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浙江大学学报(工学版)
J4  2010, Vol. 44 Issue (11): 2088-2092    DOI: 10.3785/j.issn.1008973X.2010.11.008
机械工程     
基于形态小波和S变换的滚动轴承故障特征提取
杨先勇1,2,周晓军1,张文斌1,杨富春1,林勇1
1.浙江大学 浙江省先进制造技术重点实验室,浙江 杭州 310027；2.中国舰船研究设计中心,湖北 武汉 430064
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
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摘要：

针对传统小波在强背景噪声中提取冲击故障特征的不足,提出基于极大提升形态小波(MLMW)分析和S变换的滚动轴承故障特征提取方法.先利用MLMW变换将信号分解到不同形态尺度上,各尺度信号上保留着信号局部极值形态特征,对细节信号进行软阈值降噪处理,再从重构信号的具有良好时频聚焦性的S变换谱上提取故障特征.试验结果表明,MLMW既抑制了噪声和谐波分量,又显著强化了故障特征；相比传统小波和包络分析,能清晰地提取非平稳非线性故障特征.由于MLMW采用简单的形态算子和高效的提升方法,计算简单高效,适于故障特征的在线分析.
Abstract:

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．
出版日期: 2010-12-23
:  TH 165.3  
通讯作者: 周晓军，男，教授，博导.     E-mail: cmeesky@163.com
作者简介: 杨先勇(1980-)，男，湖北孝感人，博士生，从事车辆检测、振动噪声与信号处理、设备故障诊断研究. E-mail：ycwwyt@yahoo.com.cn
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引用本文:

杨先勇,周晓军,张文斌,杨富春,林勇. 基于形态小波和S变换的滚动轴承故障特征提取[J]. J4, 2010, 44(11): 2088-2092.

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.

链接本文:

http://www.zjujournals.com/eng/CN/10.3785/j.issn.1008973X.2010.11.008    或    http://www.zjujournals.com/eng/CN/Y2010/V44/I11/2088

［1］ LI Zhen, HE Zhengjia, ZI Yanyang, et al. Customized wave let denoising using intra and interscale 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 Shanan. Rolling bearing fault diagnosis based on adaptive filtering ［J］. Journal of Zhejiang University ：Engineering Science, 2005, 39(11): 1746-1749．
［3］ ZHANG Lijun, XU Jinwu, YANG Debin, 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 Chendong, HE Zhengjia, JIANG Hongkai. 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 Rujiang, CHU Fulei. 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 Stransform ［J］. Digital Signal Processing, 2007, 17(1): 371-393．
［9］ PINNEGAR C R, MANSINHA L. The Stransform 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．
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