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Rolling bearing fault diagnosis based on local wave method
and KPCA-LSSVM |
| YANG Xian-yong1,2, ZHOU Xiao-jun1, ZHANG Wen-bin1, YANG Fu-chun1 |
1. Zhejiang Provincial Key Laboratory of Advanced Manufacturing Technology, Zhejiang University,
Hangzhou 310027, China; 2. China Ship Development and Design Center, Wuhan 430064, China |
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Abstract Aimed at the nonstationary characteristics of rolling bearing vibration signal, a fault diagnosis method was proposed based on localwave method and KPCA(kernel principal component analysis) LSSVM(least squares support vector machine). Firstly, local wave decomposition was used to decompose rolling bearing vibration signal into several intrinsic mode function (IMF), whose feature energy, singular values and AR model parameters were computed as initial feature vectors. Secondly, ini tial feature vectors were mapped into a higherdimensional space with KPCA, and the kemel principal components were extracted as new feature vectors, which used as the input of LSSVM for fault classification. The experimental results show the KPCALSSVM method improves LSSVMs classification performance by KPCA obtaining additional discriminative information, and has better generalization than direct LSSVM method, and can identify rolling bearing fault patterns more accurately．
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Published: 21 September 2010
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基于局域波法和KPCA-LSSVM的滚动轴承故障诊断
针对故障滚动轴承振动信号具有非平稳性,提出基于局域波法和核主元分析最小二乘支持向量机(KPCA LSSVM )的故障诊断方法.先对轴承振动信号进行局域波分解得到若干内禀模式函数(IMF),分别计算各IMF分量的特征能量、奇异值和AR模型参数作为原始特征向量,再用KPCA将原始特征向量映射到高维特征空间提取主元构造新的特征向量,将其作为LSSVM分类器的输入来实现轴承的故障诊断.故障诊断试验结果表明,KPCALSSVM诊断方法通过KPCA得到更多的识别信息,改善了LSSVM的分类性能,相对于直接LSSVM诊断方法具有更优的泛化性,可准确识别轴承的故障类别和严重程度.
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