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浙江大学学报(工学版)
J4  2010, Vol. 44 Issue (3): 432-439    DOI: 10.3785/j.issn.1008973X.2010.03.004
    
Rolling bearing fault diagnosis based on morphological
wavelet theory and bispectrum analysis
 LIN Yong, ZHOU Xiao-Jun, ZHANG Wen-Bin, YANG Xian-Yong
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Abstract  

The nonlinear waveletmorphological wavelet is introduced into the field of vibration signal processing to explore methods that can process effectively both Gauss noise and nonGauss noise based on the routine of bispectrum analysis. Morphological wavelet packet was developed and its analysis and synthesis steps were described, to perfect the morphological wavelet theory. On the basis of excellent denoising effects of morphological wavelet packet, three kinds of cascaded algorithms were put forward based on the morphological wavelet packet, Hilbert envelope analysis and bispectrum analysis, that is morphological wavelet packetbispectrum, morphological wavelet packetHilbertbispectrum and Hilbertmorphological wavelet packet bispectrum.These three algorithms showed good inhibition for Gauss noise and nonGauss noise. Hilbertmorphological wavelet packet bispectrum algorithm especially unfolds best adaptability. Results in rolling bearing signal processing show that Hilbertmorphological wavelet packetbispectrum algorithm can tell bearing fault of different components and different fault degree from each other clearly.

Published: 20 March 2012
CLC:  TP277  
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Cite this article:

LIN Yong, ZHOU Xiao-Jun, ZHANG Wen-Bin, YANG Xian-Yong. Rolling bearing fault diagnosis based on morphological
wavelet theory and bispectrum analysis. J4, 2010, 44(3): 432-439.

URL:

http://www.zjujournals.com/eng/10.3785/j.issn.1008973X.2010.03.004     OR     http://www.zjujournals.com/eng/Y2010/V44/I3/432


基于形态小波理论和双谱分析的滚动轴承故障诊断

为了探索基于双谱估计流程的对包含高斯和非高斯成分的故障信号进行有效处理的方法,将非线性小波形态小波引入到振动信号处理中来,并进一步完善形态小波理论,提出形态小波包分解与重构算法.通过形态小波包软阈值降噪实现非高斯信号高斯化处理,在此基础上,发展了3种形态小波包双谱分析级联算法：形态小波包双谱估计、形态小波包Hilbert双谱估计、Hilbert形态小波包双谱估计,该方法对故障信号的高斯和非高斯成分都能有效抑止,尤其是Hilbert形态小波包双谱估计算法,适应性强,处理效果更好.经过滚动轴承故障诊断试验,结果表明,基于形态小波包和双谱分析的算法,能够准确区分不同类型、不同程度的故障序列,并取得了良好的效果.

［1］ 李军伟,韩捷,李志农,等.小波变换域双谱分析及其在滚动轴承故障诊断中的应用［J］.振动与冲击,2006,25(5):9295
LI Junwei, HAN Jie, LI Zhinong, et al. Bispectrum analysis in the wavelet transform domain and its application to the fault diagnosis of rolling bearings［J］. Journal of Vibration and Shock, 2006,25(5): 9295
［2］ 杨江天,陈家骥,曾子平.基于高阶谱的旋转机械故障征兆提取［J］.振动工程学报,2001,3(1): 1318.
YANG Jiangtian,CHEN Jiaji, ZENG Zipin. Extracting fault features using higher order spectra for rotating machinery［J］. Journal of Vibration Engineering,2001,3(1): 1318.
［3］ 朱忠奎、孔凡让、王建平,等.循环双谱及其在齿轮箱故障识别中的应用研究［J］.振动工程学报,2004,17(2): 224 225.
ZHU Zhongkui,KONG Fanrang, WANG Jianping, et al. Study on the applications of cyclic bispectrum in gearbox fault diagnosis［J］. Journal of Vibration Engineering, 2001, 3(1): 1318.
［4］ HEIJMANS H J A M, GOUTSIAS J. Nonlinear multiresolution signal decomposition schemes. Part 2: morphological wavelets［J］. IEEE Transactions on Image Processing, 2000, 9(11): 18971913.
［5］ 唐常青.数学形态学方法及其应用［M］.北京：科学出版社,1990.
［6］ 张贤达.时间序列分析高阶统计量方法［M］.北京：清华大学出版社,1996.
［7］ 邱天爽,张旭秀,李小兵,等.统计信号处理非高斯信号处理及其应用［M］.北京:电子工业出版社,2004.
［8］ Bearing Data Center. Case Western Reserve University ［EB/OL］. ［20080120］. http://www.eecs.cwru.edu/ laboratory/ bearing.
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