Wavelet Analysis: Mother Wavelet Selection Methods

Article Preview

Abstract:

Wavelet analysis, being a popular time-frequency analysis method has been applied in various fields to analyze a wide range of signals covering biological signals, vibration signals, acoustic and ultrasonic signals, to name a few. With the capability to provide both time and frequency domains information, wavelet analysis is mainly for time-frequency analysis of signals, signal compression, signal denoising, singularity analysis and features extraction. The main challenge in using wavelet transform is to select the most optimum mother wavelet for the given tasks, as different mother wavelet applied on to the same signal may produces different results. This paper reviews on the mother wavelet selection methods with particular emphasis on the quantitative approaches. A brief description of the proposed new technique to determine the optimum mother wavelet specifically for machinery faults diagnosis is also presented in this paper.

You might also be interested in these eBooks

Info:

Periodical:

Pages:

953-958

Citation:

Online since:

September 2013

Export:

Price:

[1] Z. Peng, F. Chu, Y. He, Vibration Signal Analysis and Feature Extraction Based on Reassigned Wavelet Scalogram, Journal of Sound and Vibration. 253(5) (2002)1087-1100.

DOI: 10.1006/jsvi.2001.4085

Google Scholar

[2] M.H. Lim, M. Salman Leong, Improved blade fault diagnosis using discrete blade passing energy packet and rotor dynamics wavelet analysis, Proceedings of ASME Turbo Expo 2010: Power for Land, Sea and Air. (2010)31-37.

DOI: 10.1115/gt2010-22218

Google Scholar

[3] S. Fu, B. Muralikrishnan and J. Raja, Engineering surface analysis with different wavelet bases, ASME Journal of Manufacturing Science and Engineering. 125(4)(2003)844-852.

DOI: 10.1115/1.1616947

Google Scholar

[4] A. Mojsilović, M. V. Popović, D. M. Rackov, On the selection of an optimal wavelet basis for texture characterization, IEEE Transactions on Image Processing. 9(12) (2000)2043-(2050).

DOI: 10.1109/83.887972

Google Scholar

[5] L. S. Safavian, W. Kinsner, and H. Turanli, A quantitative comparison of different mother wavelets for characterizing transients in power systems, Canadian Conference on Electrical and Computer Engineering. (2005)1453-1456.

DOI: 10.1109/ccece.2005.1557253

Google Scholar

[6] S. Y. Wang, X. G. Liu, J. Yianni, T. Z. Aziz, and J. F. Stein, Extracting burst and tonic components from surface electromyograms in dystonia using adaptive wavelet shrinkage, Journal of Neuroscience Methods. 139(2)(2004)177-184.

DOI: 10.1016/j.jneumeth.2004.04.024

Google Scholar

[7] N. Ahuja, S. Lertrattanapanich and N. K. Bose, Properties determining choice of mother wavelet. IEE Proceedings Vision, Image and Signal Processing. 152(5)(2005)659 -664.

DOI: 10.1049/ip-vis:20045034

Google Scholar

[8] Martha Flanders, Choosing a wavelet for single-trial EMG, Journal of Neuroscience Methods. 116 (2002)165-177.

DOI: 10.1016/s0165-0270(02)00038-9

Google Scholar

[9] M. F. Faizal and A. Mohamed, Comparing the performance of various mother wavelet functions in detecting actual 3-phase voltage sags, 2nd IEEE International Conference on Power and Energy. (2008).

DOI: 10.1109/pecon.2008.4762557

Google Scholar

[10] Majid Ahadi and Mehrad Sharif Bakhtiar, Leak detection in water-filled plastic pipes through the application of tuned wavelet transforms to Acoustic Emission signals, Applied Acoustics. 71 (2010)634-639.

DOI: 10.1016/j.apacoust.2010.02.006

Google Scholar

[11] Baoping Tang, Wenyi Liu, Tao Song, Wind turbine fault diagnosis based on Morlet wavelet transformation and Wigner-Ville distribution, Renewable Energy. 35 (2010)2862-2866.

DOI: 10.1016/j.renene.2010.05.012

Google Scholar

[12] N. Salto, Simultaneous noise suppression and signal compression using a library of orthonormal bases and the minimum description length criterion, In: (2nd ed. ), E. Foufoula-Georgiou and P. Kumar, Editors, Wavelets in Geophysics, Academic Press. (2004).

DOI: 10.1016/b978-0-08-052087-2.50017-7

Google Scholar

[13] E. Y. Hamid, R. Mardiana, Z. I. Kawasaki, Wavelet-based compression of power disturbances using the minimum description length criterion, IEEE Power Engineering Society Summer Meeting. 3(2001)1772-1777.

DOI: 10.1109/pess.2001.970344

Google Scholar

[14] M. A. S. K. Khan , T. S. Radwan, M. A. Rahman, Wavelet Packet Transform Based Protection of Three-Phase IPM Motor, 2006 IEEE International Symposium on Industrial Electronics. (2006)2122 - 2127.

DOI: 10.1109/isie.2006.295901

Google Scholar

[15] M. A. S. K. Khan , M. A. Rahman, A novel neuro-wavelet-based self-tuned wavelet controller for IPM motor drives, IEEE Transactions on Industry Applications. 46(3)(2010)1194-1203.

DOI: 10.1109/tia.2010.2045213

Google Scholar

[16] L. Yang, M. D. Judd and C. J. Bennoch, Denoising UHF signal for PD detection in transformers based on wavelet technique, 2004 Annual Report Conference on Electrical Insulation and Dielectric Phenomena. (2004)166 - 169.

DOI: 10.1109/ceidp.2004.1364215

Google Scholar

[17] Wenjie Li, Research on extraction of partial discharge signals based on wavelet analysis, International Conference on Electronic Computer Technology. (2009).

DOI: 10.1109/icect.2009.57

Google Scholar

[18] Brij N. Singh, Arvind K. Tiwari, Optimal selection of wavelet basis function applied to ECG signal denoising, Digital Signal Processing. 16 (2006)275-287.

DOI: 10.1016/j.dsp.2005.12.003

Google Scholar

[19] Lei Zhang, Paul Bao, and Xiaolin Wu, Multiscale LMMSE-Based Image Denoising With Optimal Wavelet Selection, IEEE Transactions on Circuits and Systems for Video Technology. 1(4)(2005)469 - 481.

DOI: 10.1109/tcsvt.2005.844456

Google Scholar

[20] Patrick P. C. Tsui, Otman A. Basir, Wavelet basis selection and feature extraction for shift invariant ultrasound foreign body classification, Ultrasonics. 45 (2006)1-14.

DOI: 10.1016/j.ultras.2006.05.214

Google Scholar

[21] R. Yan, Base wavelet selection criteria for non-stationary vibration analysis in bearing health diagnosis, Electronic Doctoral Dissertations for UMass Amherst, Paper AAI3275786, http: /scholarworks. umass. edu/dissertations/AAI3275786, January 1, (2007).

Google Scholar

[22] P. K. Kankar, Satish C. Sharma, S. P. Harsha, Fault diagnosis of ball bearings using continuous wavelet transform, Applied Soft Computing. 11(2011)2300-2312.

DOI: 10.1016/j.asoc.2010.08.011

Google Scholar

[23] J. Rafiee and P. W. Tse, Use of autocorrelation of wavelet coefficients for fault diagnosis, Mechanical System and Signal Processing. 23(5)(2009)1554-1572.

DOI: 10.1016/j.ymssp.2009.02.008

Google Scholar

[24] J. Rafiee, M.A. Rafiee and P. W. Tse, Application of mother wavelet functions for automatic gear and bearing fault diagnosis, Expert System with Applications. 37 (2010)4568-4579.

DOI: 10.1016/j.eswa.2009.12.051

Google Scholar

[25] J. Rafiee, P. W. Tse, A. Harifi, M. H. Sadeghi, A novel technique for selecting mother wavelet function using an intelligent fault diagnosis system, Expert Systems with Applications. 36 (2009)4862-4875.

DOI: 10.1016/j.eswa.2008.05.052

Google Scholar

[26] W. G. Morsi and M. E. El-Hawary, The most suitable Wavelet for steady-state power system distorted waveforms, Canadian Conference on Electrical and Computer Engineering. (2008)17-22.

DOI: 10.1109/ccece.2008.4564487

Google Scholar

[27] H. D. Cheng, RuiMin, MingZhang, Automatic wavelet base selection and its application to contrast enhancement, Signal Processing. 90(2010)1279-1289.

DOI: 10.1016/j.sigpro.2009.10.013

Google Scholar

[28] A. Phinyomark, C. Limsakul, and P. Phukpattaranont, Evaluation of Mother Wavelet Based on Robust EMG Feature Extraction Using Wavelet Packet Transform. Proceeding of 13th International Annual Symposium on Computational Science and Engineering. (2009).

DOI: 10.5755/j01.eee.122.6.1816

Google Scholar

[29] J. Rafiee, M. A. Rafiee, N. Prause, M. P. Schoen, Wavelet basis functions in biomedical signal processing, Expert Systems with Applications. 38(2011)6190-6201.

DOI: 10.1016/j.eswa.2010.11.050

Google Scholar