Rolling bearing fault feature extraction based on
morphological wavelet and S-transform
YANG Xian-yong1,2, ZHOU Xiao-jun1, ZHANG Wen-bin1, YANG Fu-chun1, LIN Yong1
1. Zhejiang Provincial Key Lab oratory of Advanced Manufacturing Technology, Zhejiang University,
Hangzhou 310027, China;2. China ship Development and Design Center, Wuhan 430064, China
Aimed at the deficiency of traditional wavelet extracting impulse fault features from strong noise background, a fault feature extraction method for rolling bearing was proposed based on max-lifting morphological wavelet(MLMW) and S-transform. Firstly, decomposed to different levels by MLMW, signal’s local maxima were mapped to scale signals and preserved over several scales, and the detail signals were denoised by soft threshold. Secondly, the signal was reconstructed, and fault features were extracted from the denoised signal‘s S-transform spectrum with excellent time-frequency focus characteristic. The experimental results show MLMW analysis not only reduces noise and harmonic components, but also significantly enhances fault features, and can extract nonlinear and non-stationary fault features more clearly than classical wavelet transform and envelopment analysis. Furthermore, with simple morphological operators and efficient lifting scheme adopted, the MLMW algorithm is simple and the cost is low, so it is suitable for on-line fault features analysis．
YANG Xian-yong, ZHOU Xiao-jun, ZHANG Wen-bin, YANG Fu-chun, LIN Yong. Rolling bearing fault feature extraction based on
morphological wavelet and S-transform. J4, 2010, 44(11): 2088-2092.
［1］ LI Zhen, HE Zhengjia, ZI Yanyang, et al. Customized wave let denoising using intra and interscale dependency for bear ing fault detection ［J］. Journal of Sound and Vibration, 2008, 313(1): 342-359．
［2］ 孙晖, 朱善安. 基于自适应滤波的滚动轴承故障诊断研究［J］. 浙江大学学报：工学版,2005, 39(11):1746-1749．
SUN Hui, ZHU Shanan. Rolling bearing fault diagnosis based on adaptive filtering ［J］. Journal of Zhejiang University ：Engineering Science, 2005, 39(11): 1746-1749．
［3］ ZHANG Lijun, XU Jinwu, YANG Debin, et al. Fault fea ture extraction to equipments using morphological wavelet denoising ［C］∥ Proceedings of ICMT 2006. Beijing: Science Press, 2006(2): 1320-1324．
［4］ 段晨东, 何正嘉, 姜洪开. 非线性小波变换在故障特征提取中的应用［J］. 振动工程学报, 2005, 18(1): 129-132．
DUAN Chendong, HE Zhengjia, JIANG Hongkai. Fault feature extract ion using nonlinear wavelet transform ［J］. Journal of Vibration Engineering, 2005, 18(1): 129-132．
［5］ HEIJMANS H J A M, GOUTSIAS J. Nonlinear multiresolu tion signal decomposition schemes—Part II: Morphological wavelets ［J］. IEEE Trans Image Processing, 2000, 9(11): 1897-1913.
［6］ SWELDENS W. The lifting scheme: a construction of second generation wavelets ［J］. SIAM Journal on Mathematical Analysis, 1997, 29(2): 511-546．
［7］ HAO Rujiang, CHU Fulei. Morphological undecimated wavelet decomposition for fault diagnostics of rolling ele ment bearings ［J］. Journal of Sound and Vibration, 2009, 320(4): 1164-1177．
［8］ STOCKWELL R G. A basis for efficient representation of the Stransform ［J］. Digital Signal Processing, 2007, 17(1): 371-393．
［9］ PINNEGAR C R, MANSINHA L. The Stransform with windows of arbitrary and varying shape ［J］. Geophys, 2003, 68(1): 381-385．
［10］ DONOHO D L, JOHNSTONE I M. Adapting to unknown smoothness via wavelet shrinkage ［J］.Jounal of the Amer ican Statistical Assoc, 1995, 90(432):1200-1224.
［11］ Case Western Reserve University. Bearing data center. ［EB/OL］.(2005-02-01) ［2008-10-06］. http://www.eecs.cwru.edu/laboratory/bearing．
