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Journal of Control Science and Engineering
Volume 2017, Article ID 3583610, 14 pages
https://doi.org/10.1155/2017/3583610
Research Article

A Novel Multimode Fault Classification Method Based on Deep Learning

1School of Computer and Information Engineering, Henan University, Kaifeng, China
2School of Automation, Hangzhou Dianzi University, Hangzhou, China

Correspondence should be addressed to Yulin Gao; moc.361@nhniluyoag and Chenglin Wen; nc.ude.udh@lcnew

Received 23 December 2016; Accepted 22 February 2017; Published 20 March 2017

Academic Editor: Youqing Wang

Copyright © 2017 Funa Zhou 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

Due to the problem of load varying or environment changing, machinery equipment often operates in multimode. The data feature involved in the observation often varies with mode changing. Mode partition is a fundamental step before fault classification. This paper proposes a multimode classification method based on deep learning by constructing a hierarchical DNN model with the first hierarchy specially devised for the purpose of mode partition. In the second hierarchy , different DNN classification models are constructed for each mode to get more accurate fault classification result. For the purpose of providing helpful information for predictive maintenance, an additional DNN is constructed in the third hierarchy to further classify a certain fault in a given mode into several classes with different fault severity. The application to multimode fault classification of rolling bearing fault shows the effectiveness of the proposed method.