IGBT

1. Data-based inverter IGBT open-circuit fault diagnosis in vector
control induction motor drives
Chengguang Huang Jin Zhao Chaorong Wu
Dept. of control science and engineering
Huazhong University of Science and Technology
Wuhan, China
hcgcool@163.com jinzhao617@163.com Wcr_zq@hotmail.com
Abstract—This study describes a method of diagnosis for IGBT
open-circuit faults in vector control induction motor drives. This
method combines the wavelet analysis and the support vector
machines (SVM). The wavelet analysis is used to extract fault
characteristics, and the SVM is used to isolate fault modes. This
method is insensitive to the motor speed and load. The results of
Matlab simulation analysis shows that this method can effectively
carry out the fault diagnosis for IGBT open-circuit faults in
vector control induction motor drives.
Keywords- fault diagnosis; wavelet analysis; support vector
machine
I. INTRODUCTION
In most of induction motor speed closed-loop systems, the
power source is supplied by a voltage-source inverter. The
inverter executes the PWM strategy through the IGBTs’
switching on and off. Therefore, the inverter is am important
component of the induction motor speed closed-loop system.
Meanwhile, repeated impact of energy during the IGBTs’
switching on and off makes the inverter more prone to failure
in the whole system. Fault diagnosis in inverter is able to
avoid the further development of the faults and damage to
other components. In addition, faults isolation can provide a
basis for fault-tolerant solution and maintenance guidance
when a fault occurs.
Generally, the faults types of Inverter’s IGBTs can be
classified into short-circuit faults and open-circuit faults [1].
The inverter has a protection circuit for IGBTs short-circuit
fault, therefore, an additional fault detection technique is
unnecessary. This paper only investigates single open-circuit
faults in IGBTs.
Currently, three methods are mainly used in fault diagnosis:
model-based, artificial intelligence and data-based methods.
There are mainly three approaches used in model-based
method, including current vector model, voltage analytical
model, and observer model. In [2], a technique was introduced
to detect the intermittent misfiring of the switching devices in
a voltage-fed PWM inverter, based on the time-domain
response analysis of the induction motor current space vector.
In [3], two approaches were proposed to detect and isolate
faults in a pulse-width modulation inverter of synchronous
machine. The first one is based on the analysis of the current-
vector trajectory. The second one, which is similar to the first
method, estimates the instantaneous frequency of the current
vector to detect the faults. In [4], the proposed two approaches
are based on the Concordia stator mean current pattern to
diagnose IGBT open-circuit faults in high-power case and
low-power case, respectively. In [5], the technique requires the
measurement of voltages and is based on the analytical model
of the voltage source inverter. The errors of voltages are used
to detect faults in inverter. This technique presents easier
implementation and faster detection, but needs to incorporate
extra voltage sensors in the system. In [10], an observer-based
diagnosis technique for IGBT open-circuit faults in induction
motor driver was proposed. The decoupled currents, which are
in the rotating coordinate of the induction motor system, are
estimated by the observer of the healthy induction motor
system. a directional residual evaluation is obtained and
compare with an evaluation threshold to detect faults. Model-
based method depends on the accuracy of the model. But in
induction motor system, an accurate model of the whole
system is hard to obtain due to parameters of the motor system
are not permanent in different states or environment.
Therefore, artificial intelligence and data-based methods that
do not require accurate model are of great interest. In [6, 9],
the authors presented a fault diagnostic using signal analysis
and machine learning for inverter. This technique needs to
extract the currents, voltages and torque as the characteristics
for training the fault diagnostic neural network (FDNN). Then,
the FDNN is used to detect and isolate the faults in the inverter.
In [7], an approach based on the load current analysis and
fuzzy logic was presented for the detection and isolation of
IGBT open-circuit faults in inverter. In [11], a technique based
on wavelet decomposition was described for detection and
identification of IGBT open-circuit faults in direct torque
control (DTC) induction motor driver. Artificial intelligence
method mostly depends on the intelligence arithmetic but
ignores the research of fault characteristics. Data-based
method focuses on the time-frequency analysis of the signals
to extract the fault characteristics. But, the fault isolation
requires an aid of other technique. Hence, fault diagnosis
technologies combined with a variety of methods will be a
developing direction, such as the literature [4, 8], which
combine model-based method with artificial intelligence.
Existing diagnostic techniques are mostly dependent on the
characteristics of the current signals. And characteristics of the
current signals are more complex in vector control motor
system than currents open-loop motor system due to the
currents closed-loop regulator. This paper presents a data-
based diagnosis technique for the IGBT open-circuit faults in
1039978-1-4673-6322-8/13/$31.00 c 2013 IEEE

2. the inverter for the induction motor vector control system. In
this method, wavelet decomposition technique is used to
extract the fault characteristics of the three-phase currents.
And the SVM are used to isolate the fault modes. Different
from the existing techniques, the proposed approach can
extract the characteristics of the complex change currents in
vector control motor system with insensitivity to speed and
load. And no extra sensors are need in this approach.
II. SYSTEM INTRODUCTION AND FAULTS ANALYSIS
A. System introduction
In this paper, the control strategy of the induction motor
closed-loop control system is vector control. Vector control
appeared in the 1970s, its purpose is to make the induction
motor can be controlled as a separately excited DC motor, to
achieve a high performance control for the induction motor.
According to the calculation of the unit vectors, vector control
can be divided into the direct vector control and indirect vector
control. The unit vectors are obtained through the estimation
of the flux vector by the voltage model or current model in
direct vector control. However, the acquisition of the unit
vectors is calculated by the integral for the speed and slip. The
latter is more popular in industrial applications. The induction
motor control system in this article is used indirect vector
control, and system structure diagram as shown in Fig. 1.
e
*
di
*
qi
ai
bi
ci
i
i
di
qi
*
qu
*
du
*
u
*
u
r
*
r
Figure 1 ram of system structure. Diag
B. Fault analysis
In this paper, only IGBT open-circuit faults are considered
and six fault modes ( to ) are isolated. to
represents open-circuit fault in to , respectively. Fig. 2
shows inverter structure and the fault mode .
1S 6S 1S 6S
1T 6T
1S
dcV
a
b
c
1T 3T 5T
2T 4T 6T
1S
Figure 2. Inverter structure and the fault mode 1S
The object of this study is a vector control induction motor
system belonging to the current closed-loop system. Because
of the current closed-loop regulator, the three-phase currents
in current closed-loop system are very different from that in
current open-loop system, when the fault occurs. In current
open-loop system, the given voltages u and u are the
active sine values, which does not change with the emergence
of the fault. Therefore, the faulty phase current is sine half-
wave and the other two healthy phases currents are still sine
wave, when the fault occurs. Fig. 3 shows the waveform of the
given voltages and the three-phase currents in current open-
loop V/f system, when the open-circuit fault occurs.1T
1.35 1.4 1.45 1.5 1.55 1.6 1.65 1.7 1.75
-100
0
100
ualfa
1.35 1.4 1.45 1.5 1.55 1.6 1.65 1.7 1.75
-100
0
100
ubeta
1.35 1.4 1.45 1.5 1.55 1.6 1.65 1.7 1.75
-10
0
10
ia
1.35 1.4 1.45 1.5 1.55 1.6 1.65 1.7 1.75
-10
0
10
ib
1.35 1.4 1.45 1.5 1.55 1.6 1.65 1.7 1.75
-10
0
10
ic
Figure 3. Waveform of the given voltages and the three-phase currents
The open-circuit fault occurs at 1.5s and the system
operating point is at 500 red/min and no-lord. In vector control
system, the given voltages u
1T
and u can not retain the sine
values, when the fault occurs. When the open-circuit fault
occurs, phase a current loses most of positive part. The state
current space vector trajectory is no longer the arc in this
section, which shown by dotted lines in Fig. 4.
1T
a
c
b
o
seccurrent lack tion
Figure 4. Current-lack section of fault mode 1S
The section is defined as a current-lack section of phase a
positive current. Feedback currents and will drop as
soon as the state current space vector enters into this section.
And the output of regulator and will increase quickly
to counteract the drop until they reach saturation. This process
causes the distortion of the given voltages u
di qi
du qu
and u . When
1040 2013 IEEE 8th Conference on Industrial Electronics and Applications (ICIEA)

3. state current space vector leaves current-lack location of phase
a positive current, and will break away from saturation.
And u
du qu
and u will recover the sine values. In this period of
the state current space vector, the given voltages u and u
are no longer the sine values. Therefore, three-phase currents
dose not retain the sine values, but appear periodicity
distortions. Fig. 5 shows the waveform of the output of
regulator, given voltages and the three-phase currents in space
vector control system, when the open-circuit fault occurs.1T
0.4 0.45 0.5 0.55 0.6 0.65
-300
-200
-100
0
100
200
300
ud
0.4 0.45 0.5 0.55 0.6 0.65
-400
-200
0
200
400
uq
0.4 0.45 0.5 0.55 0.6 0.65
-400
-200
0
200
400
ualfa
0.4 0.45 0.5 0.55 0.6 0.65
-400
-200
0
200
400
ubeta
0.4 0.45 0.5 0.55 0.6 0.65
-10
0
10
ia
0.4 0.45 0.5 0.55 0.6 0.65
-10
0
10
ib
0.4 0.45 0.5 0.55 0.6 0.65
-10
0
10
ic
Figure 5. Waveform of the output of regulator, given voltages and the three-
phase currents
The open-circuit fault occurs at 0.5s and the system
operating point is at 500 red/min and no-lord. The distortions
appear on the three-phase currents along with the state current
space vector enter into current-lack location of faulty phase
current. They can reflect the fault modes of the inverter at a
certain extent. In this paper, wavelet analysis is used to extract
the characteristics of the distortions from the three-phase
currents as the fault characteristic. Then this fault characteristic
will be used for the fault isolation.
1T
III. FAULT DIAGNOSIS
Data-based fault diagnosis methods can be broadly divided
into two parts. The first part, extraction of the characteristics is
enforced by wavelet analysis for the distortions in currents,
and fault characteristics vectors are built; the second part is
isolation of the fault modes, and the characteristics vectors are
used for training the SVM and the trained SVM is used for
isolating the fault modes, meanwhile, the characteristics
vectors can be used for testing the validity of the SVM as a
testing set. Fig. 6 shows the diagnosis process as follows.
Figure 6. Diagnostic process
A. Extraction of the characteristics
Extraction of the fault characteristics is necessary before the
isolation of the faults. An effective fault characteristics
extraction can reflect the characteristics of each type of fault,
moreover, it simplifies the data structures and reduces the
difficulty of the fault isolation. Therefore, extraction of the
fault characteristics is a very important part of the fault
diagnosis. In this paper, wavelet analysis is used to decompose
the three-phase currents signals, and then the fault
characteristics are extracted and constructed as the input
(training set and testing set) of the SVM for faults isolation.
Four-level wavelet decomposition of the three-phase current
signals (nS , ,n a b c represent three phases) by the filter
bank can extract three approximate signals 4,napp
, ,n a b c and three detail signals 4,nd , ,n a b c .
A series of Sharp in the detail signals reflect the distortion of
the original signals, which is cased by the fault, and the
amplitude of the sharp realty the strength of the distortion in
the original signals. Meanwhile, the strengths of each phase
distortion are different in different fault modes. Therefore, the
energy of the sharp in the three-phase detail signals is
extracted to define as a class of fault characteristics, and to
constitute a three-dimensional vector
4,nd
, ,d a b cE E E E ,
where , ,a b cE E E are energy of the sharp in the three phases
detail signals , respectively.4,nd
Vector dE can express the characteristic of the three-phase
currents in fault modes. But the sharp of the detail signals only
represent which phase is failure. The other fault characteristics
is need to determine which IGBT is fault in the phase. When a
fault occurs, due to the fault-phase current is uncontrollable
during half period, the positive energy of the three-phase
currents does not equal the negative. And the polarity which
energy is weaker locates the fault IGBT in the phase.
Therefore, the ratio of the positive energy to the negative in
the approximate signals is defined as
the other fault characteristics. Then a three-dimensional vector
4,napp , ,n a b c
, ,app a b cf f f f is constituted, where the , ,a b cf f f are
ratio in the three phases approximate signals ,
respectively.
4,napp
2013 IEEE 8th Conference on Industrial Electronics and Applications (ICIEA) 1041

4. dE and appf together make up a fault characteristics vector
, , , , ,a b c a b cE E E E f f f . According to above extraction
method, the three-phase currents in fault modes are processed
as the training set and the testing set for the faults isolation in
next part.
B. Isolation of the faults
Fault isolation is a very important part of the fault diagnosis.
In this paper, the support vector machine (SVM) is used to
isolate the different fault modes. SVM is a new machine
learning technique developed on the basis of statistical
learning theory. SVM can solve small sample problems and
has good generalization ability using the principles for
structural risk minimization. Compared with the other machine
learning theories based on the infinite sample hypothesis, the
SVM is more coincident with the actual system which has
only a limited sample.
SVM map inputs vector nonlinearly into a high dimensional
feature space by a kernel function and construct the optimum
separating hyperplane in order to realize sample classification.
SVM is originally introduced under the condition of two kinds
and developed to an effectual means to solve the problems of
many kinds of pattern recognition. In this paper, IGBT open-
circuit faults isolation in the inverter is a multi-classification
problem, so a one-versus-one policy is adopted. In a multi-
fault isolation, 1 2k k classifiers are built when there
are fault modes, and each classifier is only built for two
fault modes, then the voting is used to define the fault mode,
as shown in Fig. 7.
k
1SVM 2SVM lSVM 1 2k k
SVM
... ...
... ...
Figure 7. SVM structure
The kernel function and SVM classification algorithm are
important to the accuracy of the SVM. In this paper, the radial
basis function (RBF) is adopted as kernel function and the C-
SVC algorithm is used for implementation of classification
algorithm. The using SVM is shown in Fig. 8.
Figure 8. Process of fault isolation
According to the above part, the fault characteristics vectors
are extracted from simulation data to form the sample set
,i j
S E , where 1,2,...,6i means six kinds of open-
circuit fault modes and 1,2,..., ij k means sample size
in fault mode i , then the training set and the testing set
are selected from the sample S . Penalty factor c in the
C-SVC algorithm and parameter in the RBF are optimized
by cross-validation. is used to train the SVM, and then
is used to test the accuracy of the trained SVM to
determine whether the method of fault diagnosis is valid.
ik
mS
nS
g
mS
nS
IV. SIMULAITON RESULT
The whole system has been simulated using the Matlab
software. The parameters of the system are given in Table I.
TABLE I PARAMETERS OF THE SYSTEM
Power(P) 5.5kw
Poles(p) 4
Stator leakage inductance(Lls) 0.00386H
Rotor leakage inductance(Llr) 0.00635H
magnetizing inductance(Lm) 0.1024H
Stator resistance(Rs) 0.813
Rotor resistance(Rr) 0.531
Moment of inertia(J) 0.02 .
Supply voltage 50Hz, 380V
Controller sampling period(Tsp) 4
10 s
simulation step size(Ts) 6
10 s
With upper-IGBT T1 open-circuit fault in phase a , for
example. The fault occurred at 0.5 with a 500
and no-load. The three-phase currents are shown in Fig. 9.
s / minrev
0.3 0.35 0.4 0.45 0.5 0.55 0.6 0.65 0.7
-10
0
10
ia
time(s)
current(A)
0.3 0.35 0.4 0.45 0.5 0.55 0.6 0.65 0.7
-10
0
10
ib
time(s)
current(A)
0.3 0.35 0.4 0.45 0.5 0.55 0.6 0.65 0.7
-10
0
10
ic
time(s)
current(A)
Figure 9. T1 open fault, three-phase currents
1042 2013 IEEE 8th Conference on Industrial Electronics and Applications (ICIEA)

5. Fig. 10 , and shows the result of the db4 four-
level wavelet decomposition for the three-phase currents,
respectively.
a b c
0 20 40 60 80 100 120
0
20
40
ap
0 20 40 60 80 100 120
-30
-20
-10
0
an
0 20 40 60 80 100 120
0
20
40
bp
0 20 40 60 80 100 120
-30
-20
-10
0
bn
0 20 40 60 80 100 120
0
10
20
30
cp
0 20 40 60 80 100 120
-40
-20
0
cn
0 500 1000 1500 2000 2500 3000 3500
-10
0
10
s
0 50 100 150 200 250
-50
0
50
a4
0 50 100 150 200 250
-2
0
2
d4
0 50 100 150 200 250 300 350 400 450 500
-1
0
1
d3
0 100 200 300 400 500 600 700 800 900
-0.5
0
0.5
d2
0 200 400 600 800 1000 1200 1400 1600 1800
-1
0
1
d1
(a)
(b)
(c)
Figure 10. Db4 four-level wavelet decomposition for the three-
phase currents
Fig. 11 , and shows the result of sharp
signals in the four-level detail signals, which are extracted
using the method referred in above section.
a b c
0 20 40 60 80 100 120
-5
0
5
(a)
0 20 40 60 80 100 120
-5
0
5
(b)
0 20 40 60 80 100 120
-5
0
5
(c)
Figure 11. Sharp signals of the four-level detail signals
the vector dE can be calculated as 2.606,6.293,6.926 .
Fig. 12 shows the both positive and negative signals of the
four-level approximate signals.
Figure 12. Positive and negative signals of the four-level
approximate signals
pa , are the positive and negative of the approximate
signal in phase a , , and , are in phase and
phase , respectively. The vector
na
pb nb pc nc b
c appf can be calculated as0 500 1000 1500 2000 2500 3000 3500
-10
0
10
s
0 50 100 150 200 250
-50
0
50
a4
0 50 100 150 200 250
-5
0
5
d4
0 50 100 150 200 250 300 350 400 450 500
-2
0
2
d3
0 100 200 300 400 500 600 700 800 900
-2
0
2
d2
0 200 400 600 800 1000 1200 1400 1600 1800
-2
0
2
d1
0.132,1.269,1.352 .
Using the same methods, the sample can be obtained
from different operation status (torque and speed) in Table II,
when the open-circuit fault occurs in T1.
1S
TABLE II THE SAMPLE OF T1 OPEN FAULT CHARACTERISTICS
Operation-
status
/min,rev Nm
aE bE cE af bf cf
500 0 2.606 6.293 6.926 0.132 1.269 1.352
500 5 2.306 5.647 5.638 0.008 1.593 1.105
500 10 3.839 4.927 5.135 0.143 1.386 1.405
550 0 3.494 6.361 6.750 0.221 1.294 1.192
550 5 3.749 7.036 7.008 0.184 1.756 1.055
550 10 1.573 5.266 5.653 0.006 1.571 1.407
600 0 3.486 6.909 7.631 0.145 1.242 1.247
600 5 3.533 6.374 6.39 0.014 1.768 1.066
600 10 3.480 8.559 8.525 0.086 1.454 1.280
650 0 4.572 13.361 13.915 0.226 1.168 1.203
650 5 3.535 7.851 8.086 0.167 1.811 1.021
650 10 2.508 7.717 7.595 0.124 1.335 1.485
700 0 3.481 9.397 9.969 0.104 1.148 1.344
700 5 4.077 8.767 8.955 0.013 1.647 1.106
700 10 3.355 7.761 7.877 0.036 1.310 1.544
750 0 3.773 13.547 14.107 0.099 1.179 1.246
750 5 4.589 9.098 8.507 0.180 1.542 1.240
750 10 2.97 10.408 10.549 0.072 1.397 1.513
800 0 3.142 9.758 9.967 0.104 1.314 1.139
800 5 4.292 9.363 9.886 0.014 1.600 1.283
800 10 2.596 10.403 10.319 0.007 1.330 1.484
850 0 3.155 15.770 15.931 0.075 1.254 1.132
850 5 5.005 15.159 16.065 0.084 1.605 1.238
850 10 4.914 9.852 9.954 0.087 1.270 1.623
900 0 3.162 11.773 11.748 0.255 1.218 1.193
900 5 4.079 11.485 12.089 0.054 1.524 1.290
900 10 3.701 12.250 11.814 0.011 1.302 1.692
950 0 2.395 12.185 12.236 0.029 1.297 1.171
950 5 4.000 13.569 13.814 0.015 1.435 1.515
950 10 3.321 13.583 13.180 0.014 1.239 1.589
1000 0 3.326 14.066 13.614 0.048 1.314 1.107
1000 0 8.091 15.458 13.615 0.207 1.466 1.503
1000 10 5.472 14.060 13.370 0.177 1.239 1.670
0 500 1000 1500 2000 2500 3000 3500
-10
0
10
s
0 50 100 150 200 250
-50
0
50
a4
0 50 100 150 200 250
-5
0
5
d4
0 50 100 150 200 250 300 350 400 450 500
-2
0
2
d3
0 100 200 300 400 500 600 700 800 900
-2
0
2
d2
0 200 400 600 800 1000 1200 1400 1600 1800
-2
0
2
d1
2013 IEEE 8th Conference on Industrial Electronics and Applications (ICIEA) 1043

6. The samples of five other fault modes ( , , , , )
can be obtained respectively in the same way.
2S 3S 4S 5S 6S
Then, part of each sample is selected for the training set
(500, 600, 700, 800, 900 speed with 0, 5 and 10
torque are selected). The others are testing set Penalty
factor in the C-SVC and parameter
in the RBF are obtained by cross-validation implement by
package in Matlab. After the training, 46 support
vectors are obtained, including 10 support vectors for fault
mode 1, 8 for fault mode 2, 7 for fault mode 3, 4 for fault
mode 4, 10 for fault mode 5, and 7 for fault mode 6.
Meanwhile, 15 classification function are obtain as follows
/ minrev
Nm
60c 0.000122g
libsvm
*
sgn , , 1,2,...,15i if x x b i
Where { 0.2419,0.0221,0.0813,0.0441, 0.0191,0.4745,0.5063,...
0.5109,0.2726,0.3521,0.1638, 0.1276, 0.0855, 0.1517,0.0215}
ib
Trained SVMs are tested by the testing set, and the result is
shown in Fig. 13.
Figure 13. result of the testing set using trained SVM
In the Figure. 13, the hollow points represent the actual
fault modes of the testing sample, and the solid pionts represent
the testing fault mode by SVM. Where the two kinds of points
are not overlap means the fault isolation error. The Fig. 13
shows the accuracy rate is 98 , to achieve satisfactory
accuracy, hence, illustrates the validity of this method in fault
diagnosis for the IGBT open-circuit faults in vector control
induction motor drives.
.1%
V. CONCLUSION
In this paper, the data-based fault diagnosis method
combing wavelet analysis and support vector machine is
proposed. This method can avoid the impact of the current
closed-loop controller, and are insensitive to the motor speed
and load. The results of simulation show that the accuracy of
fault isolation is satisfactory, and suitable for vector control
induction motor driver system.
ACKNOWLEDGMENT
Thanks for the supports of the National Natural Science
Foundation of China under grant numbers 61273174 and
61034006.
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1044 2013 IEEE 8th Conference on Industrial Electronics and Applications (ICIEA)