基于ESMD和SVM的滚动轴承故障诊断

Roller Bearing Fault Diagnosis Based on ESMD and SVM

针对轴承诊断典型样本较少的问题,提出一种基于极点对称模态分解(ESMD)和支持向量机(SVM)的滚动轴承故障诊断方法。其对信号进行ESMD分解,提取含主要故障信息的IMF能量值进行归一化处理,构成能量特征向量并建立SVM,即可准确判断轴承的工作状态。工程实例分析表明,该方法诊断准确率较高(100%),能够有效应用于轴承的故障诊断。

Aiming at the problem of few typical samples for bearing diagnosis, a fault diagnosis method of rolling bearing based on pole symmetric modal decomposition （ESMD） and support vector machine （SVM） is proposed in this paper. The ESMD decomposition of the signal, extract the IMF information containing the main fault information normalized to form the energy feature vector and the establishment of SVM, you can accurately determine the working status of the bearing. The engineering case analysis shows that this method has high diagnostic accuracy （100%） and can be effectively applied to bearing fault diagnosis.