基于小波包分解和支持向量机的机械故障诊断方法被引量：25

METHOD OF MACHINERY FAULT DIAGNOSIS BASED ON WAVELET PACKET DECOMPOSITION AND SUPPORT VECTOR MACHINE

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摘要提出应用小波包分解和支持向量机进行机械故障诊断的方法。该方法将振动信号小波包分解后的频带能量作为特征向量 ,输入到由多个支持向量机构成的多故障分类器中进行故障识别和分类。试验结果表明 ,与神经网络相比 ,采用支持向量机进行故障诊断可以获得更高的诊断精度 ,表明该方法是有效的、可行的。 Since there are some problems in machinery fault diagnosis, such as difficulty in getting adequate fault data samples, extracting feature vectors and acquiring fault diagnosis knowledge, a novel method of machinery fault diagnosis based on wavelet packet decomposition and support vector machine is proposed. According to the method, the energy of different frequency bands after wavelet packet decomposition constitutes the input vectors of support vector machine as feature vectors. And these feature vectors are inputted into multiple fault classifiers to identify faults. The new method, by which multiple faults can be diagnosed, only requires a small quantity of fault data samples and doesn't know the empirical knowledge of fault diagnosis. Compared with artificial neural networks(ANN),the method can achieve excellent diagnosis accuracy, and the results prove the method is efficient and feasible.

作者何学文卜英勇

机构地区中南大学机电工程学院

出处《机械强度》 CAS CSCD 北大核心 2004年第1期20-24,共5页 Journal of Mechanical Strength

关键词小波包分解能量谱支持向量机故障诊断多故障分类器机械故 Wavelet packet decomposition Energy spectrum Support vector machine Fault diagnosis Multiple fault classifiers

分类号 TH17 [机械工程—机械制造及自动化]

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1HE ZhengJia,CI YanYang,MENG QingFeng,et al. Fault diagnosis principles of non-stationary signal and applications to mechanical equipment. Beijing:Higher Education Press,2001.95-105(h Chinese)(何正嘉,訾艳阳,孟庆丰,等.机械设备非平稳信号的故障诊断原理及应用.北京:高等教育出版社,2001.95-105).
2Vapnik V. The nature of statistical learning theory. New York:Spring-Verlag,1995.
3Christopher J C Burges. A Tutorial on support vector machine for pattern recognition. Data Mining and Knowledge Discovery,1998,2:121 ～ 167.
4Steve R Gunn. Support vector machines for classification and regression.Southampton: Department of Electronics and Computer Science of University of Southampton,1998. 1 ～ 28.
5HU ChangHua,ZHANG JunBo,XIA Jun,et al. System analysis and design based on Matlab-Wavelet analysis. Xi' an: Xidian University Press,2000.1-25,264-271(h Chinese)(胡昌华,张军波,夏军,等.基于MATLAB的系统分析与设计--小波分析.西安:西安电子科技大学出版社,2000.1-25,264-271).
6.
7.
8.
9Bennett K P,Blue J A. A support vector machine approach to decision trees. Department of Mathematical Sciences Math Report No. 97-100,Troy,New York: Rensselaer Polytechnic Institute,1997. http://www.math. rpi. edu/～ bennek.
10Weston J,Watkins C. Multi-class support vector machines. Technical Report CSD-TR-98-04,London: Dept. of Computer Science,University of London,1998. 1 ～ 10.