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DIAGNOSIS OF BROKEN ROTOR BARS IN
INDUCTION MOTOR BY USING VIRTUAL
INSTRUMENTS
Article · September 2013

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International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print),
INTERNATIONAL
JOURNAL OF ELECTRICAL ENGINEERING &
ISSN 0976 – 6553(Online) Volume 4, Issue 5, September – October (2013), © IAEME
TECHNOLOGY (IJEET)

ISSN 0976 – 6545(Print)
ISSN 0976 – 6553(Online)
Volume 4, Issue 5, September – October (2013), pp. 78-86
© IAEME: www.iaeme.com/ijeet.asp
Journal Impact Factor (2013): 5.5028 (Calculated by GISI)
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IJEET
©IAEME

DIAGNOSIS OF BROKEN ROTOR BARS IN INDUCTION MOTOR
BY USING VIRTUAL INSTRUMENTS
S.M. Shashidhara1, Dr.P. Sangameswara Raju2
1

Professor, Dept of E&CE, Proudadhevaraya Institute of Technology, Hospet, India
2

Professor, Dept of EEE, SVU College of Engineering, Tirupati, India.

ABSTRACT
The condition monitoring of the electrical machines can significantly reduce the costs of
maintenance by allowing the early detection of faults. In this paper some results on non-invasive
detection of broken rotor bars in squirrel-cage induction motors are presented. The applied method is
the motor current signature analysis (MCSA) which utilizes the results of spectral analysis of the
motor stator current. The diagnosis procedure was performed by using virtual instruments (VIs). The
substantial presence of some well-defined sideband frequencies in the harmonic spectrum of the
measured line current clearly indicates the rotor faults of the induction motor. The theoretical basis
of this method was proved by laboratory tests.
KEY WORDS: Condition monitoring, Fault detection, Motor current signature analysis (MCSA),
Squirrel-cage Induction Motor, Virtual instrument (VI).
I.

INTRODUCTION

Condition monitoring is the procedure of monitoring a parameter of status in machinery
(vibration, temperature etc), in order to identify a significant change which is indicative of an arising
fault. It is a major element of predictive maintenance. The use of conditional monitoring allows
maintenance to be scheduled, or other actions to be taken to prevent failure and avoid its aftermaths.
Condition monitoring has a unique benefit in that circumstances that would shorten normal lifespan
can be addressed before they develop into a major failure. Condition monitoring techniques are
normally used on rotating equipment and other machinery.
Induction motors play an crucial role in the safe and efficient operation of industrial plants.
Generally they are designed for 30 years fault-free lifetime, but many of them are not available at all
times. Numerous electric machine parts are especially susceptible to failures. The stator windings are
subject to insulation break-down caused by mechanical vibration, heat, age, damage during
installation, etc. The machine rotor bars and end rings can be broken by the various stresses that act on
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International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print),
ISSN 0976 – 6553(Online) Volume 4, Issue 5, September – October (2013), © IAEME

the rotor. Machine bearings are subject to excessive wear and damage caused by deficient lubrication,
asymmetrical loading, or misalignment. In numerous applications these failures of the electrical
machines can shut down industrial process totally. Such unexpected machine shut downs cost both
time and revenue that could be averted if an early warning system is available against imminent
failures. Fault diagnosis schemes are intended to provide advanced warnings of incipient faults, so
that corrective action can be taken without detrimental interruption to processes. Fault diagnosis of
electrical machines can lead to greater plant availability, extended plant life, higher quality products,
and smoother plant operations.
The remainder of this work is organized as follows: Section 2 describes the literature survey
on diagnosis of broken rotor bar faults using the fast fourier transform (FFT) signal analysis method
and section 3 discusses the LabVIEW based fault diagnosis method. The experimental study and the
observations are presented in Section 4. Finally, the paper concludes in Section 5.

Fig 1 Induction Motor
II.

BROKEN ROTOR BAR FAULTS

Squirrel cage rotor design and manufacturing have gone through little change over the years
[4]. Rotor-related faults are usually associated with thermal stresses, magnetic stresses, residual
stresses in insufficient manufacturing, and environmental stresses that are induced by moisture [3].
Rotor faults begin at high resistance, causing high temperature, and then advance as cracking or holes
in the rotor bars [5]. These faults are more probable to take place at the end rings. Different
parameters such as pulsations in motor speed, air gap flux, vibration and motor current can be
monitored for the detection of broken rotor bars. Early fault detection techniques can significantly
reduce maintenance costs for these motors. The condition monitoring aims at fault diagnosis in
electric motors. The spectral analysis techniques are considered as one of the prominent techniques in
the literature [6-10].
Motor current Spectrum analysis has been the preference of most researchers. Thomson et al
[11], Kliman et al. [12] and Flippetti et al [13] used the MCSA technique to detect broken bar faults.
They established sideband components (fb) around the fundamental frequency to detect broken bar
faults.

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International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print),
ISSN 0976 – 6553(Online) Volume 4, Issue 5, September – October (2013), © IAEME

Fig 2 Faulty Rotor
The lower sideband is specific to a broken bar, while the upper sideband is an outcome of
speed oscillation. f0 is the frequency of the phase current, s is the motor slip, and k = 0, 1, 2, . . ., n.

The magnitude of the sideband frequency components change in accordance with the load
inertia. In addition to Eq. (1), other spectral components that occur can be observed in the motor line
current with the aid of the equation below [15].

Fig 3 Ideal Current Spectrum

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International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print),
ISSN 0976 – 6553(Online) Volume 4, Issue 5, September – October (2013), © IAEME

III.

FREQUENCY SPECTRUM ANALYSIS BY LABVIEW

NI USB-6008 Data Acquisition device: The NI USB-6008 (National Instruments) data
acquisition device was used to record real diagnostic signals originating from the instrument panel
installed at the laboratory. The NI USB-6008 data acquisition device is shown in Fig 4. This data
acquisition device is equipped with 8 single analogue inputs (or 4 differential programmable analogue
inputs), 2 analogue outputs and 12 programmable digital I/O systems. The information received from
the input or output control signals are sent to the control unit (a PC) through a USB connection. The
signal transmitted between the data acquisition device and the PC conforms to fullspeed USB
standards.

Fig 4. Data acquisition device

Fig 5 Framework of data acquisition and analysis

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International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print),
ISSN 0976 – 6553(Online) Volume 4, Issue 5, September – October (2013), © IAEME

3-Phase
Induction Motor

Stator current
isolator

Data
Acquisition

Signal Conditioning

Signal Interface
with PC

Signal Processing
(FFT Analysis)

Result Visualization

Fig 6 Flowchart of Lab VIEW based DAQ

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International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print),
ISSN 0976 – 6553(Online) Volume 4, Issue 5, September – October (2013), © IAEME

Fig 7 Block diagram of the developed Virtual Instrument Panel
The rated data of the machine were: 3-Phase Squirrel Cage Induction Motor, Rated voltage
415 V rms, Frequency 50Hz, Rated Power 1HP, Rated Current 1.8 Amp, Rated Speed 1405 RPM,
Number of Poles 4. The motor was coupled with a DC Generator and the Generator was loaded with
rheostats.
Tests were carried out for different loads with the healthy motor and with similar motors
having upto 4 broken rotor bars. The rotor faults were kindled breaking the rotor bars by drilling into
the rotor.
The measured current signals were processed using the Fast Fourier Transformation (FFT)
through Virtual Instrumentation. The results obtained for the healthy motor and those having rotor
faults were compared, especially looking for the sideband components having the frequencies given
by equation (1) and (2).
As expected sideband frequency harmonics can be observed in the figures. Thus the theory of
the slip-frequency sidebands is demonstrated by experiments. As these sideband frequencies are
function of the slip, they are changing with the speed (implicitly with the load). This phenomenon can
be distinctly observed from Fig. 10, where the power density of the measured currents for the motor
having 4 broken bars are plotted for three different cases. As it can be ascertained, the magnitude of
the sideband frequency constituents is also increasing as the load is increased. As already stated, the
presence of the slip frequency sidebands establishes the existence of the broken rotor bars. The
magnitude is the function of the number of the broken bars.
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International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print),
ISSN 0976 – 6553(Online) Volume 4, Issue 5, September – October (2013), © IAEME

Fig 8 Stator current waveform and current spectrum in healthy motor at no load and speed 1400rpm

Fig 9 Stator current waveform and current spectrum in faulty state motor with load under speed 1310
rpm

Fig 10 Stator current waveform and current spectrum in faulty state motor with load at speed 1215
rpm

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International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print),
ISSN 0976 – 6553(Online) Volume 4, Issue 5, September – October (2013), © IAEME

IV.

CONCLUSIONS

From the huge number of obtained results here only the most significant ones are presented. In
this study, the major parts of interest are the effects of the broken rotor bar fault on motor stator
current spectrum under healthy and faulty conditions.
The current spectrums obtained from the current signal for four broken bars at no load and
with load are given in Figures 8 to 10 respectively. At no load condition, the side band frequencies
are close to fundamental frequency and the amplitudes of the sidebands are almost negligible. The
detection of the slip frequency sideband at no load or light load is quite difficult. Figure 10 shows the
current spectrum component of motor with four broken bars under load condition. It is observed from
the figure that broken bar fault detection at load performed in more reliable way. The frequency
components related to broken bar can be quite clearly recognized in the current spectrum.
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International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print),
ISSN 0976 – 6553(Online) Volume 4, Issue 5, September – October (2013), © IAEME

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AUTHORS

Prof. S. M. Shashidhara is working as Professor in Electronics &
Communication Engineering dept of Proudadhevaraya Institute of Technology,
Hospet, India. Member of ISTE, IEEE, Execom Member of Communications
Society, Bangalore. His areas of interest include Power Electronics, Power
Systems Protection, Digital Signal Processing and Communication Systems.

Dr. P. Sangameswara Raju, received Ph.D from Sri Venkateswara
University, Tirupati, Andhra Pradesh. He is working as Professor in the dept of
Electrical & Electronics Engineering, S.V. University. Tirupati, Andhra
Pradesh, India. He has over 50 publications in National and International
Journals and conferences to his credit. His areas of interest are Power Systems
operation, control and stability.

86

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