Induction Motor Diagnostic System Based on Electrical DetectionMethod and Fuzzy Algorithm
Hong-Chan Chang1 • Shang-Chih Lin1 • Cheng-Chien Kuo1 • Cheng-Fu Hsieh1
Received: 1 December 2015 / Revised: 25 March 2016 / Accepted: 3 May 2016 / Published online: 19 May 2016
� Taiwan Fuzzy Systems Association and Springer-Verlag Berlin Heidelberg 2016
Abstract This study develops an electrical detection
method for the diagnosis and fault detection of induction
motors. An experiment constructs two types of defect
models: broken bar and dynamic eccentricity. Electrical
signals acquired during the operation of a motor are
transformed through a fast Fourier transform to obtain the
feature frequency components for identifying the type of
motor fault. Subsequently, the Clark-Concordia transform
is used to compare the stator current Concordia pattern
between faulty and healthy motors. Finally, a fuzzy infer-
ence system is designed for assessing the severity of motor
faults. The proposed method not only can diagnose the type
of motor fault, but can also assess the operational state of a
motor. The method is suitable for preparing a maintenance
program for induction motors and for reducing their
excessive maintenance cost.
Keywords Electrical detection method � Fast Fouriertransform � Clark-Concordia transform � Fuzzy inference
system � Fault detection
1 Introduction
To maintain the normal operation of a motor, maintenance
work on it is necessary. Maintenance work is typically
performed only after an accident, after which, in order to
prevent one from happening again, maintenance work is
conducted at a fixed period of time. Regular maintenance
for detecting defects and reducing accidents can indeed
play a role in sustaining a motor’s normal operations, but
the actual status of the equipment is not considered,
resulting in the possibility of excessive maintenance.
Maintenance involves considerable manpower, material,
financial resources, and time. Furthermore, apart from
underutilizing all types of resources, maintenance reduces
the normal production time and production efficiency.
Currently, researchers around the world are stepping up
their study of online monitoring technology. Detecting
accidents promptly and reducing their incidence can cer-
tainly reduce the cost of motor maintenance.
The present literature of motor fault diagnosis mostly
utilizes the electrical detection method, vibrational detec-
tion method, and partial discharge detection method. Dif-
ferent detection methods have different effects on detecting
faults, with the electrical detection method relatively good
at diagnosing rotor and eccentric faults in a spectral com-
ponent. Therefore, this paper uses the electrical detection
method to analyze rotor and eccentric faults of a motor and
assess the operating state of the motor with such faults [1,
2]. Motor current signature analysis (MCSA) is currently
one of the most popular techniques for monitoring the
condition of medium-voltage induction motors online in an
industrial environment [3–8]. When a fault occurs, the
magnetic flux changes. The measured time domain stator
current signal is transformed through a fast Fourier trans-
form (FFT) to the frequency domain, and a stator current
spectrum is subsequently produced for identifying fault
features.
A fuzzy algorithm can quantize a considerable amount
of inaccurate, unclear depictions to facilitate computerized
information processing. From the earliest to the latest
& Hong-Chan Chang
1 Department of Electrical Engineering, National Taiwan
University of Science and Technology, 43, Sec. 4, Keelung
Rd., Taipei 10607, Taiwan
123
Int. J. Fuzzy Syst. (2016) 18(5):732–740
DOI 10.1007/s40815-016-0199-4
development of the theory, fuzzy algorithms have been
used in a variety of applications, including those related to
classification, control, and mathematical programming.
Fuzzy algorithms have a wide range of applications and are
rapidly maturing. Many scholars have proposed diagnostic
methods based on a fuzzy inference system (FIS) [9–13]. In
fault diagnosis, the operating conditions are not confined to
only ‘‘good’’ and ‘‘bad,’’ but may have a state in-between
these two. A set of fuzzy rules and membership functions
can explain the fuzzy concept of induction motor fault
severity. According to many studies, although MCSA can
detect motor fault features, it is incapable of assessing the
operational state of an induction motor. Therefore, we set
up FIS and experimentally verify it using broken bar and
dynamic eccentricity fault detection models. The combined
use of this diagnostic system and the feature frequency
components diagnoses the fault type of induction motors
and assesses the severity of the fault. Thus, the diagnostic
system can facilitate the preparation of a maintenance
program [14].
2 Induction Motor Diagnostic System
2.1 Experimental Setup
In this study, we use a 2-hp squirrel cage induction motor
that is operated at the rated voltage and frequency, with the
detailed specifications Table 1. Figure 1 shows the exper-
imental architecture. We conduct experiments in a motor
test facility having a power regulator that supplies the
required motor power. We measure electrical signals,
which are then saved in a LabVIEW interface, using
voltage and current sensors and then analyze the electrical
signals by employing a motor diagnostic system for
assessing the operational state of the motor and for iden-
tifying the type of motor fault.
2.2 Experimental Model
• Healthy motor Before constructing a model for
detecting the type of motor fault, a normal motor is
used as a reference. The measured spectral feature and
stator current Concordia pattern of the normal motor
are compared with those of a failed motor.
• Rotor In a motor rotor bar, a hole with a diameter and
depth of 7 and 30 mm, respectively, is drilled. The
motor diagnostic system is then used to determine the
fault location, as shown in Fig. 2a.
• Eccentricity This defect model shifts upward by
0.5 mm between the motor and the load by using load
power equipment. The adjustment platform is shown in
Fig. 2b.
3 Detection Methodology
Table 2 presents recent studies applying the electrical
detection method for motor diagnosis. Fault diagnosis has
been discussed a lot, but the evaluation of a motor oper-
ating state is seldom studied. The motor operating state has
been evaluated in [11, 12, 17, 23]. With [17, 23] observing
the differences in the stator current Concordia pattern of
motor and designing a fuzzy diagnostic system to effec-
tively evaluate the severity of motor fault, but these ref-
erences only have discussed one motor fault type.
Therefore, this paper diagnoses motor rotor and eccentric
faults based on electrical detection method. The stator
current signal of a motor is transferred to a Concordia
pattern so as to observe the difference between a healthy
motor and a faulty motor. Moreover, a fuzzy inference
system is designed, which considers the loading level and
membership function range, so that the output result is
more referential.
Figure 3 shows the diagnostic system diagram. First, we
simultaneously implement the FFT and Concordia trans-
form for the measured three-phase current signal. After the
Concordia transform, the Concordia pattern of a healthy
motor and a faulty motor can be observed. A fuzzy inference
system is then designed according to the error rate of the
pattern to assess the motor operating state. On the other
hand, after the FFT of electrical signals, the motor features
are observed in the spectrum, and these features are com-
pared with the healthy motor, whereby the feature spectrum
component is extracted at last to identify the motor fault
type. The result of operating state gives effective advice to
on-site operation personnel. The result of this fault diagnosis
can assist in the arrangement for maintenance after stop-
page. The diagnosis system flowchart is shown in Fig. 4.
Table 1 Induction motor specification
Item Specification
Type Squirrel cage induction motor
Horsepower 2 HP
Numbers of phases 3 Phase
Poles 4 Pole
Rated voltage 220 V
Rated frequency 60 Hz
Rated speed 1715 rpm
Insulation class E
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3.1 Concordia Pattern
A two-dimensional representation can describe a three-
phase induction motor operating phenomenon. The analy-
sis of a three-phase induction motor can be simplified using
the Clark-Concordia transformation [15–20]. This reduces
the current number of phases to make observations easier.
The current Concordia vector components (ia, ib) are a
function of the main phase variables (ia, ib, ic), which is
evident in (1) and (2).
ia ¼ffiffiffi
3
2
r
ia �1ffiffiffi
6p ib �
1ffiffiffi
6p ic; ð1Þ
ib ¼1ffiffiffi
2p ib �
1ffiffiffi
2p ic; ð2Þ
In ideal conditions, three-phase currents lead to a Con-
cordia vector with the following components (3) and (4).
ia ¼ffiffiffi
6p
2IM sinxst; ð3Þ
ia ¼ffiffiffi
6p
2IM sin xst �
p2
� �
; ð4Þ
where IM is the supply phase current maximum value and
xs is the supply frequency.
ConcordiaTransform
FastFourier
Transform
FuzzyInferenceSystem
CompareSpectral
Component
Severity IndexFeature
FrequencyAcquisition
Operational State
Fault Type
H R E
Normal Caution Warning Dangerous
(a)
(b) (c)
Fig. 1 Experimental architecture: a view of the experimental setup, b LabVIEW interface, c induction motor diagnostic system
0.05 cm
(a) (b)
Fig. 2 Experimental model: a broken bar, b eccentric
Table 2 Recent Studies
ComparisonReferences
[5, 7, 8, 21] [6] [11, 12, 23] [15, 16, 20] [17] This paper
Fault diagnosis
Stator d d d
Rotor d d d
Bearing d d
Eccentric d
Operational state assessment d d d
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The so-called Concordia vector is a plot between ia and
ib. It has a circular pattern centered at the origin, as shown
in Fig. 5. This is a very simple reference figure and shows
that faulty conditions can be detected by monitoring the
deviation in the acquired patterns.
3.2 Spectral Component
This study analyzes broken bars and dynamic eccentricity.
The causes of the broken bars and eccentricity failure and
the corresponding feature frequency components are as
follows.
• Rotor Rotors consist of a core and an end ring. In
addition, they need copper or aluminum bars poured
when the magnetic field is turned on media. The main
reason for the occurrence of broken bars is the
formation of defects. If the raw materials that are
poured into copper or aluminum bars are unevenly
ia
ib
ic
abc to dq
Fuzzy InferenceSystem
Fuzzy Rules Data Base
Operating StateAssessment
Compare SpectralComponent
Feature FrequencyAcquisition Fault Diagnosis
Fast Fourier Transform
Concordia Pattern
Fig. 3 Diagnosis system diagram
Start
Clark-ConcordiaTransformation (1)-(2)
(ia, ib, ic) to (i , i )
Calculation Error Rate(9)-(10)e1, e2
Fuzzy SystemInput 1: e1, Input 2: e2
Design of Fuzzy RulesTable 3
DefuzzificationOutput 1: Severity Value
Measurement signalia, ib, ic, va, vb, vc
Fast Fourier Transform
Spectral Component Analysis (7)-(8)
Operational StateNormal, Caution,
Warning, Dangerous
Define the Severity Threshold Value
Fig. 7
Fault Identification(7)-(8)
Design of Fuzzy Membership Functions
Fig. 6(a), (b)
Fig. 4 Diagnosis system flowchart
iα
i
MI26
Fig. 5 Current patterns for ideal conditions
H.-C. Chang et al.: Induction Motor Diagnostic System Based on Electrical… 735
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mixed, then they can produce bubbles, and bars
containing bubbles are not suitable for use in the
motor. When the motor load is overloaded, the resulting
high current may also cause broken bars. The operation
of an induction motor with a rotor bar defect generates
a negative sequence of rotor currents due to rotor
asymmetry. It induces a principal frequency component
in the stator current, which increases up to the
frequency (1 ± 2 s) in units of femtosecond. Because
of reflection, the rotor asymmetry frequencies are ±s,
±3 s,…, with all the frequencies in units of femtosec-
ond. Consequently, line components are generated
around the fundamental frequency in the stator current
spectrum [21]. These components have the frequencies
given by (5)–(7).
ns ¼120fs
p; ð5Þ
where ns is the synchronous speed, fs is the supply
frequency, and p is the number of poles.
S ¼ ns � nr
ns; ð6Þ
where S is the slip, ns is the synchronous speed, and nris the actual speed.
fbb ¼ ð1� 2kSÞfs; ð7Þ
where fbb is broken rotor bars fault characteristic fre-
quency formula, k is any positive integer, S is the slip,
and fs is the supply frequency.
• Eccentricity In the production line, the connection of
the motor and the load often requires the use of
couplings. When the axis of the rotor and the bearing
centerline tilt or pan, eccentricity results. The eccen-
tricity of a cylinder rotating around an air gap can be
classified as static, dynamic, or mixed eccentricity.
Static eccentricity corresponds to the center of rotation
being simply displaced from the original center. For
dynamic eccentricity, the center of rotation remains at
the origin and the cylinder is displaced. For mixed
eccentricity, both the cylinder and the center of
rotation are displaced from their respective original
positions [22]. The frequencies of these components
are given by (8).
fecc ¼ fs 1� m1� S
p
� �� �
; ð8Þ
where fecc is eccentricity fault characteristic frequency
formula, m is any positive integer, p is the number of
poles, and S is the slip. This scheme provides the
advantage of not requiring any knowledge of the
machine construction.
3.3 Fuzzy Inference System
Fuzzy systems involve a set of membership functions and
rules. During the operation of the induction motor, apart
from its operating state being ‘‘good’’ or ‘‘bad,’’ there must
be some distinction between the severities of these two
cases. A fuzzy algorithm quantifies the operational char-
acteristics of an induction motor for further processing on a
computer to assess the operational state.
-50 0 50(a)
(b)
0
1
Severity
0
1
e1, e2
N Z P
Z L M H
-0.333 0 0.333 0.667 1 1.333
[%]
Fig. 6 Membership functions: a input variables, b output variables
Fig. 7 Induction motor operational state
Table 3 Fuzzy rules
Input 1 (e1)
Positive Zero Negative
Input 2 (e2)
Positive Danger Caution Danger
Zero Warning Normal Danger
Negative Warning Caution Warning
736 International Journal of Fuzzy Systems, Vol. 18, No. 5, October 2016
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• Input variables Stator current Concordia patterns of
healthy and faulty motors are used to compute the FIS
input variables [23]. They are defined as (9) and (10):
e1ðkÞ½%� ¼ PhðkÞ � Pf ðkÞPhðkÞ
; ð9Þ
e2ðkÞ½%� ¼ e1ðkÞ � e1ðk � 1Þe1ðkÞ
; ð10Þ
where, Ph is the healthy motor stator current Concordia
pattern (‘‘healthy pattern’’) that is considered as the
reference; Pf is the faulty motor Concordia pattern
(‘‘faulty pattern’’); e1 can be expressed as an error rate
between the two patterns; and e2 can be expressed as
the error rate’s trend. For simplicity, only negative (N),
zero (Z), and positive (P) labels are considered for the
input variables as shown in Fig. 6a.
• Output variables The output is the severity index,
which should be capable of indicating the fault severity.
In terms of linguistic variables, we consider four levels
for this variable: zero (Z), light (L), medium (M), and
high (H), as shown in Fig. 6b. The output membership
function boundary is generally from 0 to 1, but in this
case, the range of output results is between 0.2 and 0.8.
To confine the severity index of the output to the range
from 0 to 1, we adjust the boundary of the output
membership function from -0.333 to 1.333. Thus, we
can conveniently classify the severity index into four
types of operational states as shown in Fig. 7.
• Fuzzy rules Once the membership function forms have
been determined, the fuzzy if–then rules can be
generated as shown in Table 3. If e1 is Z and e2 is Z,
then the operational state is similar to that of a healthy
motor, and hence this condition is called ‘‘Normal.’’ If
e1 is Z and e2 is P or N, then due to the non-zero trend,
the operational state is not similar to that of a healthy
motor, and thus this condition is called ‘‘Caution.’’ If e1is P or N, then the operational state is not similar to that
of a healthy motor at present. When e1 is N, the stator
current is higher than that of a healthy motor, and
therefore the severity may be higher than P. For the e2range, the condition is called ‘‘Warning’’ or ‘‘Danger’’.
4 Experimental Results and Analysis
Induction motor fault features should be observed under
the frequency domain in which they are more obvious.
Although we can determine fault features using MCSA,
this technique is unable to assess the operational state of an
induction motor. Therefore, we convert the stator current
signal into spectral components and a Concordia pattern
Table 4 Feature Component Analysis
Fault type
Broken bar Eccentricity
Speed (rpm)
Rated 1800 1800
Actual 1715 1715
Slip (S) 0.0472 0.0472
fs (Hz) 60 60
Pole (p) – 4
k 1 –
m – 2,4,6
Feature component (Hz) 65.664
54.336
31.416
117.168
145.752 Fig. 8 Stator current spectrum: a health, b broken bar, c dynamic
eccentricity
H.-C. Chang et al.: Induction Motor Diagnostic System Based on Electrical… 737
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through FFT and Clark–Concordia transforms, respec-
tively. Consequently, we determine both the fault type
(from the spectral components) and operational state (from
FIS).
4.1 Fault Identification
Irrespective of the condition of an induction motor, tests
are performed by operating it under full load (rated power)
and at a rotor speed of 1715 rpm. Therefore, the slip is
obtained as 0.0472 using (5) and (6). Table 4 presents the
feature components.
A comparison of the results with those of a healthy
motor spectrum is shown in Fig. 8. Clearly, for both
eccentricity and broken bar models, the feature components
appear near the corresponding feature frequency spectrum.
Therefore, the experimental results confirm that MCSA
effectively identifies the type of induction motor fault.
4.2 Severity Index
The results are now compared with those of the stator
current Concordia pattern of a healthy motor, as shown in
Fig. 9. We observe that the stator current Concordia pattern
changes slightly when a fault occurs. FIS can then be used
to obtain the severity index from the variation between
different patterns. To obtain more accurate experimental
data, the stator currents are sampled at a 10 kHz sampling
rate, with the mean of the outputs considered as the final
severity index. The operational state of the induction motor
is finally assessed based on the magnitude of the severity
index. Figure 10a shows the severity index of broken bars;
the mean is 0.2491, indicating that the operation state is
‘‘Normal.’’ Although the bars are broken, the severity is
low, and thus the induction motor can continue to operate.
Figure 10b displays the severity index of the dynamic
eccentricity; the mean is 0.2811, and hence the operation
Fig. 9 Stator current Concordia patterns: a broken bar, b dynamic eccentricity
Fig. 10 Severity index: a broken bar, b dynamic eccentricity
738 International Journal of Fuzzy Systems, Vol. 18, No. 5, October 2016
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state is ‘‘Caution.’’ The induction motor can continue to
operate, but maintenance personnel need to notice whether
couplings tilt or deviate. Table 5 lists the experimental
results.
5 Conclusion
According to many studies, MCSA can provide motor fault
features, but cannot assess the operational state of an
induction motor. In this study, we have simultaneously
observed the stator current Concordia pattern and spectral
components of a motor and then used FIS to assess the
operational state of the motor. Apart from the identification
of fault types, the results show that the operational state can
also be effectively evaluated. Thus, the proposed method
helps in effectively designing maintenance procedures and
in reducing excessive repair costs and accidents.
Acknowledgments The research was supported by the Ministry of
Science and Technology of the Republic of China, under Grant No.
MOST 103-2221-E-011-077-MY2.
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Table 5 Experimental results
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Spectral component (A)
33 Hz 0.0042 0.0018 0.0641
54 Hz 0.0078 0.0751 0.0088
66 Hz 0.0069 0.0490 0.0055
118 Hz 0.0006 0.0009 0.0714
147 Hz 0.0007 0.0005 0.0398
Severity index (mean) 0 0.2491 0.2811
Operational state Normal Normal Caution
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Hong-Chan Chang (M’87) was born in Taipei, Taiwan, on 5 March
1959. He received his B.S., M.S., and Ph.D. degrees, all in electrical
engineering, from National Cheng Kung University in 1981, 1983,
and 1987, respectively. In 1987, he joined the National Taiwan
University of Science and Technology (NTUST), Taipei, Taiwan, as a
faculty member. He is presently a Professor and formerly served as
the vice president of NTUST. His major areas of research include
pattern recognition, partial discharge, and application of artificial
intelligence to power systems.
Shang-Chih Lin was born in Taichung, Taiwan, on 9 May 1988. He
is currently a Ph.D. candidate in the department of electrical
engineering of the National Taiwan University of Science and
Technology (NTUST). He received his B.S. and M.S. degrees from
the Nan Kai University of Technology (NKUT) in 2010 and 2012,
respectively. His research interests include fault diagnosis, pattern
recognition, optimization algorithms, and economic dispatch.
Cheng-Chien Kuo (M’01) was born in Yunlin, Taiwan, on 9 August
1969. He received his B.S., M.S., and Ph.D. degrees, all in electrical
engineering, from the National Taiwan University of Science and
Technology (NTUST) in 1991, 1993, and 1998, respectively. He has
been with National Taiwan University of Science and Technology
(NTUST) since 2015, where he is currently a Professor in the
Department of Electrical Engineering. His research interests include
microprocessor, energy management system and optimization tech-
niques, and partial discharge.
Cheng-Fu Hsieh was born in Taipei, Taiwan, on 10 January 1992. He
is currently a master in the department of electrical engineering of the
National Taiwan University of Science and Technology (NTUST). He
received his B.S. degree from the National Yunlin University of
Science and Technology (NYUST) in 2010. His research interests
include fault diagnosis and fuzzy algorithm.
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