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Journal of Engineering
Volume 2016, Article ID 1308108, 7 pages
http://dx.doi.org/10.1155/2016/1308108
Research Article

Rolling Bearing Fault Diagnosis Based on ELCD Permutation Entropy and RVM

1Zhengzhou Railway Vocational & Technical College, No. 9 Qiancheng Road, Zhengdong New District, Zhengzhou, Henan 451460, China
2College of Electronics and Information Engineering, SIAS International University, No. 168 Renmin Road, Xinzheng 451150, China
3Department of Automation & Control, Henan University of Animal Husbandry & Economy, Zhengzhou, Henan 451150, China

Received 9 April 2016; Accepted 26 July 2016

Academic Editor: Sheng-Rui Jian

Copyright © 2016 Jiang Xingmeng et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Aiming at the nonstationary characteristic of a gear fault vibration signal, a recognition method based on permutation entropy of ensemble local characteristic-scale decomposition (ELCD) and relevance vector machine (RVM) is proposed. First, the vibration signal was decomposed by ELCD; then a series of intrinsic scale components (ISCs) were obtained. Second, according to the kurtosis of ISCs, principal ISCs were selected and then the permutation entropy of principal ISCs was calculated and they were combined into a feature vector. Finally, the feature vectors were input in RVM classifier to train and test and identify the type of rolling bearing faults. Experimental results show that this method can effectively diagnose four kinds of working condition, and the effect is better than local characteristic-scale decomposition (LCD) method.