max - 3*Sigma - Mean 
If Alarm &gt; 0 Then failtest = failtest + 1 
If failtest = 1 Then GOTO REPT 
If failtest &gt; 1 Then Sound-Suspected-Fault-Alarm 
______________________________________