基于有理双树复小波和SVM的滚动轴承故诊断方法  

Fault Diagnosis Method of Rolling Bearing Based on Dual-tree Rational-dilation Complex Wavelet Packet Transform and SVM

作　　者：孙珊珊[1] 何光辉[1] 崔建[1] 

机构地区：[1]重庆大学数学与统计学院,重庆401331

出　　处：《计算机科学》2015年第B11期131-134,共4页Computer Science

基　　金：国家自然科学基金项目(61173030)资助

摘　　要：滚动轴承故障类型被支持向量机(SVM)智能识别的关键是故障特征的提取。为了提取最优的故障特征,提高SVM的分类识别精度,提出了基于有理双树复小波和SVM的滚动轴承故障诊断方法。首先通过双树复小波包变换将非平稳的振动信号分解得到不同频带的分量,然后对每个分量求能量并作归一化处理,最后将从各个频带分量中提取的能量特征参数作为支持向量机的输入来识别滚动轴承的故障类型。研究结果表明该方法可以有效、准确地识别轴承的故障模式。In order to improve the recognition accuracy of SVM classification, a fault diagnosis method was proposed based on dual-tree rational-dilation complex wavelet transform and support vector machine （SVM）, according to the characteristics of roiling bearing fault vibration signal. Firstly, the fault signal is decomposed into several different frequency band components through dual-tree rational-dilation complex wavelet transform. Secondly, normalization pro- cessing is made from the energy of each component. Finally, the energy characteristics parameters of each frequency band component are taken as input of the SVM to identify the fault type of rolling bearing. The experimental results prove that the proposed method can identify the fault type accurately and effectively.

关 键 词：有理双树复小波变换 特征提取 支持向量机 滚动轴承 故障分类 

分 类 号：TH133.3[机械工程—机械制造及自动化] TH165