The design of a new sparsogram for fast bearing fault diagnosis: Part 1 of the two related manuscripts that have a joint title as “Two automatic vibration-based fault diagnostic methods using the novel sparsity measurement – Parts 1 and 2”

Rolling element bearings are widely used in rotating machines. An early warning of bearing faults helps to prevent machinery breakdown and economic loss. Vibration-based envelope analysis has been proven to be one of the most effective methods for bearing fault diagnosis. The core of an envelope analysis is to find a resonant frequency band for a band-pass filtering for the enhancement of weak bearing fault signals. A new concept called a sparsogram is proposed in Part 1 paper. The aim of the sparsogram is to quickly determine the resonant frequency bands. The sparsogram is constructed using the sparsity measurements of the power spectra from the envelopes of wavelet packet coefficients at different wavelet packet decomposition depths. The optimal wavelet packet node can be selected by visually inspecting the largest sparsity value of the wavelet packet coefficients obtained from all wavelet packet nodes. Then, the wavelet packet coefficients extracted from the selected wavelet packet node is demodulated for envelope analysis. Several case studies including a simulated bearing fault signal mixed with heavy noise and real bearing fault signals collected from a rotary motor were used to validate the sparsogram. The results show that the sparsogram effectively locates the resonant frequency bands, where the bearing fault signature has been magnified in these bands. Several comparison studies with three popular wavelet packet decomposition based methods were conducted to show the superior capability of sparsogram in bearing fault diagnosis.

[1]  K. Loparo,et al.  Bearing fault diagnosis based on wavelet transform and fuzzy inference , 2004 .

[2]  P. Tse,et al.  An improved Hilbert–Huang transform and its application in vibration signal analysis , 2005 .

[3]  Hong-Zhong Huang,et al.  Rolling element bearing fault detection using an improved combination of Hilbert and wavelet transforms , 2009, Journal of Mechanical Science and Technology.

[4]  I. S. Bozchalooi,et al.  A joint resonance frequency estimation and in-band noise reduction method for enhancing the detectability of bearing fault signals , 2008 .

[5]  이화영.  X , 1960, Chinese Plants Names Index 2000-2009.

[6]  Tomasz Barszcz,et al.  A novel method for the optimal band selection for vibration signal demodulation and comparison with the Kurtogram , 2011 .

[7]  Robert B. Randall,et al.  The spectral kurtosis: application to the vibratory surveillance and diagnostics of rotating machines , 2006 .

[8]  P. Tse,et al.  Machine fault diagnosis through an effective exact wavelet analysis , 2004 .

[9]  Yaguo Lei,et al.  Application of an improved kurtogram method for fault diagnosis of rolling element bearings , 2011 .

[10]  Que Pei-wen,et al.  Sparsity enhancement for blind deconvolution of ultrasonic signals in nondestructive testing application. , 2008, The Review of scientific instruments.

[11]  Liang Chen,et al.  Signal extraction using ensemble empirical mode decomposition and sparsity in pipeline magnetic flux leakage nondestructive evaluation. , 2009, The Review of scientific instruments.

[12]  Kihong Shin,et al.  Fundamentals of Signal Processing for Sound and Vibration Engineers , 2008 .

[13]  Qiang Miao,et al.  Identification of characteristic components in frequency domain from signal singularities. , 2010, The Review of scientific instruments.

[14]  Peter W. Tse,et al.  Wavelet Analysis and Envelope Detection For Rolling Element Bearing Fault Diagnosis—Their Effectiveness and Flexibilities , 2001 .

[15]  Robert B. Randall,et al.  THE RELATIONSHIP BETWEEN SPECTRAL CORRELATION AND ENVELOPE ANALYSIS IN THE DIAGNOSTICS OF BEARING FAULTS AND OTHER CYCLOSTATIONARY MACHINE SIGNALS , 2001 .

[16]  I. S. Bozchalooi,et al.  A smoothness index-guided approach to wavelet parameter selection in signal de-noising and fault detection , 2007 .

[17]  Wensheng Su,et al.  Rolling element bearing faults diagnosis based on optimal Morlet wavelet filter and autocorrelation enhancement , 2010 .

[18]  Ioannis Antoniadis,et al.  Rolling element bearing fault diagnosis using wavelet packets , 2002 .

[19]  Yuh-Tay Sheen,et al.  On the study of applying Morlet wavelet to the Hilbert transform for the envelope detection of bearing vibrations , 2009 .

[20]  Robert B. Randall,et al.  Rolling element bearing diagnostics—A tutorial , 2011 .

[21]  Robert X. Gao,et al.  Wavelet transform with spectral post-processing for enhanced feature extraction [machine condition monitoring] , 2003, IEEE Trans. Instrum. Meas..

[22]  J. Antoni.  Fast computation of the kurtogram for the detection of transient faults , 2007 .

[23]  Robert X. Gao,et al.  Wavelet transform with spectral post-processing for enhanced feature extraction , 2002, IMTC/2002. Proceedings of the 19th IEEE Instrumentation and Measurement Technology Conference (IEEE Cat. No.00CH37276).

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

[25]  Dong Wang,et al.  Robust health evaluation of gearbox subject to tooth failure with wavelet decomposition , 2009 .

[26]  B Liu.  Selection of wavelet packet basis for rotating machinery fault diagnosis , 2005 .

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

[28]  Ruqiang Yan,et al.  Wavelets: Theory and Applications for Manufacturing , 2010 .

[29]  Liang Wei,et al.  Sparse deconvolution method for improving the time-resolution of ultrasonic NDE signals , 2009 .

[30]  Tet Hin Yeap,et al.  A joint wavelet lifting and independent component analysis approach to fault detection of rolling element bearings , 2007 .