Open access peer-reviewed chapter

Wavelet-Based Analysis of MCSA for Fault Detection in Electrical Machine

Written By

Mohammad Rezazadeh Mehrjou, Norman Mariun, Mahdi Karami, Samsul Bahari Mohd. Noor, Sahar Zolfaghari, Norhisam Misron, Mohd Zainal Abidin Ab. Kadir, Mohd. Amran Mohd. Radzi and Mohammad Hamiruce Marhaban

Submitted: November 25th, 2014 Reviewed: September 15th, 2015 Published: December 9th, 2015

DOI: 10.5772/61532

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Early detection of irregularity in electrical machines is important because of their diversity of use in different fields. A proper fault detection scheme helps to stop the propagation of failure or limits its escalation to severe degrees, and thus it prevents unscheduled downtimes that cause loss of production and financial income. Among different modes of failures that may occur in the electrical machines, the rotor-related faults are around 20%. Successful detection of any failure in electrical machines is achieved by using a suitable condition monitoring followed by accurate signal processing techniques to extract the fault features. This article aims to present the extraction of features appearing in current signals using wavelet analysis when there is a rotor fault of eccentricity and broken rotor bar. In this respect, a brief explanation on rotor failures and different methods of condition monitoring with the purpose of rotor fault detection is provided. Then, motor current signature analysis, the fault-related features appeared in the current spectrum and wavelet transform analyses of the signal to extract these features are explained. Finally, two case studies involving the wavelet analysis of the current signal for the detection of rotor eccentricity and broken rotor bar are presented.


  • Wavelet transform
  • Line start permanent magnet motor
  • Induction motor
  • Eccentricity
  • Broken rotor bar

1. Introduction

Electrical machines are widely used for many industrial processes and play a non-substitutable role in a variety of industries [12]. In spite of their reliability and robustness, electrical machines are still prone to failures due to the exposure to a wide diversity of strict conditions and environments, incorrect operations, or even manufacturing defects [3]. These faults, gradual deterioration, and failures can lead to motor interruption, if left undetected, and their resulting unplanned downtime is very expensive. Early detection of irregularity in electrical machines with proper fault diagnosis schemes will help prevent high-cost failures, thereby decreasing the cost of maintenance and preventing unscheduled downtimes. However, stopping the propagation of the fault limits its escalation to severe degrees, which results in loss of production and financial income.

The fault identification schemes are basically based on data collection of electrical machines followed by signal processing. The condition of an electrical machine is examined from data that are acquired through sensors and supportive equipment methods. By using a suitable signal processing technique, each fault can be then detected via a specific feature present in the measured signal of a faulty motor when compared to a fault-free one. Hitherto, a number of data acquisition techniques, which display a certain parameter of the electrical machine, have been established. Essentially, the efficiency of a data acquisition method is characterized by its accuracy, cost, and importantly its capability to quantify the fault. On the contrary, condition monitoring based on data acquisition techniques requires the user to have adequate knowledge and proficiency to differentiate a normal operating condition from a potential failure state. The key step, but a difficult task, in the fault detection of electrical machines is to extract the fault-related features from the acquired signal and identify the condition of motor. Fault-related features are parameters derived from the acquired data that specify the existence of failure in device. The current signal of electrical machine is non-linear and non-stationary with strong noise interference; hence, the energy of early signal is too low to extract fault-related features in time domain [4]. Advanced signal processing methods based on analysis of time–frequency domain have been proposed as effective approaches for fault detection in electrical machines [5].

The focus of this chapter is on the extraction of features present in the current spectrum of electrical machine when one of two important rotor faults, eccentricity and broken rotor bar, exists. To extract features from the current spectrum in the presence of these faults, an advanced signal processing method, wavelet packet analysis, is used. In this regard, the fundamentals related to the detection of these two faults using wavelet packet analysis of current signal are explained in the following sections.


2. Rotor faults

From the investigations on different failure modes in electrical machines, the rotor-related faults are around 20% of failures may happen in the motor [6]. The rotor is exposed to different types of stresses that seriously affect its normal condition and subsequently create faults in it. Bonnett and Soukup explained the stresses that motors are subjected to and their unfavourable causes [7]. Failures in rotor are classified into eccentricity of rotor, crack and/or breakage of rotor cage bars, and crack and/or breakage of end rings and rotor bow [8]. These irregularities bring specific secondary failures that cause serious faults in electrical machines. Moreover, these types of faults may not show any symptoms during early stage until propagating to the next step and leading to the sudden collapse [911]. In recent years, rotor faults have been increasingly studied for developing advanced techniques that permit online early detection and diagnosis of motor faults to avoid any negative consequences of unexpected shutdowns, but this area still needs more research because of the complexity of the motor during the runtime. In this section, a brief description of different rotor faults is provided.

2.1. Rotor eccentricity

In a fault-free machine, the rotor is centre aligned in the stator bore that results in uniform air gap between the stator and rotor. In fault-free electrical machines, the rotation centre of the rotor is the same as the geometric centre of the stator bore. As a result, the rotor symmetrical axis (Cr), stator symmetrical axis (Cs), and rotor rotational axis (Cg) coincide with each other, and thus the magnetic forces are balanced in opposite directions. Rotor eccentricity, displacement of the rotor from its centred position in the stator bore, generates an asymmetric air gap between the stator and rotor [12]. The rotor eccentricity also produces unbalanced magnetic pull (UMP), which is a radial magnetic force on the rotor shaft. The UMP also pulls away the rotor from the stator bore centre, thus causing excessive stress on the electrical machine [13, 14]. Eccentricity commonly presents in rotating electrical machines, and the maximum permissible level of eccentricity is defined, which is 5 or 10% of the air-gap length [15]. If eccentricity exceeds the permissible level, it will increasingly damage the winding, stator core and rotor core in the motor due to rubbing of the stator with the rotor [12, 14]. Three different types of eccentricity occur in an electrical machine: static eccentricity, dynamic eccentricity, and mix eccentricity. As an example, static eccentricity is explained next.

Static eccentricity in electrical machines occurs when the rotor symmetrical axis is concentric with the rotor rotational axis; however, they are dislocated with respect to the stator symmetrical axis; hence, the position of minimum radial air-gap length is fixed. In this state, the mutual inductances across the stator and rotor as well as the self- and mutual inductances among the rotor phases are related to the angular position of the rotor [16]. The implication of static eccentricity fault in motor is depicted in Figure 1.

Static eccentricity can be due to numerous motives such as elliptical stator core, wrong placement of the rotor or stator at the setup or subsequent of maintenance, incorrect bearing positioning, bearing deterioration, shaft deflection, housing imperfection, end-shield misalignment, excessive tolerance, and rotor weight or pressure of interlocking ribbon [1719]. Static eccentricity leads to second failures which cause drastic harm to the rotor, stator core and windings. The radial forces in the static eccentricity condition produce a steady UMP in the radial route across the motor because the reluctance of the magnetic flux path decreases with the transmission of flux on the side of tiny air gap [20]. Albeit, the winding current induces more magnetic flux that causes a stronger pull and leads to the expansion of the air gap on the opposite side where the reluctance increases, thereby decreasing the flux and magnetic side pull. Therefore, the UMP compels the rotor to move toward the area of the narrowest air-gap length. During abrasion, the stator core subsequently generates abnormal vibration and severely damages the rotor, windings and the stator [7]. Consequently, the static eccentricity causes acoustic noise, premature failure in the bearing, rotor deflection and bent rotor shaft.

Figure 1.

Cross section of motor under static eccentricity fault

The degree of static eccentricity is calculated by the equation based on Figure 2 [16]:

DSE=|CsCg |gE1

where CsCgis the vector of static transfer which is invariant for rotor angular positions and gis the uniform air-gap length.

Figure 2.

Location of stator and rotor under static eccentricity condition

2.2. Rotor bar breakage

The breakage of rotor bars is one of the important failures in the rotor cage of electrical machines. During the operation of electrical machines, rotor bars may be broken partially or completely. The main reasons for bar breakage include electrical, mechanical, and environmental stresses during the operation of electrical machines and/or improper design of rotor geometry. Once a bar breaks, the stress increases and deteriorates the condition of the neighbouring bars progressively. Such a destructive process can be prevented, if any crack in the bar is detected early [21]. Typical causes of rotor bar breakage are referred as follows [22]: high thermal and mechanical stresses, direct online starting duty cycles for which the rotor cage was not well designed to endure against the stresses, imperfections in design and fabrication process of the rotor cage bars. Any failure in rotor bars itself causes unbalanced currents and torque pulsation and, therefore, decreases the average torque [23].

The rotor bars are short-circuited on both sides of the rotor by end rings. Depending on the type of squirrel cage in the motor, the source of failures in the end ring differs, in die-cast aluminium rotors caused by porosity of casting and in fabricated rotor cages caused by poor end-ring joints during manufacturing. Once the preliminary failure occurs, localized heat may extend to the rotor cage excessively. Therefore, the fault propagation is continued by multiple start-ups similar to load variations, which create high centrifugal forces. Accordingly, end-ring faults cause a drastic increase in the current and speed fluctuation [21].

2.3. Rotor bow

Any irregular thermal variation (heating or cooling) and unfavourable thermal distribution of the rotor during operation of electrical machines may bow the rotor [24]. The bow created in the rotor prevents sufficient alignment in the motor and generally produces a preload on the bearings. Bend locations in the rotor cause major failures in other parts of the motor [25]. The bow in the rotor is classified as local and extended [24]. When an asymmetrical heating is confined to a part of the rotor, a local bow is generated. For example, rotor-to-stator rubbing can generate a local asymmetric thermal distribution, which causes the local bow. When an asymmetrical heating extends along the rotor, an extended bow is generated. Long-lasting gravity effects on off-line machines generate rotor bow classified as an extended bow, when unsuitable rotor straightening turning system is not used [24]. Since the rotor is limited by two bearings, extended bow commonly causes a shaft bow [24].


3. Condition monitoring techniques for rotor fault detection

Condition monitoring programme which can predict a failure in electrical machines has received considerable attention for many years [2, 8]. Successful detection of any failure in electrical machines is achieved by using suitable condition monitoring. When a failure occurs, some machine parameters are exposed to changes that depend upon the fault degree. Any irregularity in the rotor of electrical machines presents with variation distributed in the rotor currents. The feedback of these currents to the air-gap field produces specific signatures of fault in the spectrum of speed, torque, current, and power. Reliable condition monitoring techniques depend on the best understanding of the mechanical and electrical characteristics of the electrical machines in both fault-free and faulty situations. Researchers have used different condition monitoring techniques that can be categorized as follows [8]:

  • Acoustic emission

  • Air-gap torque

  • Current

  • Electromagnetic field monitoring

  • Induced voltage

  • Instantaneous angular speed

  • Motor circuit analysis

  • Power

  • Surge testing

  • Vibration

  • Voltage

3.1. Motor current signature analysis

The drowned current signal by an electrical machine contains a single component. Any magnetic or mechanical asymmetries in the machine generate other frequency components in the stator current spectrum. These frequency components are diverse according to each specific fault in the machine.

Motor current signature analysis (MCSA) analyses the stator current signal to identify the presence of any failure in electrical machines. This analysis method has been introduced as an effective way for monitoring electrical machines for many years [8]. From all these methods suggested in the literature, MCSA is a forerunner because of its advantages [10, 13, 2628]:

  • Online monitoring characteristics

  • Remote monitoring ability

  • Non-invasive feature

  • Inexpensive equipment and easy measurement

  • Different fault detection capability (such as broken rotor bars, air-gap eccentricity, stator faults, etc.)

  • Early-stage fault detection

  • Highly sensitive

  • Selective

When a failure is generated in the electrical machine, depending on the severity of this fault, some of the machine parameters change. For instance, the current spectrum of an ideal electrical machine contains a single component corresponding to the supply frequency. Any asymmetry in electrical machine causes other components to appear in a spectrum of stator current. When a rotor bar breaks, current does not flow through it, and hence no magnetic flux is created around the breakage bar. Therefore, there is no non-zero backward rotating field that rotates at the slip frequency speed with respect to the rotor. This asymmetry in the magnetic field of rotor induces harmonics in stator windings, which are superimposed on it. These superimposed harmonics appear at frequency spectrums as described in

fBRB=[1±2KS]fS E2

where fBRBis the harmonic component due to broken rotor bar, S is the slip, fS is the fundamental frequency, and k= 1, 2,... [29].

Any asymmetry caused by static eccentricity produced other components that appear in the spectrum of stator current. The characteristic frequency component associated with static eccentricity is located according to Eq. (3) [16]:

fstatic=[1±mp]f E3

where fstaticis the harmonic component due to static eccentricity in line start permanent magnet synchronous motor (LSPMSM), mis an odd integer value, pis the number of pole pair, and fis the line frequency.


4. Wavelet

Frequency domain analysis is not reliable for fault detection because some outside parameters can affect the location and amplitude of fault-related feature. This parameter can be classified as follows: first, the fault frequency components depend on the slip of the motor; second, the fault feature amplitude is load dependent; third, the frequencies of the fault components are affected by voltage fluctuations; and fourth, long sampling interval is needed for a high-resolution frequency. Therefore, in general, frequency domain analyses are suitable for the steady-state situation. The problem involved in the analysis of non-stationary signals can be shunned by time–frequency analysis of the signal, which illustrates the signal in three-dimensional axis as time, frequency, and amplitude. The most popular time–frequency representations include Wigner–Ville distribution, short-time Fourier transform, and wavelet transform.

Wavelet transform expresses a signal in oscillatory function series at different frequencies and time. Wavelet transform divides the original signal into time-scale space, where the dimension of windows at time and scale (frequency) is not rigid [30]. Therefore, in fault diagnostics domain, wavelet transform has been used to extract the dominant features from original signals [31]. Various types of wavelet transforms have been widely used in the condition monitoring of electrical machine. Among all these techniques, discrete wavelet transform and wavelet packet transform (WPT) are the most common ones, explained in the following.

4.1. The principle of discrete wavelet decomposition

Discrete wavelet transform is based on signal analyses using a minor set of scales and specific number of translations at each scale. Mallat (1989) introduced a practical version of discrete wavelet transform called wavelet multi-resolution analysis [32]. This algorithm is based on the fact that one signal is disintegrated into a series of minor waves belonging to a wavelet family.

A discrete signal f[t] could be decomposed as


where ϕis the scaling function (father wavelet) and ψis the wavelet function (mother wavelet), Ais the approximate coefficient and Dis the detail coefficient.

The multi-resolution analysis commonly uses discrete dyadic wavelet, in which positions and scales are based on powers of two. In this approach, the scaling function is depicted by the following equation:


that is, ϕm0,nis the scaling function at a scale of 2m0shifted by n. Wavelet function is also defined as


that is, ψm,nis the mother wavelet at a scale of 2mshifted by n.

Generally, approximate coefficients  Am0,nare obtained through the inner product of the original signal and the scaling function


The approximate coefficients decomposed from a discretized signal can be expressed as


In the dyadic approach, the approximation coefficients Am0,nare at a scale of 2m0. The filter, g[n], is a low-pass filter. Similarly, the detail coefficients Dm,ncan be generally obtained through the inner product of the signal and the complex conjugate of the wavelet function:


The detail coefficients decomposed from a discretized signal can be expressed as


In the dyadic approach, Dm,nare the detail coefficients at a scale of  2m0. The filter, h[n]is a high-pass filter.

The multi-resolution analysis utilizes discrete dyadic wavelet and extract approximations of the original signal at different levels of resolution. An approximation is a low-resolution representation of the original signal. The approximation at a resolution 2mcan be split into an approximation at a coarser resolution 2m1and the detail. The detail represents the high-frequency contents of the signal. The approximations and details can be determined using low- and high-pass filters. In the multi-resolution analysis, the approximations are split successively, while the details are never analysed further. The decomposition process can be iterated, with successive approximations being decomposed in turn; hence one signal is broken down into many lower-resolution components. This process is called the wavelet decomposition tree as shown in Figure 3. It illustrates the dyadic wavelet decomposition algorithm regarding the coefficients of the transform at different levels according to the description by Polikar et al. (1998) [33].

Figure 3.

Dyadic wavelet decomposition algorithm [34].

4.2. The principle of wavelet packet decomposition

The wavelet packet transform is a direct expansion of discrete wavelet transform, where the details as well as approximation are split up. Therefore, this tree algorithm is a full binary tree that offers rich possibilities for signal processing and better signal representation in comparison to a discrete one.

A wavelet packet function has three naturally interpreted indices in time–frequency functions:

ψj,ki=2j2ψi(2jtk), i=1, 2, 3,... E11

where integers j, k,and iare called the scale, translation, and simulation parameters, respectively. Scaled filter h(n) and the wavelet filter g(n) are quadrature mirror filters associated with the scaling function Φ(t) and the wavelet function ψ(t) [32]. The conjugate mirror filters hand gwith finite impulse responses (FIRs) of size kcan define the fast binary wavelet packet decomposition (WPD) algorithm of the signal f(t):

{d00(t)=f(t)dj+12n(t)=kh(k2t)dji i=0, 1,.....,2j1dj+12n+1(t)=kg(k2t)dji E12

The wavelet packet component signals fji(t)are produced by a combination of wavelet packet function ψj,kn(t)as follows:


where the wavelet packet coefficients Cj,ki(t)are calculated by


Provided the wavelet packet functions are orthogonal

ψj,km(t)ψj,kn(t)=0 if mnE15

As data sets of wavelet packet coefficients increase in size, the energy principle is applied to current signals after WPT for fault location estimation [35].

4.3. A review of wavelet decomposition for fault detection

Different types of wavelet transform techniques have been widely used in algorithms designed for fault detection in electrical machines. Table 1 presents the common types of these techniques.

RefYearDiagnostic Monitoring Techniques (MTs)Signal ProcessingClassifier and Decision-making ToolPurposeAchievement and Limitation
short time fourier transform(STFT), continuous wavelet transform(CWT), Wigner distribution-To compare individual signal processing techniques using both test and actual dataThe CWT has been proven to be the most efficient technique for the extraction of the frequency component of interest. Limitation: available. The technique would be of particular use to industrial applications where motors are frequently started on no load, or have been moved to a workshop environment where fully loaded conditions are neither practical nor achievable
[36]2001Motor current signature analysis (MCSA)Discrete wavelet transform (DWT)-To develop current monitoring procedure for BRB detectionA new approach in detection of BRB having only stator current signal
[37]2002MCSAWT,park transform(PT)-To compare model-based and signal-based approaches based on Park transform for BRB detectionThe spectral decomposition obtained by the wavelet transform may be used to isolate different kinds of faults
[38]2002MCSA, voltage, speedWavelet packet decomposition (WPD)Artificial neural networkTo develop a model-based diagnosis system for detection of various faults including BRBThe proposed system was shown effective in detecting early stages of different IM faults
[39]2003MCSAWPDArtificial neural networkTo improve MCSA monitoring procedure for BRB and air-gap eccentricity detectionIt provides feature representations of multiple frequency resolutions for faulty modes
DWT-To improve the start-up current monitoring procedure for BRB detection using a filter that actively tracks the changing amplitude, phase and frequency to extract the fundamental from the transientThis method does not require parameters such as speed or number of rotor bars. It is not load dependent and can be applied to IMs that operate continuously in the transient mode
[41]2005MCSAfast fourier transform (FFT), WPD-To improve MCSA monitoring procedure for the detection of various faults including BRBThe features of BRB and static eccentricity yield similar results in the wavelet analysis, but are different in Fourier analysis. Therefore the use of both types of analysis together can distinguish the faults
DWT-To develop start-up current monitoring procedure for BRB detectionThe method is not load dependent and can be effective on small lightly loaded machines
CWT-To develop start-up current monitoring procedure using envelope extraction of current spectrum for BRB detectionThe procedure is not affected by other factors such as initial rotor position, phase of the supply and supply imbalance. It is able to classify the different degrees of BRB. Limitation: A partial BRB could not be indicated
[44]2006MCSAWPDAdaptive neuro-fuzzyTo present a novel online diagnostic algorithm for BRB and air-gap eccentricity detection in variable speed drive systemsAlthough the algorithm is able to detect the fault with high accuracy, the number of training iterations and the CPU processing time were reduced
[45]2006Instantaneous powerDWT-To improve IP monitoring for BRB detection under various load levelsWavelet approach applied to IP showed superior ability for BRB detection compared to the frequency domain analysis
[46]2006MCSAFFT, WPDFuzzy entropy– artificial neural networkTo improve MCSA monitoring procedure for the detection of various faults including BRBAn approach was proposed based on Fourier transformation and wavelet transform and neural network system to classify the faults
DWT-To develop start-up current monitoring procedure for BRB detection. To compare the influence of the discrete wavelet transform parameters (type of mother wavelet, order of the mother wavelet, sampling rate or number of levels of the decomposition) over the diagnosisThe tests show that if the start-up transient is not very short, the reliability of the proposed method for BRB detection is similar to that of the classical approach, based on the Fourier transform, in the case of loaded motors. In addition, the method can detect faults in an unloaded condition, and it allows a correct diagnosis of a fault-free machine in some particular cases where Fourier analysis leads to an incorrect fault diagnosis
[48]2007Induced voltageFFT, WT-To investigate the limitations and harmonics of the induced voltage after supply disconnection harmonics for BRB detectionFourier transform did not provide information about fault severity and load variations. A method based on wavelet analysis of induced voltage spectrum was developed for BRB detection Limitation: Tests need to be carried out for fault-free motor to develop a baseline response. It is sensitive to changes in load, system inertia, rotor temperature and supply voltage
[49]2007MCSAWPD-To detect incipient bearing fault via stator current analysisCover better analysis under various conditions and more tolerant frequency bands with WPD method
[50]2007MCSAWPD-To detect real-time fault for various disturbances in three-phase IMSelecting the optimal levels of decomposition and optimum mother wavelet
[51]2008MCSASTFT, WT-To improve MCSA monitoring procedure for BRB and stator shorted turns detectionWavelet decomposition is superior to STFT. Power spectral density for wavelet details was introduced as a merit factor for fault diagnosis. The proposed method can diagnose shorted turns and BRB in non-constant load–torque IM applications
DWTPrinciple component analysis (PCA), kernel PCA, support vector machineTo develop transient current monitoring procedure; uses intelligent system for detection and classification of various faults including BRBFeature reduction and extraction using component analysis via PCA and KPCA are highlighted. The performance of WSVM is validated by applying it to fault detection and classification of induction motor based on start-up transient current signal. Limitation: A proper pre-processing for the transient current signal is needed to improve the emerging salient differences between conditions in induction motors
[35]2008MCSAWPDArtificial neural networkTo estimate transmission line faultA powerful and reliable method by applying the energy criterion after wavelet packet transform (WPT) for reducing the data size
[53]2009MCSAWPDArtificial neural networkTo propose a novel online diagnosis algorithm for BRB detectionThe diagnosis can be performed with reduced load condition. An accurate measurement of the slip speed is not necessary
DWT-To develop start-up current monitoring procedure for distinguishing various faults including BRB and other phenomena, such as load–torque oscillationsThe proposed methodology showed promising ability for the reliable discrimination of simultaneous electromechanical faults and the diagnosis of faults combined with other phenomena
[55]2009Current (envelopes)DWT-To propose a new technique, slip independent, for BRB detection under different load levelsThe proposed method gives the same reliable results for BRB detection under different load levels when applying to the stator-current space–vector magnitude and the instantaneous magnitude of the stator-current signal
[56]2009VibrationWPDArtificial Neural NetworkTo optimize gear failure identification using GAs and ANNsThe technique determines the best values ’mother wavelet’, ’decomposition level’ and ’number of neurons in hidden layer’
[57]2009VibrationWPDHybrid support machineTo propose an intelligent method to diagnose rotating machinery failuresAn accurate and quick fault-type estimation method by applying hybrid SVM to the energy criterion after WPA
[58]2009MCSADWT-To compare a different wavelet family for BRB fault detectionThe technique determines the best mother wavelet
[59]2010MCSAWT,PSD-To develop BRB detection methods based on MCSAThe method has the ability to detect BRB for both constant torque and for variable load torque
[60]2011MCSAFFT,WT-To propose a new method for early fault detectionThe approach has been proved to be effective to detect failures in its very early stages
[4]2012VibrationWPD,EMDArtificial neural networkTo integrate the fine resolution advantage of WPD with the self-adaptive filtering characteristics of empirical mode decomposition (EMD) to early fault diagnosisAbility to extract weak signals and early fault detection of rotating machinery
[61]2013MCSAStationary WPDMulticlass support vector machinesBRB feature extraction by SWPT under lower-sampling rateLower computation and cost without any effect on the performance of SWPT to detect BRB
[62]2013VibrationWPD,FFTArtificial neural networkTo classify fault and predict remaining useful lifeTo deal with complex problems and non-linear systems and predict remaining useful life
[63]2014VibrationWPD-To improve the accuracy and rectify the distortion of WPT coefficientsMagnifying the amplitude of the fault characteristic frequency
[34]2014MCSADWT-To investigate the ability of different types of wavelet functions for early BRB detectionThe reliability of the fault detection depends on the type of wavelet function applied for decomposition of the signal
[64]2014Apparent powerDWT-To develop air-gap eccentricity fault detection methods based on apparent powerThe energy evaluation of a known bandwidth permits to define a fault severity factor (FSF)
[65]2014MCSADWTFuzzy support vector machine, principal component analysis, kernel neural networkTo develop eccentricity fault detection methods and degree precisely based on MCSA in PMSMThe novel index for eccentricity fault diagnosis is introduced based on energy, peak, head angle of the peak, the area below the peak, the gradient of the peak and coefficients of the autoregressive (AR) model

Table 1.

Summary of published paper with the aim of using wavelet transform for broken rotor bar and eccentricity fault detection


5. Case study 1

The detection of static eccentricity in three-phase LSPMSM using motor current signature analysis is studied. A detailed description of experimental test rig used in this study and the method used for signal measurement and analysis is provided in the following.

5.1. Experimental set-up

The experimental test rig is shown in Figure 4. The tested motor for both fault-free and faulty (with static eccentricity) cases is a three-phase LSPMSM with the specification as mentioned in Table 2. The motor is directly fed by the grid power supply, while the stator windings are Y connected, and the current nominal value is 1.28 A. The LSPMSM is coupled to torque/speed sensor in order to measure the torque value in different operation conditions. On the other side, a mechanical load is provided by a DC-excited magnetic powder brake (MPB) coupled to torque/speed sensor. The specific load torque level could be furnished to the motor shaft by controlling the input dc voltage of MPB. This system is used to sample the stator current non-invasively when the motor is operated in the steady-state condition. Notably, only one phase-current signal is required to be recorded for the detection process in this study. The recorded signals are analysed by a computer-based signal processing program.

Figure 4.

Experimental test rig

5.2. Method

The method proposed in this study for the creation of eccentricity fault in LSPMSM, by changing the original bearings of motor with a new set of bearing with larger inner diameter and smaller outer diameter, results in the creation of free space between the shaft and bearings and also between the bearings and the housing of end shields. Static eccentricity is created by fixing concentric inner rings between the new bearings and shaft on both ends of LSPMSM and non-concentric outer rings between the new bearings and housings of both end shields. The aforementioned strategy is used to create 33% and 50% static eccentricity in the motor discussed in the case study.

Rated output power (HP)1
Rated voltage (V)415
Rated frequency (Hz)50
Number of poles4
Rated speed (RPM)1500
Air-gap length (mm)0.30

Table 2.

Specification of three-phase LSPMSM

The current spectrum is stored with the sampling frequency (fS) of 5 kHz over a total sampling period of 6.5 s, which allows the analysis of the signals with a minimum frequency of 0.15 Hz. Daubechies-24 (db24) is used as the mother wavelet in discrete wavelet transform (DWT) analyses. Since the four-pole, three-phase LSPMSM is considered, the characteristic frequency component associated with static eccentricity is located at 25 Hz, according to Eq. (3) [16].

The number of decomposition levels (ld) can be determined using Eq. (16) which is ld=7in this case.

ld=log(fS/fstatic)log(2) E16

The frequency bands of wavelet signals are summarized in Table 3. Energies of the detail coefficient E(Dj)are calculated using the following formulas [45]:

E(Dj)=1Nli=1Nl(Dj)2[i] E17

where j=1, 2, ..., ldand Nlis the data length of the decomposition level.

SignalFrequency band

Table 3.

The frequency bands of wavelet signals

5.3. Results and discussion

Figure 5 shows the stator current signal (original signal) and D5, D6, and D7 are the detail signals obtained by db24 at level 7 for fault-free LSPMSM. The fault-related components (fstatic) are visible at 25 Hz, which confirm the productivity of D7 signal for the detection of static eccentricity. The original and detail signals of LSPMSM with 33% and 50% static eccentricity are indicated in Figures 6 and 7, respectively.

Figure 5.

DWT analysis of current signal of fault-free LSPMSM

A comparison between Figures 5, 6, and 7 shows that the signals of D7 are clear from any distortion in a fault-free motor while the high distortions are manifested in D7 in the presence of static eccentricity that demonstrates the faulty condition of LSPMSM. The source of these distortions is due to the increase in the amplitudes of fault-related frequency components based on Eq. (3).

Figure 6.

DWT analysis of current signal of LSPMSM under 33% static eccentricity

Figure 7.

DWT analysis of current signal of LSPMSM under 50% static eccentricity

An effective static eccentricity detection index is introduced for three-phase LSPMSM based on the energy of D7 for the stator current signal. The proposed index is examined for fault-free and eccentric LSPMSM with 33% and 50% static eccentricity as shown in Figure 8. The energy variation of D7 (index) for stator current signal using db24 is provided in Table 4.

Figure 8.

Static eccentricity severity versus proposed index (energy ofD7)

IndexStatic eccentricity degree (%)
Energy of D7193320378

Table 4.

Evaluation of proposed index due to fault degree

5.4. Conclusion

Discrete wavelet transform is employed to analyse the stator current signal of three-phase LSPMSM in order to propose an effective index for static eccentricity fault detection. The energy of detail signal (D7) is introduced as eccentricity index. The achieved results confirm the productivity of the proposed method for the motor discussed in the case study.


6. Case study 2

The detection of broken rotor bar in three-phase squirrel cage induction motor using motor current signature analysis is studied. A detailed description of experimental test rig used in this study and the method used for signal measurement and analysis is explained in the following.

6.1. Experimental set-up

Figure 9 illustrates the experimental test rig used in this study. Table 5 presents the parameters of the three-phase squirrel cage induction motor used for both fault-free and faulty motors. The faulty motor is with three broken rotor bars. The motor is directly fed by the grid power supply, while the stator windings are Y connected and the current nominal value is 2.2 A. In order to measure the torque and speed value of the squirrel cage induction motor in different operation conditions, a torque/speed sensor is coupled to it. A generator is used as a load and the specific load torque level can be furnished to the motor shaft by controlling the resistor connected to the generator. Recall that only one phase current signal is required to be recorded for the detection process in this study. The recorded signals are then analysed by a computer-based signal processing program.

Figure 9.

Experimental set-up

Rated output power (HP)1
Rated voltage (V)415
Rated frequency (Hz)50
Number of poles6
Rated speed (RPM)1000
Number of rotor bars28

Table 5.

Specification of a three-phase squirrel cage induction motor

6.2. Method

The architecture of the proposed system for broken rotor bar detection is shown in Figure 10, and the procedure used in this study is as follows: First, to force a real bar breakage in the rotor, a hole is drilled artificially in it. The original stator current was recorded from a three-phase induction motor. The stator current is sampled at 20 kHz lasting four seconds for both fault-free and faulty motors (three-rotor bar breakage) at 80% full load. The measured current signals are then decomposed using wavelet packet transform with two different purposes: One as a pre-processing of signal for FFT analysis and the frequency of ((12s)fS) obtained is used as a fault feature for broken rotor bar detection. The other purpose of using WPT is for feature extraction, where some statistical features determined by wavelet packet coefficients are used for broken rotor bar detection. For both purposes, Daubechies-44 (db44) is applied as a mother wavelet in 12 levels of decomposition. To extract the fault-related feature, those nodes are taken that involve fault frequency (fBRB). Figure 11 shows the process explained above, called the wavelet packet tree. In this work, the signal energy, root mean square (RMS), and kurtosis are obtained as selected features for the diagnosis of the broken rotor bar.

Figure 10.

The architecture of the proposed system for broken rotor bar detection

Figure 11.

Approximations and details in wavelet packet decomposition

6.3. Results and discussion

In order to obtain the differences between fault-free and faulty conditions under 80% full-load conditions, WPD was used for the feature extraction. The WPD gives distinguishable signatures from stator current signal in a specific frequency band. After WPD of the current signal, two procedures for failure feature extraction using WPD are used (Figure 10). One procedure includes using FFT for the determination of amplitude of fault frequency and the other includes the statistical analysis of coefficients extracted by WPD.

The amplitude of fault frequency in the current spectrum for fault-free motors and for motors with three broken bars achieved in the first procedure is presented in Table 6. The results indicate that the amplitude of harmonic components ((12s)fS) in both nodes, presented in Table 6, increase the faulty condition. However, the degree of increase is not significant, and it cannot be used to differentiate the conditions.

NodeFrequency BandAmplitude (1-2s)fs
[10, 6](39.06–48.83)0.430.43
[11, 13](46.39–48.83)0.420.42
fBRB =(1–2s)fS =47.604 Hz

Table 6.

Amplitudes of harmonic components for fault-free and faulty motors

In the second procedure, three statistical parameters including RMS, kurtosis and energy are calculated using the statistical analysis of coefficients determined by WPD of current signal. Table 7 presents these statistical parameters in three different nodes [10, 6], [11, 13] and [12, 26]. These parameters are compared to define the most appropriate frequency band that represents the frequency components from the broken rotor bar. According to Table 7, the nodes [11, 3] (46.39–48.83 Hz) in wavelet packet tree are the most dominant bands that can differentiate between fault-free and faulty motors under full load.

FeatureCondition[10, 6][11, 13][12, 26]

Table 7.

Statistical features for fault-free and faulty motors

6.4. Conclusions

This case study proposes a feature extraction system for broken rotor bar detection using wavelet packet coefficients of the stator current. It is shown that in a faulty case, the amplitude in specific side bands increases and dominant features of signals can be extracted for fault diagnostics. The results of this study indicate that the energy, kurtosis and RMS value of WPD coefficients are the appropriate features for detecting broken rotor bar in particular bands.



The authors would like to express their gratitude to the Ministry of Education, Malaysia for their financial support through grant number FRGS-5524356 and Universiti Putra Malaysia for the facilities provided during this research work.


  1. 1. Didier G, Ternisien E, Caspary O, Razik H. A new approach to detect broken rotor bars in induction machines by current spectrum analysis. Mechanical Systems and Signal Processing. 2007; 21: 1127–1142.
  2. 2. Andrew JK, Lin D, Dragan B. A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mechanical Systems and Signal Processing. 2006; 20: 1483–1510.
  3. 3. Chow MY. Guest editorial special section on motor fault detection and diagnosis. IEEE Transactions on Industrial Electronics. 2000; 47.5: 982–983.
  4. 4. Bin GF, Gao JJ, Li XJ, Dhillon BS. Early fault diagnosis of rotating machinery based on wavelet packets – Empirical mode decomposition feature extraction and neural network. Mechanical Systems and Signal Processing. 2012; 27: 696–711.
  5. 5. Burnett R, Watson J, Elder S. The application of modern signal processing techniques to rotor fault detection and location within three phase induction motors. Signal processing. 1996; 49.1: 57–70.
  6. 6. Bonnett AH, Yung C. Increased efficiency versus increased reliability. IEEE Industry Applications Magazine. 2008; 14.1: 29–36.
  7. 7. Bonnett AH, Soukup GC. Cause and analysis of stator and rotor failures in three-phase squirrel-cage induction motors. IEEE Transactions on Industry Applications. 1992; 28.4: 921–937.
  8. 8. Mehrjou MR, Mariun N, Hamiruce Marhaban M, Misron N. Rotor fault condition monitoring techniques for squirrel-cage induction machine – a review. Mechanical Systems and Signal Processing. 2011; 25.8: 2827–2848.
  9. 9. Ebrahimi BM, Faiz J, Lotfi-fard S, Pillay P. Novel indices for broken rotor bars fault diagnosis in induction motors using wavelet transform. Mechanical Systems and Signal Processing. 2012; 131–145.
  10. 10. Thomson WT, Fenger M. Current signature analysis to detect induction motor faults. IEEE Industry Applications Magazine. 2001; 7.4: 26–34.
  11. 11. Bellini A, Filippetti F, Tassoni C, Capolino GA. Advances in diagnostic techniques for induction machines. IEEE Transactions on Industrial Electronics. 2008; 55.12: 4109–4126.
  12. 12. Cameron JR, Thomson WT, Dow AB. Vibration and current monitoring for detecting airgap eccentricity in large induction motors. In: Proceedings of IEE Electric Power Applications; 1986. pp. 155–163.
  13. 13. Nandi S, Toliyat HA, Li X. Condition monitoring and fault diagnosis of electrical motors – a review. IEEE Transactions on Energy Conversion. 2005; 20.4: 719–729.
  14. 14. Faiz J, Ojaghi M. Different indexes for eccentricity faults diagnosis in three-phase squirrel-cage induction motors: a review. Mechatronics. 2009; b19: 2–13.
  15. 15. Thomson WT, Gilmore RJ. Motor current signature analysis to detect faults in induction motor drives–fundamentals, data interpretation and industrial case histories. In: Proceedings of the 32nd Turbomachinery Symposium, Houston, TX, USA; 2003. pp. 145–156.
  16. 16. Karami M, Mariun N, Mehrjou MR, Ab Kadir MZA, Misron N, Mohd Radzi MA. Static eccentricity fault recognition in three-phase line start permanent magnet synchronous motor using finite element method. Mathematical Problems in Engineering. 2014; 1–12.
  17. 17. Torkaman H, Afjei E. Sensorless method for eccentricity fault monitoring and diagnosis in switched reluctance machines based on stator voltage signature. IEEE Transactions on Magnetics. 2013; 49.2: 912–920
  18. 18. Hong J, Park S, Hyun D, Kang T, Lee SB, Kral C, Haumer A. Detection and classification of rotor demagnetization and eccentricity faults for PM synchronous motors. IEEE Transactions on Industry Applications. 2012; 48.3: 923–932.
  19. 19. Wiedenbrug EJ, Teska M, Nandi S, Chelvan IT. Automated monitoring of airgap eccentricity for inverter-fed induction motors under standstill conditions. IEEE Transactions on Industry Applications. 2011; 47.3: 1257–1266.
  20. 20. Dorrell DG, Cossar C. A vibration-based condition monitoring system for switched reluctance machine rotor eccentricity detection. IEEE Transactions on Magnetics. 2008; 44.9: 2204–2214.
  21. 21. Nandi S, Bharadwaj R, Toliyat HA, Parlos AG. Study of three phase induction motors with incipient rotor cage faults under different supply conditions. In: Proceedings of IEEE Industry Applications Conference, 34th IAS Annual Meeting; 1999. pp. 1922–1928.
  22. 22. Sasi AB, Gu F, Li Y, Ball AD. A validated model for the prediction of rotor bar failure in squirrel-cage motors using instantaneous angular speed. Mechanical Systems and Signal Processing. 2006; 20.7: 1572–1589.
  23. 23. Ilonen J, Kamarainen JK, Lindh T, Ahola J, Kälviäinen H, Partanen J. Diagnosis tool for motor condition monitoring. IEEE Transactions on Industry Applications. 2005; 41.4: 963–971.
  24. 24. Pennacchi P, Bachschmid N, Vania A, Zanetta GA, Gregori L. Use of modal representation for the supporting structure in model-based fault identification of large rotating machinery : Part 1- theoretical remarks. Mechanical Systems and Signal Processing. 2006; 20.3: 662–681.
  25. 25. Bachschmid N, Pennacchi P, Tanzi E. Cracked rotors: a survey on static and dynamic behaviour including modelling and diagnosis. Springer Science & Business Media, 2010. ISBN 978-3-642-01484-0.
  26. 26. Siddique A, Yadava GS, Singh B. A review of stator fault monitoring techniques of induction motors. IEEE Transactions on energy conversion. 2005; 20.1: 106–114.
  27. 27. Thorsen OV, Dalva M. A survey of faults on induction motors in offshore oil industry, petrochemical industry, gas terminals, and oil refineries. IEEE Transactions on Industry Applications. 1995; 31.5: 1186–1196.
  28. 28. Acosta GG, Verucchi CJ, Gelso ER. A current monitoring system for diagnosing electrical failures in induction motors. Mechanical Systems and Signal Processing. 2006; 20: 953–965.
  29. 29. Kliman GB, Koegl RA, Stein J, Endicott RD, Madden MW. Noninvasive detection of broken rotor bars in operating induction motors. IEEE Transactions Energy Conversion 1988; 3.4: 873–879, 1988
  30. 30. Daubechies I. The wavelet transform, time-frequency localization and signal analysis. IEEE transactions on information theory. 1990; 36.5: 961–1005.
  31. 31. Peng ZK, Chu FL. Application of the wavelet transform in machine condition monitoring and fault diagnostics: a review with bibliography. Mechanical Systems and Signal Processing. 2004; 18.2: 199–221.
  32. 32. Mallat SG. A theory for multiresolution signal decomposition: the wavelet representation. IEEE Transactions on Pattern Analysis and Machine Intelligence. 1989; 11.7: 674–693.
  33. 33. Polikar R, Udpa L, Udpa SS, Taylor T. Frequency invariant classification of weld inspection signals. IEEE Ultrasonics Ferroelectrics and Frequency Control. 1998; 45.3: 614–625.
  34. 34. Mehrjou M R, Mariun N, Karami M, Misron N, Toosi S, Zare MR. Evaluation of wavelet-functions for broken rotor bar detection of induction machine using coefficient-related features. International Journal of Applied Electronics in Physics & Robotics. 2013; 1.1: 18–23.
  35. 35. Ekici S, Yildirim S, Poyraz M. Energy and entropy-based feature extraction for locating fault on transmission lines by using neural network and wavelet packet decomposition. Expert System Application. 2008; 34.4: 2937–2944.
  36. 36. Abbaszadeh K, Milimonfared J, Haji M, Toliyat HA. Broken bar detection in induction motor via wavelet transformation. In: Proceedings of the 27th Annual Conference of the IEEE in Industrial Electronics Society; 2001. pp. 95–99.
  37. 37. Combastel C, Lesecq S, Petropol S, Gentil S. Model-based and wavelet approaches to induction motor on-line fault detection. Control Engineering Practice. 2002; 10.5: 493–509.
  38. 38. Kim K, Parlos AG. Induction motor fault diagnosis based on neuropredictors and wavelet signal processing. IEEE/ASME Transactions Mechatronics. 2002; 7.2: 201–219.
  39. 39. Ye Z, Wu B, Sadeghian A. Current signature analysis of induction motor mechanical faults by wavelet packet decomposition. IEEE Transactions on Industry Electronics. 2003; 50.6: 1217–1228.
  40. 40. Douglas H, Pillay P, Ziarani A. A new algorithm for transient motor current signature analysis using wavelets. IEEE Transactions on Industry Applications. 2004; 40.5: 1361–1368.
  41. 41. Bae H, Kim S, Kim YT, Lee SH. Application of time-series data mining for fault diagnosis of induction motors. Lecture Notes in Computer Science Computational Science and Its Applications. 2005; 1085–1094.
  42. 42. Douglas H, Pillay P, Ziarani A. Broken rotor bar detection in induction machines with transient operating speeds. IEEE Transactions Energy Conversion. 2005; 20.1: 135–141.
  43. 43. Supangat R, Ertugrul N, Soong L, Gray D, Hansen C, Grieger J. Broken rotor bar fault detection in induction motors using starting current analysis, In: Proceedings of European Conference on Power Electronics and Application, Dresden, Germany, 2005.
  44. 44. Ye Z, Sadeghian A, Wu B. Mechanical fault diagnostics for induction motor with variable speed drives using adaptive neuro-fuzzy inference system. Electric Power Systems Research. 2006; 9, 10: 742–752.
  45. 45. Kia SH, Mabwe AM, Henao H, Capolino GA. Wavelet based instantaneous power analysis for induction machine fault diagnosis. In: Proceedings 32nd Annual Conference on IEEE Industrial Electronics, IECON; 2006. pp. 1229–1234.
  46. 46. Lee SH, Cheon SP, Kim Y, Kim S. Fourier and wavelet transformations for the fault detection of induction motor with stator current. Lecture Notes in Computer Science. Springer. 2006; 4114: 557–569.
  47. 47. Antonino-Daviu J, Riera-Guasp M, Roger-Folch J, Palomares M. Validation of a new method for the diagnosis of rotor bar failures via wavelet transform in industrial induction machines. IEEE Transactions on Industry Applications. 2006; 42.4: 990–996.
  48. 48. Supangat R, Grieger J, Ertugrul N, Soong WL, Gray DA, Hansen C. Detection of broken rotor bar faults and effects of loading in induction motors during rundown. In: Proceedings of IEEE International Electric Machines and Drives Conference; 2007. pp. 196–201.
  49. 49. Zarei J, Poshtan J. Bearing fault detection using wavelet packet transform of induction motor stator current. Tribology International. 2007; 40: 763–769.
  50. 50. Khan MASK, Member S, Radwan TS, Member S, Rahman MA. Real-time implementation of wavelet packet transform-based diagnosis and protection of three-phase induction motors. IEEE Transactions on Energy Conversion. 2007; 22.3: 647–655.
  51. 51. Cusido J, Romeral L, Ortega JA, Rosero JA, Espinosa AG. Fault detection in induction machines using power spectral density in wavelet decomposition. IEEE Transactions on Industrial Electronics. 2008; 55.2: 633–643.
  52. 52. Widodo A, Yang BS. Wavelet support vector machine for induction machine fault diagnosis based on transient current signal. Expert Systems with Applications. 2008; 35.1–2: 307–316.
  53. 53. Sadeghian A, Ye Z, Wu B. Online detection of broken rotor bars in induction motors by wavelet packet decomposition and artificial neural networks. IEEE Transactions on Instrumentation and Measurement. 2009; 58.7: 2253–2263.
  54. 54. Antonino-Daviu J, Rodriguez PJ, Riera-Guasp M, Pineda-Sanchez M, Arkkio A. Detection of combined faults in induction machines with stator parallel branches through the DWT of the startup current. Mechanical Systems and Signal Processing. 2009; 23.7: 2336–2351.
  55. 55. Hedayati Kia S, Henao H, Capolino GA. Diagnosis of broken-bar fault in induction machines using discrete wavelet transform without slip estimation. IEEE Transactions on Industry Applications. 2009; 45.4: 1395–1404.
  56. 56. Rafiee J, Tse PW, Harfi A, Sadeghi MH. A novel technique for selecting mother wavelet function using an intelligent fault diagnosis system. Expert Systems with Applications. 2009; 36: 4862–4875.
  57. 57. Xian GM, Zeng BQ. An intelligent fault diagnosis method based on wavelet packet analysis and hybrid support vector machines. Expert Systems Applications. 2009; 36: 12131–12136.
  58. 58. Mehrjou MR, Mariun N, Hamiruce Marhaban M, Misron N. Evaluation of Fourier and wavelet analysis for efficient recognition of broken rotor bar in squirrel-cage induction machine. In: Proceedings of IEEE International Conference on Power and Energy, Kuala Lumpur, Malaysia, 2010.
  59. 59. Cusido J, Romeral L, Ortega JA, Garcia A, Riba JR. Wavelet and PDD as fault detection techniques. Electric Power Systems Research. 2010; 80.8: N915–N924.
  60. 60. García-Escudero LA, Duque-Perez O, Morinigo-Sotelo D, Perez-Alonso M. Robust condition monitoring for early detection of broken rotor bars in induction motors. Expert Systems with Applications. 2011; 38.3: 2653–2660.
  61. 61. Keskes H, Braham A, Lachiri Z. Broken rotor bar diagnosis in induction machines through stationary wavelet packet transform and multiclass wavelet SVM. Electric Power Systems Research. 2013; 97: 151–157.
  62. 62. Zhang Z, Wang Y, Wang K. Fault diagnosis and prognosis using wavelet packet decomposition, Fourier transform and artificial neural network. Journal of Intelligent Manufacturing.2013; 24:1213-1227.
  63. 63. Jiao X, Ding K, He G. An algorithm for improving the coefficient accuracy of wavelet packet analysis. Measurement. 2014; 47: 207–220.
  64. 64. Yahia K, Cardoso AJM, Ghoggal A, Zouzou SE. Induction motors airgap-eccentricity detection through the discrete wavelet transform of the apparent power signal under non-stationary operating conditions. ISA Transactions. 2014; 53: 603–611.
  65. 65. Ebrahimi BM, Roshtkhari MJ, Faiz J, Khatami SV. Advanced eccentricity fault recognition in permanent magnet synchronous motors using stator current signature analysis. IEEE Transactions on Industrial Electronics.2014; 61.4: 2041–2051.

Written By

Mohammad Rezazadeh Mehrjou, Norman Mariun, Mahdi Karami, Samsul Bahari Mohd. Noor, Sahar Zolfaghari, Norhisam Misron, Mohd Zainal Abidin Ab. Kadir, Mohd. Amran Mohd. Radzi and Mohammad Hamiruce Marhaban

Submitted: November 25th, 2014 Reviewed: September 15th, 2015 Published: December 9th, 2015