Rolling Bearing Clustering Faults Diagnosis Based on EEMD,Permutation Entropy and PCA-GK

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Rolling Bearing Clustering Faults Diagnosis Based on EEMD,Permutation Entropy and PCA-GK

Journal Title: 河南科技大学学报（自然科学版） - Year 2017, Vol 38, Issue 2

Abstract

Aiming at the problem that the dimension of entropy eigenvectors decomposed by ensemble empirical mode decomposition( EEMD) of rolling bearing vibration signals was high,and the computational efficiency of sample entropy( SE) method was poor,a combination method based on EEMD,permutation entropy( PE),principal component analysis( PCA) and GK( Gustafson-Kessel) clustering algorithm was proposed for rolling bearings fault diagnosis. Firstly,the original signals were decomposed into a series intrinsic mode functions( IMFs) by using EEMD methods,then the PE and the SE methods were used to calculate the corresponding entropy values. Secondly,the dimension reduction and visualization of SE and PE eigenvectors which were regarded as the input of fuzzy C-mean( FCM) and GK for the rolling bearing fault recognition were achieved by PCA model. The partition coeffcient( PC) and classification entropy( CE) were used to verify and compare clustering method. The experiment results show that the method( EEMD-PE-PCA-GK) proposed in this paper is better than the other three models( EEMD-SE-PCA-FCM,EEMD-SE-PCA-GK,EEMD-PE-PCA-FCM),and the computational efficiency of PE is faster than SE method.

Authors and Affiliations

Youpeng HUANG, Shan ZHAO, Fan XU, Yanjun FANG

Keywords

rolling bearing; fault diagnosis; ensemble empirical mode decomposition; permutation entropy; Gustafson-Kessel clustering algorithm;

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EP ID EP477879
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