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基于Fisher比率与SVM的滚动轴承故障诊断方法

Keywords: 小波包分解,支持向量机,特征向量,Fisher比率,故障识别

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Abstract:

针对滚动轴承故障诊断中普遍存在的小样本学习问题,采用支持向量机实现轴承故障的模式识别.为了解决时域统计参数对于轴承故障的多分类效果较差的问题,引入小波包分解(waveletpacketdecomposition,WPD)技术,提取振动信号各频带的能量系数构造特征向量,并采用Fisher比率法对特征向量进行优化选取;然后利用支持向量机(supportvectormachine,SVM)进行故障模式识别,并与小波包分解及时域统计参数的分类效果进行对比分析.结果表明:支持向量机是实现轴承故障模式识别的一种有效手段;本方法的分类效果及时间效率明显优于传统的多维时域指标和小波能量系数分类方法;将Fisher比率法与SVM相结合可以提高轴承故障诊断的准确率.

References

[1]  王凯,张永祥,李军.基于支持向量机的齿轮故障诊断方法研究[J].振动与冲击,2006,25(6):97-99.WANG Kai,ZHANG Yong-xiang,LI Jun.Study on diagnosis method of gear fault based on support vector machine[J].Journal of Vibration and Shock,2006,25(6):97-99.(in Chinese)
[2]  黄文虎,夏松波,刘瑞岩,等.设备故障诊断原理、技术及应用[M].北京:科学出版社,1996:1-20.
[3]  JACK L B,NANDI A K,MCCORMICK A C.Diagnosis of rolling element bearing faults using radial basis function networks[J].Applied Signal Processing,1999,6:25-32.
[4]  JACK L B,NANDI A K.Support vector machines for detection and characterization of rolling element bearing faults[J].Journal of Mechanical Engineering Science,2001,215(9):1065-1074.
[5]  JACK L B,NANDI A K.Fault detection using support vector machines and artificial neural networks,augmented by geneticalgorithms[J].Mechanical Systems and Signal Processing,2002,16(2-3):373-390.
[6]  THUKARAM D,KHINCHA H P,VIJAYNARASIMHA H P.Artificial neural network and support vector machine approachfor locating faults in radial distribution systems[J].IEEE Transactions on Power Delivery,2005,20(2):710-721.
[7]  程军圣,于德介,杨宇.基于EMD和SVM的滚动轴承故障诊断方法[J].航空动力学报,2006,21(3):575-580.CHENG Jun-sheng,YU De-jie,YANG Yu.Fault diagnosis of roller bearings based on EMD and SVM[J].Journal ofAerospace Power,2006,21(3):575-580.(in Chinese)
[8]  王丽,周新立,尉询楷.基于支持向量机的故障诊断方法及其应用[J].火力与指挥控制,2006,31(4):9-11.WANG Li,ZHOU Xin-li,WEI Xun-kai.Fault diagnosis method based on support vector machines and its application[J].Fire Control and Command Control,2006,31(4):9-11.(in Chinese)

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