Proceedings of the 14th Mechatronics Forum International Conference, Mechatronics 2014
Eds. Leo J De Vin and Jorge Solis
Winding fault diagnosis of a 3-phase induction
motor powered by frequency-inverter drive using
the current and voltage signals
Agusmian P. Ompusunggu∗ , Zongchang Liu† , Hossein D. Ardakani† , Chao Jin† , Frederik Petré∗ and, Jay Lee†
∗ Flanders’
Mechatronics Technology Centre (FMTC)
Heverlee, 3001, Belgium
Email: agusmian.ompusunggu@fmtc.be
† NSF
Abstract— Three-phase induction motors are critical devices
in many engineering areas including high-speed train, aerospace,
electric vehicles, industrial robots, machine tools, etc. Nowadays,
there is an increasing need of a condition monitoring and prognostic system for induction motors to maintain the availability
of systems equipped with induction motors. This paper focuses
on the development of a winding fault diagnosis method using
3-phase current and voltage signals. To conduct this study, an
induction motor test-bed was developed and constructed. One
of the most difficult insulation faults to detect, namely interturn fault, was induced in the motor under three different
severity levels. A number of experiments were carried out under
different operating regimes, comprising both healthy and faulty
states. Two on-line winding fault detection techniques including
(i) negative impedance and (ii) voltage mismatch detector have
been implemented to develop an early fault detection scheme
for stator winding insulation of an induction motor powered by
a frequency-inverter drive (i.e. controller). In order to remove
the effect of pulse-width modulation (PWM) caused by such a
drive mainly on the voltage signals, an integration based signal
processing method is proposed to demodulate 3-phase output
voltage signals measured on the drive. While, a simple lowpass filtering is applied to the measured 3-phase output current
signals. The integration of the proposed signal processing method
along with the on-line monitoring techniques provided a robust
approach capable of detecting motor’s winding insulation (interturn) faults in early stages.
I. I NTRODUCTION
Three-phase induction motors have applications in many
engineering areas including high speed trains, aerospace, electric vehicles, robotics, machine tools, etc. Despite reliable and
matured devices, failures owing to the thermal, electrical and
mechanical stresses are inevitable. As induction motors play
a vital function in such applications, an unexpected failure
occurring in these devices can thus lead to an unscheduled
total breakdown. This undesirable situation can: (i) put human
safety at risk and (ii) possibly cause long-term downtimes, that
eventually result in high maintenance costs and lost production
(i.e. loss of financial income).
Condition Based Maintenance (CBM) strategy, which is also
known as Predictive Maintenance (PdM), has been proven
∗ Corresponding author
I/UCRC Center for Intelligent Maintenance Systems (IMS)
University of Cincinnati, Cincinnati, OH 45221, USA
Email: liuzc@mail.uc.edu
in modern industries as a maintenance strategy that can
reduce unscheduled breakdown of machines/systems due to
unexpected failures. To realize this strategy in practice, three
key technologies are therefore required, namely (a) condition
monitoring (CM), (b) diagnosis and (c) prognosis. Nowadays,
there is an increasing need for these CBM technologies due
to increasing range of induction motor applications and a
constant awareness of the high impact of their failure.
Fig. 1 shows statistical distribution of common failure
modes typically observed in induction motors. As shown in
the figure, rolling-element bearing and winding failures due to
insulation degradation are the primary causes of unexpected
breakdown in induction motors. Because of wide applications
of rolling-element bearings in almost all of rotating machinery,
many CM, diagnosis and prognosis technologies for such
bearings have been developed since the last four decades
and widely published in literature. However, the amount of
research in CM, diagnosis and prognosis of the winding faults
remains limited.
12%
10%
40%
38%
Bearing faults
Stator winding faults
Rotor faults
Other faults
Fig. 1.
Statistics of failure modes in induction motors, adapted from [1]
Winding faults due to insulation degradation can be classified into four types [2], namely (i) inter-turn short of same
phase, (ii) short between coils of same phase, (iii) short
between two phases and (iv) short between phase to earth.
Among these fault modes, inter-turn fault has been considered
as the most challenging winding fault to detect in induction
Proceedings of the 14th Mechatronics Forum International Conference, Mechatronics 2014
motors. Several off-line techniques with invasive and nondestructive testing approaches, such as (1) resistance test, (2)
DC High-Potential (Hi-Pot) test and (3) surge test, have been
developed and widely used in industry for monitoring and
detecting winding faults in induction motors [3]. To perform
such non-destructive testings, a dedicated equipment is needed.
Unfortunately, such an equipment commercially available on
the market is quite expensive (approximately ¤30,000 per
unit) that can hamper realization of CBM strategy on induction
motors. Another drawback of these off-line techniques is that
the actual winding condition of a motor cannot be assessed
while the motor is in service.
On-line techniques based on low-cost hardwares and lesscomplex signal processing for realizing CBM strategy on
induction motors have long been desired by industry. Two
promising on-line techniques using 3-phase current and voltage signals have been proposed since the last decade ([4],
[5]). These techniques assume that an induction motor is
directly connected to a 3-phase electric line, implying that
the measured 3-phase voltage and current signals are sinusoidal time waveforms. However, an induction motor in many
applications is equipped with a frequency-inverter drive (i.e.
variable frequency drive) as the controller, which typically
uses pulse-width modulation (PWM) methods to control the
3-phase voltages in order to maintain the output current and
power within the boundary of reference. Consequently, these
two existing on-line monitoring techniques are not readily
applicable to such applications. To remedy this gap, this paper
proposes an improvement on the monitoring techniques, where
the 3-phase PWM voltage signals are preprocessed prior to
applying the techniques.
The remainder of this paper is organized as follows. Section II discusses the theoretical background of the two on-line
techniques and the signal processing applied to the 3-phase
current and voltage signals. Section III briefly discusses the
experimental setup and the test procedure for data generation.
Section IV demonstrates the effectiveness of the improved online monitoring techniques through the experimental data analyses. Section V summarizes some important findings obtained
in this study.
II. T HEORETICAL BACKGROUND
A. Winding Faults Characteristics and Negative-Sequence
Impedance Detector
The concept of symmetrical components proposed by
Fortescue in 1918 [6] is a mathematical representation to
describe unbalanced power systems. It suggests that any set
of unbalanced voltages or currents can be transformed into
three sets of symmetrical balanced phases, namely zero-,
positive- and negative-sequence components. Let Vu , Vv and
Vw be 3-phase voltages. Their symmetrical components can
be calculated as:
⎡ ⎤
⎤⎡ ⎤
⎡
V0
Vu
1 1
1
1
⎣ Vp ⎦ = ⎣ 1 α α 2 ⎦ ⎣ Vv ⎦ ,
(1)
3
1 α2 α
Vw
Vn
Eds. Leo J De Vin and Jorge Solis
with V0 , Vp and Vn being the zero-, positive- and negative√
sequence components respectively, α = ej2π/3 and j = −1
being the imaginary unit.
The symmetrical components of 3-phase currents, namely
I0 , Ip and In , can also be calculated in the same mathematical
approach. Under a healthy state, the positive- and negativesequence current should be balanced while the zero-sequence
current should be barely observable. When turn-to-earth short
circuit exists, the zero-sequence current will not be zero anymore [7]. The imbalance of positive- and negative-sequence
currents however, may not be due to actual motor faults. As
explained in ([8], [9]), the imbalance of 3-phase voltage supply
can also cause imbalance currents in a healthy motor. Hence,
when using sequence current as an indicator for motor winding
fault, it is necessary to distinguish the effect of imbalanced
voltage supply and injected current (fault current) [10].
The sequence impedance can be simply calculated as the
ratio between the sequence voltage and the corresponding
current. In ([10], [11]), some approaches were proposed to
separate the negative-sequence current caused by supply voltage imbalance and the current arising from the motor stator
winding fault based on the calculation of sequence impedance
under healthy condition. However, another difficulty is that
the sequence impedance is not always constant. Factors such
as speed, load, and temperature can cause great considerable
variations on sequence impedance [12].
The magnitude of negative-sequence impedance itself can
be seen as an indicator of stator winding fault. Under a healthy
state, the negative-sequence impedance Zhn is given by:
Zhn =
Vn
Vnr + jVni
=
= Rhn + jXhn .
In
Inr + jIni
(2)
If a short circuit exists in stator windings (i.e. faulty state),
the negative-sequence current rise (hereafter called the fault
current) Im is present and the equivalent negative-sequence
impedance Zf n is given by
Vn
Vnr + jVni
= Rf n +jXf n .
=
In + I m
(Inr + jIni ) + (Imr + jImi )
(3)
For quantifying the severity level of winding faults, the ratio
between the negative-sequence impedance of a healthy and
faulty state, being denoted as k, can be considered as the
severity factor
Zf n =
k=
Zhn
Imr + jImi
=1+
.
Zf n
Inr + jIni
(4)
When the severity level of winding fault increases, the fault
current Im will increase, thus making Zf n smaller as revealed
in Eq. (3) and k larger as shown in Eq. (4). Experimental
observations in [5] also suggest similar conclusions.
B. Voltage Mismatch Detector
In order to predict the incipient winding faults in both
balanced and unbalanced systems, a method called voltage
mismatch approach was proposed by Sottile et al ([13],
[4]). This method defines certain impedance parameters and
Proceedings of the 14th Mechatronics Forum International Conference, Mechatronics 2014
performs a training stage to define a baseline for them. At
each speed regime, a baseline is defined and the behavior of
the motor would then be compared to the baseline. Since the
baseline represent the healthy condition of a motor with any
possible asymmetry in its structure, the deteriorations in the
winding insulation of the motor will not be covered by the
asymmetries in the structure of the motor.
Suppose that V0 , Vp and Vn are respectively the zero-,
positive- and negative-sequence components of the motor 3phase voltages, while I0 , Ip and In are respectively the zero, positive- and negative-sequence components of the motor
3-phase currents. The following set of equations can be
constructed according to [4]:
⎡ ⎤ ⎡
⎤⎡ ⎤
V0
z00 z01 z02
I0
⎣ Vp ⎦ = ⎣z10 z11 z12 ⎦ ⎣ Ip ⎦ ,
(5)
Vn
z20 z21 z22
In
where zxy are the parameters representing the motor’s design
and construction.
If no turn-to-earth fault is present, the summation of the 3phase currents will be zero according to Kirchhoff’s law. Consequently, the zero-sequence current I0 and the corresponding
voltage V0 will be zero. Under this assumption, Eq. (5) can
be simplified as follows [14]:
Vp = z11 Ip + z12 In ,
Vn = z21 Ip + z22 In .
(6)
In [13], it was revealed that the load does not have much
influence on winding impedance. For different speeds, current
and voltage in 3-phases are used to calculate the z-coefficients.
So a library of z-coefficients will be made from measurements
in different speed regimes. If the condition of the motor
changes, the stored z-coefficients will no longer be correct
for these equations. In such a case, the calculated Vp and Vn
will be different from their measured value. The difference
between the calculated and measured positive- and negativesequence voltages can thus be seen as an indicator of winding
faults in the motor. This indicator can be further quantified as
the Square Prediction Error (SP E) defined in the following
equation:
N
1 2
SP E =
[Vi − Vl ] ,
(7)
N i=1
where Vi denotes either the positive- or negative-sequence
voltage at an arbitrary state and Vl denotes the corresponding
voltage at a reference state.
The flowchart shown in Fig. 2 schematically summarizes
how the voltage mismatch detector works ([13], [14], [4]).
Yes
0
I0
Ground
Fault Exists
Baseline Data
from Different
Speeds
I0
Calculate
No
Vp
z 11 I p z 12 I n
Vn
z 21 I p z 22 I n
Electric Motor
Test
Data
Obtain Library of zxy
for Different Speeds
Calculate Vp and Vn
Using zxy Library
Turn to Turn
or Inter-turn
Faults Exist
Yes
Voltage
Mismatch
Detect?
Yes
I0 0
Directly Calculate
Vp and Vn
No
No Winding
Insulation
Fault
Fig. 2.
Flowchart of the voltage mismatch detector
signals. In order to retrieve the original waveforms, the 3phase voltage signals need to be demodulated, while a lowpass filtering can be simply applied to the 3-phase current
signals.
The demodulation of such PWM signals is commonly done
in three stages: Firstly convert the PWM signal to pulse amplitude modulation (PAM) signal with an integrator, secondly
apply a band-pass filter to the PAM signal and thirdly adjust
the amplitude of the resulting signal by multiplication with
the shaft rotational speed. Note that the frequency band of the
band-pass filter is made adaptive to the shaft rotational speed
as the center of the frequency band, with the bandwidth of
20 Hz. For low-pass filtering on the 3-phase current signals,
the cut-off frequency is also adjusted with respect to the
shaft rotational speed. In this study, the low-pass cut-off
frequency was set to 10 Hz above the shaft rotational speed.
The flowchart for signal processing is shown in Fig. 3. The
comparison of 3-phase voltage and current signals before and
after signal-processing are shown in Fig. 4.
PWM Voltage Signal
Demodulation
Integrator
C. Signal Processing
As the PWM technique used in a variable frequency drive
(VFD) affects the 3-phase output current and voltage signals,
these signals therefore need to be preprocessed prior to applying the two aforementioned techniques. In practice, PWM
time-waveforms are predominantly pronounced in the 3-phase
voltage signals, but less pronounced in the 3-phase current
Eds. Leo J De Vin and Jorge Solis
Rotational
speed
Current Signal
Band-pass
Filter
Low-pass
Filter
Multiplier
Filtered current
signal
Demodulated voltage
signal
Fig. 3.
Flowchart of signal processing
Proceedings of the 14th Mechatronics Forum International Conference, Mechatronics 2014
(a)
0
−10
0.2
(b)
U−Phase
V−Phase
W−phase
0.25
(c)
0.3
H(x) =
0.25
0.3
0.25
(d)
0.3
Time (s)
0.25
0.3
Time (s)
Fig. 4. Signals comparison before and after processing: (a) Original current
signal, (b) Original PWM voltage signal, (c) Low-pass filtered current signal,
(d) Demodulated voltage signal.
D. Statistical Pattern Recognition for Health Assessment
Statistical pattern recognition belongs to one of the many
approaches for machine health assessment. The objective of
applying pattern recognition is to determine the health status
by measuring the similarity between the current (arbitrary)
state features and the healthy state features. As the name
suggests, the similarity metric is quantified by calculating the
overlap between statistical distributions of features from both
healthy and faulty states. When feature values are normally
distributed, this overlap can be simply represented by the L2
distance. Yet when the feature distributions are non-normal,
which is usually the case encountered in practice, a Gaussian
Mixture Model (GMM) would be appropriate [15].
The GMM approach assumes that the non-normal feature
data are generated from several different hidden sources, which
can be described by Gaussian probability density functions
with certain weights. Attributing to the virtue of normal distribution, the weighted component Gaussian functions having
different means and variances are additive, and the resulting
model is referred to as a ”mixture model”. Hence the feature
values extracted under healthy states will be utilized to build
a GMM model as a reference. If there are no faulty condition
data available, another GMM model will be obtained from
unsupervised learning on new feature values, and the distance
between the current state and the healthy state can thus be
measured using the L2 distance. The confidence value (CV ),
which represents the normalized overlap between the two
distributions, can be calculated according to Eq. (8) based on
the L2 distance:
CV =
pi h(x, θi ),
(9)
where h(x, θ) is called ”mixture”, namely the component
probability density function (pdf) with parameter θi for signal
x, and pi is the ”mixture weight”, namely the probability that
observation comes from that component. G(x) can also be
calculated by following the same fashion.
After a threshold is set up based on expert knowledge and
experience, the health state can thus be determined. If the
faulty condition data are available, supervised learning can be
performed, and the current machine health will be determined
by choosing the ”closest” feature space [16].
0
−100
0.2
N
i=1
100
0
−10
0.2
arbitrary state. H(x) can be calculated based on Eq. (9):
U−Phase
V−Phase
W−phase
0
−500
0.2
Voltage (V)
Current (A)
10
500
Voltage (V)
Current (A)
10
Eds. Leo J De Vin and Jorge Solis
H(x) ∗ G(x)L2
,
H(x))L2 ∗ G(x))L2
(8)
where H(x) is the density estimation for a mixture distribution
of the data collected under healthy state, and G(x) is that under
III. E XPERIMENTAL M ETHODOLOGY
For experimental validation purposes, a dedicated induction
motor setup has been developed and constructed. With this test
setup, one is able to simulate the motor either in a healthy or
faulty state, where two different types of winding fault namely
(i) inter-turn and (ii) turn-to-earth faults can be induced. Note
that turn-to-earth fault has not been considered in this study
since it is much easier to detect. Besides, one can also easily
control the rotational speed and the load applied to the motor
such that experiments at different operating conditions can
be realized. In the following sections, the test setup, the
procedures of inducing winding faults and the experiments
are described.
A. Test setup
Fig. 6 shows the photograph and the schematic view of the
test setup. The setup consists of a 11kW-19.7A-400V-3-phase
induction motor driven by a variable frequency drive (VFD).
The shaft rotational speed of the motor can be varied from
0 to 3000 rpm with either a stationary mode or a transient
mode (run-up/run-down). The motor shaft is connected to the
shaft of a magnetic brake through a timing-belt and pulley
mechanism, where the transmission ratio of 2 was chosen such
that the rotational speed of the brake shaft is two times lower
than that of the motor shaft. An external load (i.e. torque) to
be applied to the motor can be varied from 0 to 50 Nm by
controlling the input current to the brake. The control signals
to the VFD and to the brake controller are sent out by a PC
using dedicated Labview programs. A variable resistor with
the resistance range of 0 - 580 Ω was used for inter-turn fault
simulations as will be discussed in the following section.
The 3-phase voltages and currents generated by the VFD
are measured respectively by high frequency band-width 3phase current and voltage probes. The signals captured by
the probes are conditioned by the corresponding signal conditioning module. A tachometer based on a proximity probe is
used to measure the shaft rotational speed of the motor where
the probe head is directed to a 4-tooth flywheel attached on
the motor shaft, thus generating 4 pulse signal per revolution.
To measure the actual torque applied to the motor, a torque
sensor is mounted on the brake shaft. All the signals are
Proceedings of the 14th Mechatronics Forum International Conference, Mechatronics 2014
Eds. Leo J De Vin and Jorge Solis
Torque sensor
Timing belt
Variable
frequency drive
Motor
under test
NI Compact
DAQ
Test Motor
3-phase current 3-phase voltage
probes
probes
Signal
conditioning
Signal
conditioning
Tachometer
Triaxial
accelerometer
NI Compact
DAQ
Magnetic
brake
Variable
resistor
Magnetic
Brake
Geared pulleys
Variable
frequency drive
(a)
Signal
conditioning
Controller
PC
(b)
Fig. 5.
Experimental setup: (a) photograph and (b) schematic view
synchronously acquired by a National Instruments (NI) data
acquisition system and then stored to the PC with a Labview
program.
B. Fault Simulation and Test Procedure
The stator winding of the motor used in this study is random
wound, which means that there are no conductive bars with
exact defined location within stator slots, as shown in Fig. 6(a).
For this study, the test motor has been modified as follows.
Three shielded wires (1, 2 and 3) are connected to the coil
of the phase w stator winding at different locations and the
other ends of the wires are brought outside as schematically
illustrated in Fig. 6(b). This way, different scenarios of interturn fault can be simulated by means of connecting two of the
shielded wires with a resistor.
1) Healthy State Simulation: To simulate a healthy state,
the floating ends of the three shielded wires shown in Fig. 6(b)
are kept unconnected.
2) Faulty State Simulation: In this study, fault simulations
were carried out under two different scenarios, referred to as
inter-turn fault I and II, as follows. To simulate inter-turn fault
I, wire 1 (in orange) was shorted to wire 2 (in green) through
a variable resistor as illustrated in Fig. 6(b). In similar way,
wire 1 was shorted to wire 3 (in black) to simulate inter-turn
fault II. Three different severity levels have been considered
in this study for each scenario. These levels were adjusted by
changing the resistance value of the variable resistor, namely
580, 300 and 50 Ω, as summarized in Table I.
3) Test Procedure: The motor was operated at a constant
speed of 3000 rpm and a constant brake torque of 12 Nm for
each condition. At an imposed degradation level (F1, F2, or
F3), the current il flowing through the variable resistor was
also measured and the corresponding dissipated power Pd was
calculated as listed in Table II.
TABLE II
C URRENTS AND DISSIPATED POWER THROUGH THE VARIABLE RESISTOR
AT DIFFERENT STATES .
State
F1
F2
F3
Inter-turn I
il [mA] Pd [W]
265
297
1126
40.7
26.5
63.4
Inter-turn II
il [mA] Pd [W]
86
155
990
4.3
7.2
49.0
Prior to signal digitizing, each measured signal was lowpass filtered with an anti-aliasing filter embedded in each
channel of the used NI data acquisition system. This way,
potential aliasing problems resulting from high frequency
noise can be avoided. With this data acquisition system, the
cut-off frequency of the anti-aliasing filter is automatically
selected depending on the used sampling frequency. Later on,
the filtered signals were sampled at 102.4 kHz with a duration
of 4 seconds. Finally, the digital data were stored in the PC
and then processed off-line with dedicated Matlab programs
as will be discussed in the next section.
IV. R ESULTS AND D ISCUSSION
TABLE I
D IFFERENT DEGRADATION LEVELS INDUCED IN THE MOTOR .
State
Resistance [Ω]
Comment
F1
F2
F3
580
300
50
Lowest level
Moderate level
Most severe level
A. Negative-Sequence Impedance Detection
The magnitudes of the negative-sequence impedance have
been calculated based on the method discussed in Section II
for each inter-turn fault scenario in both healthy and faulty
states under a constant operating speed of 3000 rpm and
constant brake torque of 12 Nm. Fig. 7 shows the distributions
of the negative-sequence impedance for all conditions. The
Count
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Count
Proceedings of the 14th Mechatronics Forum International Conference, Mechatronics 2014
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Eds. Leo J De Vin and Jorge Solis
(i)
4
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0
5.6 4 5.7
x 10
4
5.8
5.9
5.8
5.9
(ii)
6
6.1
6.2
6.3
6
6.1
6.2
6.3
6
6.1
6.2
6.3
6
6.1
6.2
6.3
6
6.1
6.2
6.3
6
6.1
6.2
6.3
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6.2
6.3
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6.2
6.3
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6.1
6.2
6.3
2
0
5.6 4 5.7
x 10
4
(iii)
2
0
5.6
5.7
5.8
5.9
Impedance (Ohm)
(a)
Count
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1
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resistor, R
4
x 10
2
0
5.6 4 5.7
x 10
2
B. Voltage Mismatch Detection
A linear regression model was built for each speed regime
using data collected from the healthy state. The regression
coefficients were stored for each speed regime to build the
library of z-coefficients. Then, those coefficients were used
to calculate the positive- and negative-sequence voltages by
Eq. (6) using data with the three conditions: healthy, interturn fault I and inter-turn fault II. Meanwhile, the voltage
symmetrical components are calculated from the demodulated
phase voltages according to Eq. (1).
5.8
5.9
(ii)
0
5.6 4 5.7
x 10
2
(iii)
1
0
5.6
5.7
5.8
5.9
(b)
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confidence value representing the health condition of the motor
for different states is listed in Table III.
It can be observed from the figure above that the magnitude
of the negative-sequence impedance for healthy condition is
larger than that for inter-turn fault I and inter-turn fault II. It is
also obvious that the highest severity level F3 is more easily
detectable than the other two fault levels. This observation
is also reflected by the calculated confidence value (CV )
which is much smaller than that of the other levels (F1 and
F2). As the fault severity level increases, the distribution is
more shifted to the left indicating that the calculated negativesequence impedance decreases.
5.9
Impedance (Ohm)
Count
Fig. 6. (a) Photograph of the disassembled motor exposing random wound
stator winding, and (b) the schematic winding diagram with three taps on the
phase w winding for different inter-turn fault scenarios.
5.8
1
v
(b)
(i)
4
4
x 10
(i)
4
2
0
5.6 4 5.7
x 10
2
5.8
5.9
5.8
5.9
(ii)
1
0
5.6 4 5.7
x 10
4
(iii)
2
0
5.6
5.7
5.8
5.9
Impedance (Ohm)
(c)
Fig. 7. Negative-sequence impedance distributions for (a) lowest severity
level F1, (b) moderate severity level F2 and (c) highest severity level F3.
Note that (i) represents healthy state, (ii) represents inter-turn fault I, (iii)
represents inter-turn fault II.
Fig. 8 shows the histogram of the residuals of the negativesequence voltage calculated from the data collected in different
states (including healthy, F1, F2 and F3). As shown in the
figure, there is a clear difference between the distributions
of healthy and the faulty states. The residual voltage of the
healthy state exhibits a zero-mean Gaussian distribution, which
is due to the uncertainties of the modeling process. However,
the residual of the faulty states are more likely a combination
of two distributions with larger variances, which is expected
due to the change of the z-coefficients. As the fault severity
level increases, the variances of the two (joined) distributions
Proceedings of the 14th Mechatronics Forum International Conference, Mechatronics 2014
Eds. Leo J De Vin and Jorge Solis
TABLE III
become larger.
C ONFIDENCE VALUE (CV)
FOR IMPEDANCE DISTRIBUTION AND
P REDICTION E RROR (SPE)
(i)
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−15
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(iii)
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−10
−5
0
5
10
15
20
1
0
2
1
0
−15
Residual Voltage (V)
(b)
(i)
4
Count
CV
SP E
≈1
2.55
Inter-turn I
F2
F3
0.85
3.23
0.72
3.55
0.06
6.12
F1
Inter-turn II
F2
F3
0.16
4.32
0.15
5.06
4e-7
11.6
1
(a)
Count
Healthy
F1
Residual Voltage (V)
Count
S QUARE
FOR VOLTAGE MISMATCH DISTRIBUTION .
4
x 10
2
0
2
−20 4 −15
x 10
−10
−5
0
(ii)
5
10
15
20
−20 4 −15
x 10
−10
−5
0
(iii)
5
10
15
20
1
0
2
ACKNOWLEDGMENT
1
0
−20
−15
−10
−5
0
This paper presents a framework for early fault detection
and diagnosis of stator winding fault (i.e. inter-turn fault) for
3-phase induction motors using two established on-line methods, i.e. negative-sequence impedance and voltage mismatch,
which are all based on the symmetrical components. The
two methods have been modified in this study by introducing
an additional signal processing step for removing the effects
of pulse-width modulation (PWM) on 3-phase voltage and
current signals. This step is necessary for the 3-phase current
and voltage signals collected from induction motors equipped
with variable-frequency drive (VFD), prior to calculating the
symmetrical components. The experimental results show obvious distinctions between healthy and faulty states using both
modified negative-sequence impedance and voltage mismatch
approaches. The L2 distance between the distributions of the
negative-sequence impedance is proposed as an indicator that
can easily capture the mean shift and variance change, while
Square Prediction Error (SPE) is proposed as an indicator to
quantify the voltage mismatch.
Both the modified negative-sequence impedance and voltage
mismatch detectors show encouraging results for winding fault
detection even for incipient faults. It should be noted that
both modified detection methods need a library of references
under different environmental and working conditions, since
the experimental results suggest that the modeling of motor
windings do vary in different conditions.
5
10
15
20
Residual Voltage (V)
(c)
Fig. 8. Residual voltage distributions for (a) lowest severity level F1, (b)
moderate severity level F2 and (c) highest severity level F3. Note that (i)
represents healthy state, (ii) represents inter-turn fault I, (iii) represents interturn fault II.
V. C ONCLUSION
Stator winding faults damage the symmetry of the winding
resistance and hence cause unbalanced sequence currents. As
a result, the equivalent sequence impedance will drop if short
circuits exist (i.e. winding faults) in any phase of the windings.
Based on this reasoning, the symmetrical components of 3phase currents and voltages can be used as effective tools for
winding fault detection in early stages.
The authors would like to thank Aaron Léon Hernández for
setting up and performing the experiments during his internship at Flanders’ Mechatronics Technology Centre (FMTC),
Belgium.
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