基于SVM和EMD 包络谱的滚动轴承故障诊断方法

针对滚动轴承故障振动信号的调制特征和传统包络分析法的缺陷以及现实中难以获得大量典型故障样本的实际情况,提出了一种基于支持向量机(Support Vector Machine,简称SVM)和经验模态分解(Empirical Mode Decomposition,简称EMD)包络谱的滚动轴承故障诊断方法.该方法首先对原始信号进行经验模态分解,将其分解为多个固有模态函数(Intrinsic Mode Function,简称IMF)之和,然后求出包含主要故障信息的若干个IMF分量的包络谱,最后定义包络谱中各种故障特征频率处的幅值比为特征幅值比,将其作为故障特征向量,并以此作为SVM分类器的输入参数来区分滚动轴承的工作状态和故障类型.实验分析结果表明了该方法的有效性.

A Fault Diagnosis Approach for Roller Bearing Based on SVM and EMD Envelope Spectrum

According to the modulation characteristics of roller bearing fault vibration signals, the limitation of the traditional envelope analysis and the situation that it is hard to obtain enough fault samples, a roller bearing fault diagnosis method based on Support Sector Machine(SVM)and Empirical Mode Decomposition (EMD) envelope spectrum is proposed in this paper. Firstly, vibration signals are decomposed into a finite number of intrinsic mode functions (IMFs), then, the ratios of amplitudes in different characteristic frequencies are defined as the characteristic amplitude ratios after the envelope spectra of some IMFs which include the most dominant fault information are obtained; finally, the characteristic amplitude ratios are regarded as the fault features and served as input parameters of SVM classifier to classify working condition and fault patterns of roller bearings. The analysis results demonstrated the effectiveness of the proposed method.