Wavelet Analysis: Mother Wavelet Selection Methods

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.

[1]  M.D. Judd,et al.  Denoising UHF signal for PD detection in transformers based on wavelet technique , 2004, The 17th Annual Meeting of the IEEE Lasers and Electro-Optics Society, 2004. LEOS 2004..

[2]  M.E. El-Hawary,et al.  The most suitable mother wavelet for steady-state power system distorted waveforms , 2008, 2008 Canadian Conference on Electrical and Computer Engineering.

[3]  A. Phinyomark,et al.  Application of Wavelet Analysis in EMG Feature Extraction for Pattern Classification , 2011 .

[4]  J. Rafiee,et al.  Application of mother wavelet functions for automatic gear and bearing fault diagnosis , 2010, Expert Syst. Appl..

[5]  Brij N. Singh,et al.  Optimal selection of wavelet basis function applied to ECG signal denoising , 2006, Digit. Signal Process..

[6]  J. Rafiee,et al.  A novel technique for selecting mother wavelet function using an intelligent fault diagnosis system , 2009, Expert Syst. Appl..

[7]  R. Mardiana,et al.  Wavelet-based compression of power disturbances using the minimum description length criterion , 2001, 2001 Power Engineering Society Summer Meeting. Conference Proceedings (Cat. No.01CH37262).

[8]  J. Rafiee,et al.  Wavelet basis functions in biomedical signal processing , 2011, Expert Syst. Appl..

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

[10]  Tipu Z. Aziz,et al.  Extracting burst and tonic components from surface electromyograms in dystonia using adaptive wavelet shrinkage , 2004, Journal of Neuroscience Methods.

[11]  Wenjie Li Research on Extraction of Partial Discharge Signals Based on Wavelet Analysis , 2009, 2009 International Conference on Electronic Computer Technology.

[12]  Mehrdad Sharif Bakhtiar,et al.  LEAK DETECTION IN WATER-FILLED PLASTIC PIPES THROUGH THE APPLICATION OF TUNED WAVELET TRANSFORMS TO ACOUSTIC EMISSION SIGNALS , 2010 .

[13]  Nirmal K. Bose,et al.  Properties determining choice of mother wavelet , 2005 .

[14]  Naoki Saito,et al.  Simultaneous noise suppression and signal compression using a library of orthonormal bases and the minimum-description-length criterion , 1994, Defense, Security, and Sensing.

[15]  Ruqiang Yan,et al.  Base wavelet selection criteria for non-stationary vibration analysis in bearing health diagnosis , 2007 .

[16]  Lei Zhang,et al.  Multiscale LMMSE-based image denoising with optimal wavelet selection , 2005, IEEE Transactions on Circuits and Systems for Video Technology.

[17]  Martha Flanders,et al.  Choosing a wavelet for single-trial EMG , 2002, Journal of Neuroscience Methods.

[18]  M. Salman Leong,et al.  Improved Blade Fault Diagnosis Using Discrete Blade Passing Energy Packet and Rotor Dynamics Wavelet Analysis , 2010 .

[19]  T.S. Radwan,et al.  Wavelet Packet Transform Based Protection of Three-Phase IPM Motor , 2006, 2006 IEEE International Symposium on Industrial Electronics.

[20]  Ming Zhang,et al.  Automatic wavelet base selection and its application to contrast enhancement , 2010, Signal Process..

[21]  Fulei Chu,et al.  VIBRATION SIGNAL ANALYSIS AND FEATURE EXTRACTION BASED ON REASSIGNED WAVELET SCALOGRAM , 2002 .

[22]  Patrick P C Tsui,et al.  Wavelet basis selection and feature extraction for shift invariant ultrasound foreign body classification. , 2006, Ultrasonics.

[23]  Wenyi Liu,et al.  Wind turbine fault diagnosis based on Morlet wavelet transformation and Wigner-Ville distribution , 2010 .

[24]  Satish C. Sharma,et al.  Fault diagnosis of ball bearings using continuous wavelet transform , 2011, Appl. Soft Comput..

[25]  B. Muralikrishnan,et al.  Engineering Surface Analysis With Different Wavelet Bases , 2003 .

[26]  Peter W. Tse,et al.  Use of autocorrelation of wavelet coefficients for fault diagnosis , 2009 .

[27]  M. Azizur Rahman,et al.  A Novel Neuro-Wavelet Based Self-Tuned Wavelet Controller for IPM Motor Drives , 2008, 2008 IEEE Industry Applications Society Annual Meeting.

[28]  W. Kinsner,et al.  Aquantitative comparison of different mother wavelets for characterizing transients in power systems , 2005, Canadian Conference on Electrical and Computer Engineering, 2005..

[29]  Aleksandra Mojsilovic,et al.  On the Selection of an Optimal Wavelet Basis for Texture Characterization , 1998, ICIP.