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Induction Machine Bearing Fault Diagnosis With Discrete Wavelet Transform Using Vibration Signal

Ashwani Kumar Chandel*, Raj Kumar Patel**

* Associate Professor, Department of Electrical and Electronics Engineering, National Institute of Technology, Hamirpur, HP India.

** M.Tech Student, Department of Electrical and Electronics Engineering, National Institute of Technology, Hamirpur (H.P.).

Periodicity:April - June'2012
DOI : https://doi.org/10.26634/jee.5.4.1858

Abstract

Condition monitoring and fault diagnosis of electrical machinery are of great concern in industries. Pre fault detection in machinery can save millions of rupee required as emergency maintenance cost. In the present paper a methodology for the diagnosis of bearing faults in an induction machine has been given. The proposed discrete wavelet transform methodology extracts principal features from vibration signals caused by the faulty bearings and subsequently analyse these for determining the type of fault. The discrete wavelet transform is fed with the accelerometer signals and it is found that the impulses appear periodically with time period corresponding to defect frequencies. The results thus obtained reveal that the proposed pre-faults detection method is very efficient and effective.

Keywords

Bearing, condition, faults, induction machine, monitoring, wavelet transform

How to Cite this Article?

Ashwani Kumar Chandel and Raj Kumar Patel (2012). Induction Machine Bearing Fault Diagnosis With Discrete Wavelet Transform Using Vibration Signal. i-manager’s Journal on Electrical Engineering, 5(4), 11-17. https://doi.org/10.26634/jee.5.4.1858

References

[1]. Singh, G.K., Kazzaz, S.A., & Ahmed, S. (2004). Vibration signal analysis using wavelet transform for isolation and identification of electrical faults in induction motor. Electric Power Systems Research, 68 (3), 119-136.

[2]. IAS Motor Reliability Working Group (1985). Report of Large Motor Reliability Survey of Industrial and commercial installations, part-1. IEEE Transactions on Industry Applications, 21(4), 853-864.

[3]. IAS Motor Reliability Working Group (1985). Report of large motor reliability survey of industrial and commercial installation, part 2. IEEE Transactions on Industry Applications, 21(4), 865-872.

[4]. Purushotham, V., Narayanan, S., & Prasad, S. A. N. (2005). Multi-fault diagnosis of rolling bearing elements using wavelet analysis and hidden Markov model based fault recognition. NDT & E International, 38 (8), 654-664.

[5]. Tandon, N., & Choudhury. A. (2000). A review of vibration and acoustic measurement methods for the detection of defects in rolling element bearings. Tribology International, 32 (1999), 469-480.

[6]. Braun, S., & Datner, B. (1977). Analysis of roller/ball bearing vibrations. ASME paper, 77 (5), 1-8.

[7]. MacFadden, P. D. & Smith, J. D. (1984). Vibration monitoring of rolling element bearing by the high frequency resonance technique, a review. Tribol Int 17(1), 3–10

[8]. Chaturvedi, G. K. & Thomas, D. W. (1981). Bearing fault detection using adaptive Noise Cancelling. ASME paper, 81(7), 1-10.

[9]. Staszewski, W. J. (1998). Structural and mechanical damage detection using wavelrts. The Shock and Vibration Digest, 6, 457-72.

[10]. Mertins (1999). Signal analysis: wavelets, filter banks, time frequency transforms and applications. John Wiley & Sons Ltd.

[11]. Mallat, S. G. (2000). A theory for multi resolution signal decomposition: the wavelet representation. IEEE Transl. on pattern analysis and machine intelligence, 11( 7), 674-693.

[12]. Prabhakar, S., Mohanty, A. R., & Sekhar, A. S. (2002). Application of discrete wavelet transforms for detection of ball bearings race faults. Tribology International, 35, 793–800.

[13]. Seker, S. & Ayaz, E. (2003). Feature extraction related to bearing damage in electric motors by wavelet analysis. Journal of the Franklin Institute, 340 (2), 125-134.

[14]. Wowk, V. (1991). Machinery Vibration, Measurement and Analysis, McGraw-Hill, New York.

[15]. Choudhury, A. & Tandon, N. (2000). Application of acoustic emission technique for the detection of defects in rolling element bearings. Test, 33, 39-45.

[16]. Gaeid, K. S. & Ping, H. W. (2011). Wavelet fault diagnosis and tolerant of induction motor: A review. International Journal, 6 (3), 358-376.

[17]. Seker, S., Upadhyaya, B. R., Erbay, A. S., McClanhan, J.P., & DaSilva, A.A. (1998). Rotating machinery monitoring and degradation trending using wavelet transform. Proc. MARCON, 23(1), 01-11.

[18]. Wang, D., Miao, Q., Fan, X., Huang, H.Z. (2010). Rolling element bearing fault detection using an improved combination of Hilbert and wavelet transforms. Journal of Mechanical Science and Technology, 23 (12), 3292-3301

[19]. Al-raheem, K. F., & Abdul-karem, W. (2010). Rolling bearing fault diagnostics using artificial neural networks based on Laplace wavelet analysis. Science And Technology, 2(6), 278-290.

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