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Advance in Electronic and Electric Engineering. ISSN 2231-1297, Volume 3, Number 8 (2013), pp. 1019-1030 © Research India Publications http://www.ripublication.com/aeee.htm

Tradeoff Analysis of Wavelet Transform Techniques for the Detection of Broken Rotor Bars in Induction Motors

S.M. Shashidhara1, Member, IEEE and Dr. P. Sangameswara Raju2

1Research Scholar, Dept of of EEE, SVU College of Engineering, Tirupati, India.

2Professor, Dept of EEE, SVU College of Engineering, Tirupati.

Abstract

This paper presents the fault detection of broken rotor bars using different mother wavelets and the tradeoff analysis of these wavelets is performed. The stator phase current was used as input to the fault detecting module based on wavelet analysis. DWT coefficients of the stator current in a particular frequency band are calculated and analysed. Daubechies db8, db9, db10 and Symlet sym7 and sym8 wavelets are applied to analyse stator current disturbed due to broken bars . The sensitivities of these wavelets to fault signals do vary and they are compared and evaluated to choose the most optimal one. This facilitates the diagnosis of broken rotor bar and also indicates the number of broken bars. This paper presents the results and demonstrates the effectiveness of the proposed approach for fault diagnosis. Index Terms: Induction motor, fault detection, tradeoff analysis, broken rotor bar, wavelet, DWT, Stator current.

1. Introduction Fault diagnosis on Induction motor is a major concern in the in the electric drives industry as 80% of the electric drives industry works on Induction motors. The fault diagnosis based on the signal processing methods is very widely used as it is a noninvasive method. While using signal processing based fault diagnosis we need to reduce the time taken for the diagnosis. The sample-by-sample comparison and analysis would be time taking and the computational complexity would be high. That is why feature based methods were used like the Fast Fourier Transform (FFT), Short

S.M. Shashidhara et al

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Time Fourier Transform (STFT) and recently Wavelet Transforms (WT). Wavelet transform takes its seat in this application because of its feature extracting capability from the signals. Motor current Signature Analysis (MCSA) is the method that would be appropriate to deal with because it is sensorless and is the most effective way of analysing the faults in the induction motors.

A comparison of stator current, vibration, and acoustic methods in detecting broken bar and bearing faults were presented by [1] The MCSA method using Wavelet Transform was used in [2] for broken rotor bar method in an induction motor. The Sensorless Induction motor control technique used the Wavelet Based fault diagnosis was presented in [3]. Empirical model decomposition was used for detecting the nonlinear system of the broken rotor fault was detected by the Wavelet Transform[4].A fault linearizing method called stepping finite element was introduced ,which would be got by filtering ,differentiating and transforming the fault signal using the Daubecheis wavelet for 5 times was presented in [5]. This paper takes up the tradeoff analysis among different mother wavelets for fault detection of rotor bars. In spite of satisfactory performance of DWT, it has some drawbacks. Selection of optimal mother wavelet is somewhat arbitrary, not a known priori which may introduce error in the detection parameters. Besides, the overlap between bands associated with wavelet signals appearing mainly for lower order wavelet. In this paper, the proposed approach is focused on the study of different wavelet performance by analysis the coefficients derived from the DWT [11] [12]. A best representative wavelet is selected for rotor fault detection by comprehensive analysis these coefficients. This permits a good interpretation of the phenomenon due to the variation of these signals reflects distinctly the development of the harmonics associated with broken rotor bars during the transient. Moreover, the use of the wavelet signals (approximation and high order details) resulting from the DWT constitutes an advantage because these signals act as filters, according to Mallat algorithm, allowing the automatic extraction of the time evolution of the low frequency components that are present in the signal during the transient [13].

2. Wavelet Transforms To extract data from signals and bring out the dynamics that agrees to the signals, a right signal processing technique is to be chosen. Generally, the process of signal processing translates a time domain signal into another domain as the diagnostic information embedded within the time domain is not readily evident in its original form. Mathematically, this can be accomplished by mapping the time domain signal as a series of coefficients, based on a equivalence between the signal x(t) and template functions {Ψ*n(t)}.

∞∞ (1)

Tradeoff Analysis of Wavelet Transform Techniques for the Detection of Broken 1021

The inner product between x(t) and Ψ*n(t) is

, (2)

2.1 Representative Signals There are many commonly used wavelets for performing the DWT. Haar is orthogonal and symmetric. The property of symmetry ascertains that the Haar wavelet bears linear phase characteristics, meaning that when a filtering is performed on a signal with this mother wavelet, there will be no phase deformation in the filtered signal. Moreover, it is the simplest base wavelet with the highest time resolution.

However, the rectangular contour of the Haar wavelet makes its corresponding spectrum with slow decay, heading to a low frequency resolution. Another is Daubechies, is orthogonal and asymmetrical, which brings in a large phase distortion. This means that it cannot be employed in applications where a phase data needs to be kept. It is also a compact support mother wavelet with a given support width of 2N -1, in which N is the order of the base wavelet.

One of the major advantages of wavelet transform for signal analysis is the abundance of the mother wavelets. From such abundance comes up a question of how to pick out a base wavelet that is most appropriate for analysing a specific signal. Since the choice in the first place may bear on the result of the wavelet transform at the end, the question is logical. For example, as shown in Figure 1.

Therefore, in the following section, a general strategy for base wavelet selection is presented. Then, several quantitative measures that can be used as guidelines for wavelet selection are given. While Morlet wavelet is efficient in pulling out the impulsive component, the Mexican-hat and Daubechies wavelets did not amply reveal the characteristics of impulsive component

. Figure 1


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2.2 Selection Criteria There are two means to assess the performance of wavelet , one is qualitative and the other is quantitative. Base wavelets are defined by orthogonality, symmetry, and compact support. Understanding these attributes will help pick out a candidate mother wavelet from the wavelet families for examining a specific signal. For example, the orthogonality dimension indicates that the inner product of the base wavelet is unity with itself, while it is zero with other scaled and shifted wavelets. As an outcome, an orthogonal wavelet is efficient for signal decomposition into non-overlapping sub-frequency bands. The symmetric property ascertains that a base wavelet can serve as a linear phase filter. A compact support wavelet is one whose basis function is non-zero only within a finite interval. This allows the wavelet transform to efficiently map signals that have localized features.

a. Db Wavelet

b. Sym Wavelet

Fig. 2: Demonstration of wavelet family


In this paper, the approach for the diagnosis of broken rotor bars is discussed and applied to industrial induction machines. Several experiments are conducted for different operating conditions and fault cases such as one-bar breakage, two-bar breakages and also a variation of load. For the purposes of testing, the bar breakages were forced in the workshop in motors.

3. Experimental Setup In this part, an on-line experimental rig is developed in order to test and verify the performance of the diagnosing system. The on-line current monitoring system is shown in Figure1. The experimentation is conducted under the self-designed test rig which is mainly composed a set of three phase induction machines, DC generate, current transducer, A/D converter, and computer. Firstly, transient stator current signals are collected from tested motors and signal preprocessing is conducted which contains smoothing and subtraction. Moreover, Matlab & Wavelet toolbox is used to decompose the acquired time domain signal into time-frequency domain. Then, fault features waveband is extracted from all intended wavelet transform level. Finally, the individual diagnosis results are applied to validate the developed model.

The tested motors are three identical three-phase, 2-pole, 36 stator slots and 28 rotor slots induction motors. The specifications of the proposed induction motors used in our experiment are 5.5KW, 3000 rpm, 20.6A, 50Hz, 2 poles, 36 stator slots, 28 rotor slots. The tests are carried out on a healthy motor and a motor with drilled bars. The rotor bar breakages were broken deliberately by drilling holes in the workshop.

Stator currents of the motor are sampled by a Hall Effect sensing element which is positioned in one of the phase line current wires. The stator current is sampled at 1.92 KHz rate and interfaced to a PC by a data acquisition system. The quantities have been measured for healthy and three broken rotor bars at varied load.

The motor load is operated through the generator shaft speed. A DC generator is coupled to the motor as the load. The excitation current of the generator has been adjusted in order to regulate the output voltage. A resistance box is connected to the terminals of the generator. The resistance of this box can be selected step by step by a selector switch on the box.

Sampling frequencies of 1920 samples/s which enable good resolution analyses, Table I shows the frequency levels of the wavelet function coefficients.

Table 1: Frequency levels of wavelet.

Wavelet Analysis

Frequency Components (Hz)

A5 0-30 D5 30-60


1024

D4 60-120 D3 120-240 D2 240-480 A1 480-960

4. NDICES for Diagnosis of Fault As already mentioned, Rotor broken bars render side band components around the fundamental frequency. Moreover, referring to Table I indicates that the wavelet coefficients in D5 consist of side band components around the fundamental frequency. Therefore, in this paper, a different wavelet (db8, db9, db10, sym7, sym8) coefficient in D5 has been used to diagnose the fault and also the number of the broken bars. By comparing these coefficients in D5, the best wavelet was selected for diagnosis of rotor fault and the number of broken rotor bars.

Fig. 3: Wavelets coefficients for healthy motor.

Fig. 4: Wavelets coefficients for one broken rotor bar.


Fig 5: Wavelets coefficients for two broken rotor bars.

Fig. 6: Wavelets coefficients for three broken rotor bars.

Fig. 7: Different Wavelet coefficients in D5 for two broken rotor

bar motor under experimental results.

Fig. 8: Criterion function 1.


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Fig. 9: Criterion function 2.

Table 2: Criterion function for different wavelets under healthy condition.

Wavele Mea STD Mean Index1 Index2 ts n fluctuatio ns Db8 4.393 2.113 0.0769 0.4809167 0.0175015 Db9 4.399 2.115 0.0782 0.4808627 0.0177735 Db10 4.421 2.116 0.0782 0.4785932 0.0176863 Sym7 4.432 2.117 0.0783 0.4777557 0.0176645 Sym8 4.462 2.118 0.0789 0.4747243 0.0176794

Table 3: Criterion function for different wavelets under one broken bar.


Table 4: Criterion Function For Different Wavelets under Two Broken Bars.

Wavele Mea STD Mean Index1 Index2 ts n fluctuatio ns


Db8 3.995 2.167 0.1235 0.5425854 0.0309097 Db9 3.985 2.168 0.1296 0.5441087 0.0325170 Db10 3.897 2.168 0.1311 0.5564849 0.0336369 Sym7 3.992 2.169 0.1357 0.5434984 0.0339921 Sym8 3.988 2.171 0.1388 0.5443377 0.0347983

Table 5: Criterion Function For Different Wavelets under Three Broken Bars.


By the numerical analysis of mean, STD and variations, the change tendencies can

be evolved when broken rotor bars occur in induction motor. However, these tendencies are not easy for operator to judge due to the values are too small to separate them. To resolve this problem, according to the above cited facts the following indices are proposed for the diagnosis of the broken bar:

Criterion function= Criterion function=

Table 6: Criterion Function for Different Wavelets for three Broken Bars in Experimental Results.



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Wherever the average of the absolute value of the wavelet coefficients in D5 is expressed in per-unit with reference to the average amplitude of the currents. The value of index 1 increases by occurrence of the breaking in the bars of the rotor and has rising trend as shown in Figure 9. According to Table II-VI, the value of index 1 for a healthy motor is 47-48% and for the motor with one broken rotor bar is 50-51%. An evident difference between these values makes the proposed index enable to diagnose faulty motor from healthy motor. On the other hand, comparison of this index for one and two broken bars indicates that this is a convenient index for the diagnosis of the number of broken bars.

Figure 7 shows the experimental results for 3 rotor broken bars which are in good correspondence with the computer simulation results. Although by introducing these two indexes, boundaries between healthy and faulty and also the numbers of broken bars is clearly depicted in Figure 8, 9. To increase the accuracy of diagnosis, the most suitable wavelet must be selected. However, there is no definite rule to lead how to pick out the right wavelet until now. Most of the inquiries are based on trial and error approach. In this paper, Daubechies db8, db9, db10 and Symlet sym7 and sym8 wavelets were used to diagnose broken rotor bars. According to Figure 8, 9, wavelet sym8 has the most apparent gap to detect 1, 2 and 3 broken rotor bar faults.

5. Conclusion In this paper, the proposed fault detecting algorithm is implemented on the stator current of a healthy and faulty induction motor. Two novel criterion functions are presented to select the optimal mother wavelet to diagnose the broken rotor bars fault and also the number of broken bars in induction motors. Both simulations and experiments prove that increase of the load and broken bars is followed by a growth in amplitude of harmonic components of a faulty induction machine. References

[1] LI W and Mechefske C 2006 Detection of Induction Motor Faults: A

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[3] Radhika, S, et al., “Precise wavelet for current signature in 3phase 1M,” Expert Systems with applications, vol. 37, no. 1, pp. 450-455, 2010.

[4] Riera-Guasp, M, et al., “A General Approach for the Transient Detection of Slip -Dependent Fault Components Based on the Discrete Wavelet Transform,” IEEE Transactions on Industrial Electronics, vol. 55, no. 12, pp. 4167-4180, 2008.


[5] Siau, J, et al., “Broken Bar Detection in Induction Motors using , New Zealand, 2003.

[6] [Benbouzid, M.E.H, “A Review of Induction Motor Signature Analysis as a Medium for Faults Detection,” IEEE Transactions on Industrial Electronics, vol. 47, no. 5, pp. 984-993, 2000.

[7] Da Silva, AA and Upadhyaya, B R, “Rotating machinery monitoring and diagnosis using short-time Fourier transform and wavelet techniques,” Proc. 1997 Int. Conf. Maintenance and Reliability, 1997.

[8] Douglas, H and Pillay, P, “The impact of wavelet selection on transient motor current signature analysis,” IEEE International Conference on Electric Machines and Drives, San Antonio, Texas, USA, pp. 80-85, 2005.

[9] Ye, Z, Wu, B and Sadeghian, A, “Current signature analysis of induction motor mechanical faults by wavelet packet decomposition,” IEEE Trans on Industrial Electronics, vol. 50, no. 6, pp. 1217-1227, 2003.

[10] Daviu, J Antonino, et al., “Validation of a new method for the diagnosis of rotor bar failures via wavelet transformation in industrial induction machines,” IEEE Trans. Ind. Appl. vol. 42, no. 4, pp. 990-996, 2006.

[11] Arashloo, R S et al, “The impact of wavelet selection on transient on Electric Machines and Drives, pp. 80 – 85.

[12] Sadri, H, “Induction Motor Broken Rotor Bar Fault Detection Using Wavelet Analysis,” Master's Thesis, Iran University of Science & Technology, 2004.

[13] Ponci, F, et al., “Diagnostic of a Faulty Induction Motor Drive via Wavelet Decomposition,” IEEE Transactions on Instrumentation and Measurement, vol. 56, no. 6, pp. 2606-2615, 2007.

[14] Burrus, C S, Gopinath, R A and Guo, H, “Introduction to Wavelets and Wavelet Transforms. A Primer,” Englewood Cliffs, NJ: Prentice-Hall, 1998.

[15] Zhang, J W, et al., “A Fault Diagnosis Approach for Broken Rotor Bars Based on EMD and Envelope Analysis,” Journal of China University Mining & Technology, vol. 17, no. 2, pp. 205-209, 2007.

[16] S. M. Shashidhara et al "stator winding fault diagnosis of three-phase induction motor by park’s vector approach" in International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering IJAREEIE) Vol. 2, Issue 7, July 2013 ISSN (Print) : 2320 – 3765 ISSN (Online): 2278 – 8875.

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.


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Dr. P. Sangameswara Raju, received Ph.D from Sri Venkateswara Univerisity, Tirupati, Andhra Pradesh. He is working as Professor and Head of the Department 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.

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