Performance of wavelet denoising in vibration analysis: highlighting

This paper proposes to highlight two aspects of denoising in vibration analysis. The first aspect aims to reveal the singularities, and the second eliminates the noise in order to keep the useful signal. These two aspects are the cause of the surjection of denoising, especially due to the choice of the performance criteria. This paper highlights the use of denoising through these aspects, and then proposes a performance criterion suitable for vibration analysis as part of a noise suppression, to apply a processing method. This paper provides a reflection on the use of discrete wavelet transform through the various parameters which are used during processing.

[1]  Robert M. Parkin,et al.  On the energy leakage of discrete wavelet transform , 2009 .

[2]  David Ebenezer,et al.  A New Fast and Efficient Decision-Based Algorithm for Removal of High-Density Impulse Noises , 2007, IEEE Signal Processing Letters.

[3]  Paresh Girdhar.  Practical Machinery Vibration Analysis and Predictive Maintenance , 2004 .

[4]  Petre Stoica,et al.  Introduction to spectral analysis , 1997 .

[5]  S. M. Wu,et al.  On-Line Detection of Localized Defects in Bearings by Pattern Recognition Analysis , 1989 .

[6]  Abdel-Ouahab Boudraa,et al.  Speech Enhancement via EMD , 2008, EURASIP J. Adv. Signal Process..

[7]  A. Boudraa,et al.  EMD-Based Signal Noise Reduction , 2005 .

[8]  K. L. Doty.  Digital Spectral Analysis of Audio Signals , 1965 .

[9]  Radu Ranta,et al.  Débruitage par ondelettes et segmentation de signaux non-stationnaires : réinterprétation d'un algorithme itératif et application à la phonoentérographie Wavelet denoising and segmentation for non-stationary signals : a reinterpretation of an iterative algorithm and application to phonoenterography , 2003 .

[10]  Aouni A. Lakis,et al.  A new method for shock detection and time-domain classification , 2007 .

[11]  Ranta,et al.  2 - Débruitage par ondelettes et segmentation de signaux non-stationnaires : réinterprétation d'un algorithme itératif et application à la phonoentérographie , 2003 .

[12]  Yong Ching Lim,et al.  On the identification of systems from data measurements using ARMA lattice models , 1986, IEEE Trans. Acoust. Speech Signal Process..

[13]  Ma Carmen Carnero,et al.  Statistical quality control through overall vibration analysis , 2010 .

[14]  Pierre Dehombreux,et al.  Effect of cascade methods on vibration defects detection , 2011 .

[15]  T. I. El-Wardany,et al.  Tool condition monitoring in drilling using vibration signature analysis , 1996 .

[16]  P. Welch.  The use of fast Fourier transform for the estimation of power spectra: A method based on time averaging over short, modified periodograms , 1967 .

[17]  David L. Donoho,et al.  De-noising by soft-thresholding , 1995, IEEE Trans. Inf. Theory.

[18]  Norbert Wiener,et al.  Extrapolation, Interpolation, and Smoothing of Stationary Time Series , 1964 .

[19]  Steven D. Glaser,et al.  Wavelet denoising techniques with applications to experimental geophysical data , 2009, Signal Process..

[20]  S. Mallat.  A wavelet tour of signal processing , 1998 .

[21]  Yvan Simard,et al.  Automatic detection of bioacoustics impulses based on kurtosis under weak signal to noise ratio , 2010 .

[22]  Xavier Chiementin,et al.  A comparative experimental study on the use of three denoising methods for bearing defect detection , 2010 .

[23]  Tang Li-wei,et al.  Detection of Gear Fault Based on Amplitude Demodulation of Rotary Speed , 2010, 2010 First International Conference on Pervasive Computing, Signal Processing and Applications.

[24]  A. Swindlehurst,et al.  Subspace-based signal analysis using singular value decomposition , 1993, Proc. IEEE.

[25]  Giovanni Ramponi,et al.  Edge detection using generalized higher-order statistics , 1993, [1993 Proceedings] IEEE Signal Processing Workshop on Higher-Order Statistics.

[26]  Govindappa Krishnappa,et al.  Bearing Diagnostics Based on Pattern Recognition of Statistical Parameters , 2000 .

[27]  Hossein Nezamabadi-pour,et al.  Image denoising in the wavelet domain using a new adaptive thresholding function , 2009, Neurocomputing.

[28]  P. Massart,et al.  From Model Selection to Adaptive Estimation , 1997 .

[29]  J. Antoni.  The spectral kurtosis: a useful tool for characterising non-stationary signals , 2006 .