induction motor diagnostic system based on electrical

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

[email protected]

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

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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

H.-C. Chang et al.: Induction Motor Diagnostic System Based on Electrical… 733

123

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

734 International Journal of Fuzzy Systems, Vol. 18, No. 5, October 2016

123

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


123

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


123

• 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


123

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


123

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

Healthy Broken bar Eccentricity

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


123

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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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induction motor diagnostic system based on electrical

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