A Proposed Fault Diagnostics Technique for Induction Motor Stator
Winding
MOHAMED I. ABU EL-SEBAH, FAEKA M.H. KHATER
Electronics Research Institute - Dokki, Cairo, EGYPT
Abstract: Online monitoring is widely used for induction motor fault diagnostics. This article presents a
fault diagnostics technique for a 3-phase induction machine. The proposed technique was developed with
fuzzy logic applied to a simplified induction motor model affected by the stator winding short turns.
Based on the 3-phase time-domain model, the machine winding with different fault conditions has been
simulated to check the resulting speed, torque, and stator current spectrum in each case. The results
indicate that the developed fault diagnostics scheme is efficient to specify the fault type of the induction
machines stator.
Key-words: fault diagnostics, Induction Motor, induction motor model, stator short winding, fuzzy logic,
stator short winding model.
Received: September 26, 2022. Revised: February 21, 2023. Accepted: March 23, 2023. Published: May 12, 2023.
.
1. Introduction
Because of their advantages such as low-cost
maintenance and high reliability, induction
motors are used in a variety of industrial
applications including drive systems and electric
vehicles. Although induction motor control is
complicated, the field orientation technique
simplifies it, elevating the induction motor to the
status of the modern industry's beating heart. To
optimize motor reliability, it is important to
maintain induction motors operational via fault
identification to avoid sudden motor damage,
[1], [2], [3], [4], [5], [6], [7].
Due to the fact that motors are prone to failure,
the problem of monitoring and preventing this
unexpected failure is one of the most important
challenges we face in the industry despite the
high reliability of these machines. Researchers
have always had to contend with the faults seen
in induction motors, specifically those related to
the stator winding, as well as faults related to the
rotor electric side and eccentricity. Their
thorough investigation has revealed that 30 to
40% of the total induction motor faults are
linked to those seen in the winding of stator, [8].
To improve reliability, great attention is paid to
the fault that occurs in induction motor drive
system diagnostics. The drive system faults are
classified into inverter faults, [9], [10] and motor
faults, while induction motor faults are divided
into two main faults categories mechanical faults
and electrical faults. The induction motor's
electrical faults are either broken bars or short
circuit of the stator winding, [11]. Many
methods used to detect the faults related to short
circuit stator winding have been adopted, [12],
[13], [14]. Motor current signature analysis
(MCSA) related to the 50/60-Hz sidebands has
become a standard test in the industry for
monitoring the induction motor stator condition,
[15], [16], [17], [18], [19], [20], [21], [22], [23].
Drive system fault diagnostics must include both
the converter and the motor. Previous research
[9], [10] presented a rule-based fuzzy Logic
system for fault scenarios of inverter-fed
induction machines, with a focus on the
inverter's power switches. The created system is
International Journal of Electrical Engineering and Computer Science
DOI: 10.37394/232027.2023.5.5
Mohamed I. Abu El-Sebah, Faeka M.H. Khater
E-ISSN: 2769-2507
33
Volume 5, 2023
capable of determining the fault kind and
location of the inverter.
The proposed technique has been validated by
detecting various types of faults in stator
winding with great accuracy. One of the most
significant advantages of this method is the
ability to diagnose faults online. The accuracy of
motor defect identification and the feasibility of
knowledge extraction are both confirmed by
simulation results. The preliminary findings
illustrate that the proposed fuzzy approach can
be utilized to accurately diagnose stator faults.
2. Induction Motor Model in ABC
Axes
In the ABC axis, the induction motor model is
represented by the following equations. The
induction motor's electrical and mechanical
components are both included in this model.
Starting with the voltage equation
(1)
Where
(2)
(3)
(4)
Where
(5)
Leading to the electrical side equations in the
form
(6)
While Mechanical Part can be driven from the
torque equation
(7)
Since
(8)
Where
Resistance matrix [Ω]:
rc
rb
ra
s
s
s
R
R
R
R
R
R
R
00000
00000
00000
00000
00000
00000
Inductance sub matrices [H]:
s
mm
m
s
m
mm
s
ss
L
LL
L
L
L
LL
L
L
22
22
22
,
r
mm
m
r
m
mm
r
rr
L
LL
L
L
L
LL
L
L
22
22
22
)()()(
)()()(
)()()(
.
CosCosCos
CosCosCos
CosCosCos
LL msr
)()()(
)()()(
)()()(
.
SinSinSin
SinSinSin
SinSinSin
LdL msr
Where
is the phase angle
dLrr
sr
dL
sr
dLdLss
L'
Matrix of inductance L:
Zeros
sr
dL
sr
dLZeros
dsr
dL
'
000)()()(
000)()()(
000)()()(
)()()(000
)()()(000
)()()(000
.
SinSinSin
SinSinSin
SinSinSin
SinSinSin
SinSinSin
SinSinSin
m
L
dsr
dL
3. Induction Motor Model in d-q
Axes
The mathematical model of the induction
machine in the rotating reference frame will be
supplied to derive the machine model in the
-
frame to be used in induction motor simulation
International Journal of Electrical Engineering and Computer Science
DOI: 10.37394/232027.2023.5.5
Mohamed I. Abu El-Sebah, Faeka M.H. Khater
E-ISSN: 2769-2507
34
Volume 5, 2023
in the d-q frame. This model can be deduced
starting from basic equations (9, 10) refereeing
to Fig. 1.
Fig. 1 Stator and rotor frame.
The stator voltage can be written in the
following vector form:
Where
r
dt
d
)(
,
a
dt
d
,
ra
dt
d
Referred to arbitrary reference frame
quantities, the stator voltage equation can be
written as the following
][ )()()()(
js
ss
js
rm
dt
d
js
ss
js
seiLeiLeiRev
(9)
][][ s
dt
d
sr
dt
d
mssrmssss iLiLiLiLjiRv
(10)
The above equation can be represented in the
following form:
s
dt
d
sas
s
sjiRv
(11)
Where the flux components are
drmdssds iLiL
qrmqssqs iLiL
Decomposing the stator voltage into two
components labelled as d,q
sd
dt
d
sqasdssd iRv
(12)
sq
dt
d
sdasqssq iRv
(13)
The rotor voltage can be written in the following
vector form:
rr dt
d
iRv r
r
Referred to the arbitrary reference frame
(14)
][])[(s
r
dt
d
r
s
s
dt
d
m
s
rr
s
smra
s
rr
s
riLiLiLiLjiRv
(15)
The above equation can be written in the
following form:
dsmdrrdr iLiL
(16)
qsmqrrqdr iLiL
Decomposing the rotor voltage into two
components labelled as d,q
rd
dt
d
rqrardrrd iRv
)(
(17)
rq
dt
d
rdrarqrrq iRv
)(
(18)
The motor in the d-q axis can be expressed using
the following matrix form equations
rq
rd
sq
sd
rrqrrammra
rrarrdmram
mmasssa
mamsass
rq
rd
sq
sd
i
i
i
i
pLRLpLL
LpLRLpL
pLLpLRL
LpLLpLR
v
v
v
v
)()(
)()(
(19)
Simplifying the above equation leads to
obtaining the motor model in the arbitrary
rotating reference frame
sr
r
ra
m
amssmrasma
r
ra
m
asrsmrasmams
mrrmrarmars
m
rarsa
rmrarmamr
m
rarsars
sq
sd
sm
sm
mr
mr
m
rs
rq
rd
sq
sd
m
rs
rq
rd
sq
sd
LRLLLRLLLL
LLLRLLLLLR
LRLLLLLRLLL
LLLLLRLLLLR
A
v
v
LL
LL
LL
LL
LLL
i
i
i
i
A
LLL
i
i
i
i
p
22
22
2
2
22
)()(
)()(
)()(
)()(
0
0
00
00
00
00
.
1
.
1
.
(20)
][ )()()()(
jr
rr
jr
sm
dt
d
jr
rr
Jr
reiLeiLeiRev
Stator Frame
Rotor Frame
Arbitrary Frame
Electric Quantities
International Journal of Electrical Engineering and Computer Science
DOI: 10.37394/232027.2023.5.5
Mohamed I. Abu El-Sebah, Faeka M.H. Khater
E-ISSN: 2769-2507
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Volume 5, 2023
)..(
2
3
sdrqsqrdme iiiiL
P
T
(21)
(22)
The machine model can be referred to rotor
reference frame by putting
ra
Fig. 2 Healthy motor input output quantities (a) the
motor supply voltage, (b) motor stator current, (c)
motor speed, (d) motor torque, and (e) three phase
stator current spectrum
The ABC model has been investigated on a healthy
case with the parameters listed in section VII and
the simulation has been carried out at a frequency of
50 Hz. The current, speed, and torque of the motor
under investigation are illustrated in Fig. 2.
4. Proposed Stator Fault detection
Technique
Figure 3 depicts the effect of decreasing the stator
resistance from R1 (rated value) to R1 –dr, as seen
on the current amplitude. It is obvious that
expanding the short circuit winding reduces the
stator resistance, causing the stator current to rise. If
knowing this, the stator resistance reduces as the
number of series windings that make up the stator
resistance per phase decreases. As a result, the stator
defective phase resistance is reduced, and the stator
current is increased dependent on the ratio of the SC
winding. In other words, the amplitude of the stator
current is proportional to the stator short turns. The
variation of the resistance detected from the current
amplitude of the stator faulty phase and hence the
percentage of the short circuit stator winding can be
determined. By eliminating the shorted winding
resistance, the faulted phase stator resistance is
calculated as follows.
(23)
Where
Rs: total resistance of the stator winding
n : The SC percentage winding
Fig.3 Induction motor torque and speed under
stator resistance variation
(a)
International Journal of Electrical Engineering and Computer Science
DOI: 10.37394/232027.2023.5.5
Mohamed I. Abu El-Sebah, Faeka M.H. Khater
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Volume 5, 2023
5. Simulation Results
Figure 2 illustrates the simulation results for the
healthy induction motor powered by a pure
sinusoidal supply. The three phase stator voltage
spectrum, three phase stator current, motor speed,
motor toque, and three phase stator current spectrum
are illustrated in Fig. 2. Figure 4 gives the current
waveform and spectrum of the motor with a 0.05
short circuit stator winding. In terms of settling
time and current amplitude, the distinction between
a healthy and a malfunctioning motor is obvious in
the faulty phase current.
Fig. 4 Faulty motor input output quantities (a) motor
stator current, (b) three phase stator current
spectrum (c) motor speed, and (d) motor torque
Figure 5 illustrates the faulty phase current
waveform and current spectrum for the Motor with
different short circuit winding. It is clear the
difference between different cases in settling time
and current amplitude. A high number of turns of
shorted winding results in a big rise time
accomplished with a high current amplitude. These
results mean that the quantity of rise time and
current amplitude can be used to determine the
amount of stator short winding in a faulty induction
motor.
Fig. 5 Motor current and its spectrum associated
with different percentages of the faulty stator
winding
6. Detecting the Faulty Motor
The proposed technique consists of three steps: first,
measurement of the induction motor stator current
waveform; second, the Amplitude of the stator
current is computed; and third, a rated speed raise
time is determined, then the computed variables are
subjected to a fuzzy algorithm. This technique
allows a simplified algorithm to carry out by a
simple low-cost controller to be used to classify the
stator fault. A mathematical model of fault analysis
has been implemented using fuzzy based on the
previous rule base table as illustrated in Table 1.
The membership of the stator current and rise time,
which comprises three fuzzy sets, is shown in Fig. 6.
In addition, Fig. 6 depicts the stator winding state,
(a)
International Journal of Electrical Engineering and Computer Science
DOI: 10.37394/232027.2023.5.5
Mohamed I. Abu El-Sebah, Faeka M.H. Khater
E-ISSN: 2769-2507
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Volume 5, 2023
which shows whether the winding of the stator is
healthy, small faulty winding, medium faulty
winding, high faulty winding, or seriously bad
winding. All memberships are set up to work on a
per-unit basis. The rule base table used as an
inference rule basis table is given in Table 1.
Fig. 6 Current membership, rise time membership,
and stator winding membership
Settling Time
Normal
High
Very High
Current Amp.
Normal
Healthy
Winding
Small
Short
Winding
medium
Short
Winding
High
medium
Short
Winding
High
Short
Winding
High Short
Winding
Very High
High
Short
Winding
High
Short
Winding
Seriously
Short
Winding
Table 1 Rule base table
The fuzzy prediction program is carried out for five
different cases illustrated in Table 2 which includes
logged data and a database for fault diagnostic
technique. The results indicate that the percentage
error in predicting healthy turns of the stator
winding is about 0.24% which verifies the
robustness of the prediction technique.
Actual
Healthy
Turns
I(A)
I%
Tr
Tr%
Predicted
Healthy
Turns
1
1
4.0711
0.333
1.5
0.333
1
2
0.98
4.3689
0.358
1.65
0.367
0.9791
3
0.96
4.7833
0.392
1.75
0.389
0.9623
4
0.95
7.7006
0.630
3.5
0.778
0.9500
5
0.94
10.1809
0.834
5.78
1.284
0.9400
Table 2 Data and results of the fuzzy prediction
program
7. Conclusion
A three phase induction machine fault diagnosis
technique has been proposed and development for
the diagnostic technique is presented. The
mathematical model of an induction motor has been
created and tested under various fault circumstances
using a time domain simulation model. The existing
spectrum enables fault diagnostic of shorted stator
windings of induction by utilization of the motor
model. The proposed fault diagnostics system has
produced logged data that indicates the problem
condition based on the database status. The created
technique can determine how much of a stator
winding is short. The technique is suitable for usage
in an induction motor on-line fault diagnostics
system. The proposed work can be extended to
include the other electrical faults and mechanical
faults to be a complete faults diagnostics algorithm
8. Machine Parameters
Rsa=Rsb=Rsc=3.85 Ohm
Rra=Rrb=Rrc=2.50 Ohm
Lls=0.0576 H Llr=0.0576 H
Lm=0.28779 H P=4
J =0.03 kg.m2 B=0.003 kg.m2Sec
Number of rotor bars=28
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Mohamed I. Abu El-Sebah, Faeka M.H. Khater
E-ISSN: 2769-2507
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Creation of a Scientific Article (Ghostwriting
Policy)
The authors equally contributed in the present
research, at all stages from the formulation of the
problem to the final findings and solution.
Sources of Funding for Research Presented in a
Scientific Article or Scientific Article Itself
No funding was received for conducting this study.
Conflicts of Interest
The authors have no conflicts of interest to declare
that are relevant to the content of this article.
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DOI: 10.37394/232027.2023.5.5
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