基于小波包和SVM的轴承故障识别方法研究
首发时间:2009-11-30
摘要:本文针对轴承故障识别问题,提出一种应用小波包与支持向量机(SVM)相结合的的故障识别方法,对故障轴承不同工作状态下的振动信号进行提取特征向量,并以此作为训练样本对多分类SVM进行训练。研究结果表明该方法融合了小波包和支持向量机的优点,可有效的进行设备故障状态识别,达到了精确进行机械系统故障诊断的目的。
关键词: 小波包 支持向量机(SVM) 轴承 多分类
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Method Of Beaing Fault Indentification Based On Wavelet Package and SVM
Abstract:For the sake of solving the problem of bearing working state on identification , a method of bearing intelligent fault identification is proposed by means of the wavelet package-support vector machine. According to the method , the energy of frequency bands after wavelet packet decomposition of the vibration signals in different working states is taken as the eigenvectors and also as training samples of SVM multi-fault classifier. The result indicated that the method fused benefits of wavelet packet and support vector machine ,so can conduct identification of equipment fault effectively and achieve the purpose of precise fault diagnosis for mechanical system.
Keywords: wavelet package support vector machine(SVM) bearing multi-fault classifier
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No.3711050170212595****
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