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Indexed by:期刊论文

Date of Publication:2013-11-01

Journal:Journal of Information and Computational Science

Included Journals:EI、Scopus

Volume:10

Issue:16

Page Number:5189-5198

ISSN No.:15487741

Abstract:In this paper, a new fault feature extraction method based on Intrinsic Mode Function (IMF) envelope sample entropy (SampEn) is proposed for rolling bearings fault diagnosis. First, the Empirical Mode Decomposition (EMD) method is utilized to decompose the vibration signals self-adaptively into a number of IMFs which represent different frequency bands from high to low. Second, the IMF envelope signals are used to highlight the fault-induced information in a structurally simpler and physically more meaningful way than the original signals. Thus, the shortcoming of SampEn assigning high values to uncorrelated random signals can be overcome. Finally, the IMF envelope SampEn serve as a fault feature vector to be input into multi-class classifier of Support Vector Machine (SVM) for identification of different bearing conditions. The experimental results indicate that the proposed approach based on IMF envelope SampEn can identify different fault types as well as levels of severity effectively and is superior to that based on IMF SampEn. ? 2013 Binary Information Press.