ROLLING BEARING FAULT DIAGNOSIS BASED ON LMD AND ICA

Abstract

For the problem of Local Mean Decomposition（ LMD） was easily affected by noise interference when in the extraction of fault features,a rolling bearing fault diagnosis method which based on LMD and Independent Component Analysis（ ICA） was proposed. Firstly,original signal was decomposed into a series of production functions（ PF） by the LMD method.Secondly,the estimate of PF was obtained after the PF components had been separated by ICA method,and the noise was effectively removed. Then,mutual information,correlation coefficient and approximate entropy which were extracted from the estimate of PF components were grouped together as a feature vector. Finally,the fault feature vectors were classified by SVM.The results of the feature extraction and fault diagnosis experiments show that the fault recognition rate of LMD-ICA method is significantly better than the traditional LMD method.

Keywords

• Rolling bearing;LMD;ICA;SVM