基于改进HHT和SVM的滚动轴承故障状态识别

针对滚动轴承故障信号特征难以提取与故障诊断效率较低问题,引入集合经验模态分解(EEMD)对Hilbert-Huang变换(HHT)进行改进,将改进的HHT结合拉普拉斯得分(Laplacian score,LS)进行轴承故障特征提取,并利用遗传算法(GA)优化支持向量机(SVM)分类参数,将其应用于滚动轴承振动信号故障状...

Fault Detection of Rolling Bearings Based on Improved HHT and SVM

In order to solve the difficulty to extract the vibration signal characteristics and the problems of low efficiency of fault diagnosis for rolling bearings,the ensemble empirical mode decomposition(EEMD)was introduced to improve the Hilbert-Huang transform(HHT).The improved HHT was then combined with the Laplacian score(LS)for bearing fault feature extraction,and the genetic algorithm(GA)was applied to optimize the parameters of support vector machine(SVM),and for the fault state recognition of rolling bearing vibration signal.Firstly,the correlation coefficient was used to filter the IMF components after EEMD decomposition,and the Hilbert marginal spectral energy and Lempel-Ziv complexity of the IMF components were calculated to form the high-dimensional feature vector.Secondly,the LS was utilized to reduce the dimension of the high-dimensional feature vector.Finally,the GA-SVM was applied to identify different fault conditions of the bearings.The test data of different bearing states verified the proposed method.The results show that the proposed method can effectively identify different fault states of the bearings.