48. MED and WPT based technique for bearings fault detection
Zhang Dan1, Sui Wentao2
1School of Electrical and Electronic Engineering, Shandong University of Technology, Zibo 255049, China
2School of Mechanical Engineering, Shandong University of Technology, Zibo 255049, China
E-mail: email@example.com, firstname.lastname@example.org
(Received 20 January 2014; received in revised form 18 March 2014; accepted 22 March 2014)
Abstract. A new technique is proposed in this work for fault detection in rolling element bearings, which is based on minimum entropy deconvolution (MED), wavelet packet decomposition (WPT) and envelop analysis. Firstly, the collected vibration signal is preprocessed to highlight defect‑related impulses, and a new indicator named envelope spectra sparsity (ESS) is proposed to automatically select the filter length of MED. Then the preprocessed signal is decomposed into WPT nodes, and the most sensitive node containing fault-related information are selected from all the nodes to improve the accuracy of the fault detection. Sparsity of wavelet packet nodes signal (SWPN) is proposed in this step as a measure indicator. Lastly the power spectrum is used to highlight the bearing fault characteristic frequencies. The effectiveness of the proposed AMED‑WPT technique in feature extraction and analysis is verified by a series of experimental tests corresponding to different bearing conditions.
Keywords: minimum entropy deconvolution, wavelet packet decomposition, envelope analysis, bearing fault.
 Wang W., Lee H. An energy kurtosis demodulation technique for signal denoising and bearing fault detection. Measurement Sciences and Technology, Vol. 24, Issue 2, 2013, p. 025601.
 Keheng Zhu, Xigeng Song, Dongxin Xue Incipient fault diagnosis of roller bearings using empirical mode decomposition and correlation coefficient. Journal of Vibroengineering, Vol. 15, Issue 2, 2013, p. 597‑603.
 Peter W. Tse, Dong Wang 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. Mechanical Systems and Signal Processing, Vol. 40, Issue 2, 2013, p. 499‑519.
 Antoni J. Fast computation of the kurtogram for the detection of transient faults. Mechanical Systems and Signal Processing, Vol. 21, Issue 1, 2007, p. 108‑124.
 Tomasz Barszcz, Adam JabŁoński A novel method for the optimal band selection for vibration signal demodulation and comparison with the Kurtogram. Mechanical Systems and Signal Processing, Vol. 25, Issue 2, 2011, p. 431‑451.
 Peter W. Tse, Dong Wang The automatic selection of an optimal wavelet filter and its enhancement by the newsparsogram for bearing fault detection. Mechanical Systems and Signal Processing, Vol. 40, Issue 2, 2013, p. 520‑544.
 Huang N. E., Shen Z., Long S. R., et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of the Royal Society A, Vol. 454, 1998, p. 903‑995.
 Wiggins R. A. Minimum entropy deconvolution. Geoexploration, Vol. 16, 1978, p. 21‑35.
 Endo H., Randall R. B. Application of a minimum entropy deconvolution filter to enhance Autoregressive model based gear tooth fault detection technique. Mechanical Systems and Signal Processing, Vol. 21, Issue 2, 2007, p. 906‑919.
 Liang W., Lei H. M, Que P. W. Sparsity enhancement for blind deconvolution of ultrasonic signals in nondestructive testing application. Review of Scientific Instruments, Vol. 79, Issue 1, 2008, p. 014901‑014906.
 Changqing Shen, Dong Wang, Fanrang Kong, Peter W. Tse Fault diagnosis of rotating machinery based on the statistical parameters of wavelet packet paving and a generic support vector regressive classifier. Measurement, Vol. 46, Issue 4, 2013, p. 1551‑1564.
 Wen-Chang Tsao, Yi-Fan Li, Duc Du Le, Min-Chun Pan An insight concept to select appropriate IMFs for envelope analysis of bearing fault diagnosis. Measurement, Vol. 45, Issue 6, 2012, p. 1489‑1498.
 Nikolaou N. G., Antoniadis I. A. Rolling element bearing fault diagnosis using wavelet packets. Independent Nondestructive Testing and Evaluation, Vol. 35, Issue 3, 2002, p. 197‑205.
Cite this article
Dan Zhang, Wentao Sui MED and WPT based technique for bearings fault detection. Journal of Measurements in Engineering, Vol. 2, Issue 2, 2014, p. 103‑110.
Journal of Measurements in
Engineering. June 2014, Volume 2, Issue 2