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Shock and Vibration
Volume 2017, Article ID 2623081, 9 pages
https://doi.org/10.1155/2017/2623081
Research Article

The Fault Diagnosis of Rolling Bearing Based on Ensemble Empirical Mode Decomposition and Random Forest

1School of Basic Sciences, Changchun University of Technology, Changchun 130012, China
2Graduate School, Changchun University of Technology, Changchun 130012, China

Correspondence should be addressed to Xiaogang Dong; nc.ude.tucc@gnagoaixgnod

Received 9 May 2017; Accepted 2 July 2017; Published 20 August 2017

Academic Editor: Simone Cinquemani

Copyright © 2017 Xiwen Qin 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

Accurate diagnosis of rolling bearing fault on the normal operation of machinery and equipment has a very important significance. A method combining Ensemble Empirical Mode Decomposition (EEMD) and Random Forest (RF) is proposed. Firstly, the original signal is decomposed into several intrinsic mode functions (IMFs) by EEMD, and the effective IMFs are selected. Then their energy entropy is calculated as the feature. Finally, the classification is performed by RF. In addition, the wavelet method is also used in the proposed process, the same as EEMD. The results of the comparison show that the EEMD method is more accurate than the wavelet method.