基于改进EMD样本熵和SVM的风机滚动轴承故障诊断

Fault Diagnosis of Rolling Bearing Based on Improved EMD Sample Entropy and SVM

风机齿轮箱振动信号成分复杂,而经验模态分解(EMD)在故障诊断中存在模态混叠和端点效应问题。针对此问题,研究了一种EEMD样本熵和高斯径向基核函数的SVM分类器的滚动轴承故障诊断方法。以风机齿轮箱滚动轴承为研究对象,提取了内圈故障、外圈故障、滚动体故障和正常轴承4种状态振动信号,利用EEMD和小波分别对振动信号分解降噪并筛选主要IMF分量;计算前4阶IMF分量的样本熵作为特征向量;最后将特征向量输入高斯径向基核函数的SVM模型进行故障识别。结果表明:EEMD算法对端点效应和模态混叠都有一定抑制作用,EEMD样本熵和SVM相结合可有效识别滚动轴承故障类型,故障识别率为97.5%,为工程应用中风机齿轮箱滚动轴承故障诊断提供参考。

The components of the vibration signal of the wind turbine(WT) gearbox are complex,and empirical mode decomposition(EMD) has problems of modal aliasing and end effect.Aiming at this problem,a rolling bearing fault diagnosis method based on EEMD sample entropy and Gaussian radial basis kernel function SVM was studied.Taking the rolling bearing of the wind turbine gearbox as the research object,the four state vibration signals of inner ring failure,outer ring failure,rolling element failure and normal bearing were extracted,and the vibration signals were decomposed and denoised respectively by EEMD and wavelet and the main IMF components were screened.The sample entropy of the first four-order IMF components was calculated as the feature vector.Finally,the feature vector was input into the SVM model of the Gaussian radial basis kernel function for fault identification.The results show that the EEMD algorithm has a certain inhibitory effect on the end effect and modal aliasing.The combination of EEMD sample entropy and SVM can effectively identify the type of rolling bearing fault,and the fault recognition rate is 97.5%,which provides a reference for the fault diagnosis of rolling bearing of WT gearbox in engineering application.