Deep neural networks-based rolling bearing fault diagnosis

Zhiqiang Chen, Shengcai Deng, Xudong Chen, Chuan Li, René Vinicio Sanchez, Huafeng Qin

Research output: Contribution to journalArticlepeer-review

127 Scopus citations

Abstract

© 2017 Elsevier Ltd Rolling bearing is one of the most commonly used components in rotating machinery. It's so easy to be damaged that it can cause mechanical fault. Thus, it is significant to study fault diagnosis technology on rolling bearing. In this paper, three deep neural network models (Deep Boltzmann Machines, Deep Belief Networks and Stacked Auto-Encoders) are employed to identify the fault condition of rolling bearing. Four preprocessing schemes including feature of time domain, frequency domain and time-frequency domain are discussed. One data set with seven fault patterns is collected to evaluate the performance of deep learning models for rolling bearing fault diagnosis, which is based on the health condition of a rotating mechanical system. The results proved that the accuracy achieved by Deep Boltzmann Machines, Deep Belief Networks and Stacked Auto-Encoders are highly reliable and applicable in fault diagnosis of rolling bearing.
Original languageEnglish
Pages (from-to)327-333
Number of pages7
JournalMicroelectronics Reliability
DOIs
StatePublished - 1 Aug 2017

Fingerprint

Dive into the research topics of 'Deep neural networks-based rolling bearing fault diagnosis'. Together they form a unique fingerprint.

Cite this