Rolling bearing fault diagnosis based on deep boltzmann machines

Shengcai Deng, Zhiwei Cheng, Chuan Li, Xingyan Yao, Zhiqiang Chen, René Vinicio Sanchez

Research output: Contribution to conferencePaper

10 Scopus citations

Abstract

© 2016 IEEE. Rolling bearing is one of the most commonly used components in rotating machinery. It is easy to be damaged which can cause mechanical fault. Thus, it is significance to study fault diagnosis technology on rolling bearing. This paper presents a Deep Boltzmann Machines (DBM) model to identify the fault condition of rolling bearing. A data set with seven fault patterns is collected to evaluate the performance of DBM for rolling bearing fault diagnosis, which is based on the health condition of a rotating mechanical system. The features of time domain, frequency domain and time-frequency domain are extracted as input parameters for the DBM model. The results showed that the accuracy presented by the DBM model is highly reliable and applicable in fault diagnosis of rolling bearing.
Original languageEnglish
DOIs
StatePublished - 16 Jan 2017
EventProceedings of 2016 Prognostics and System Health Management Conference, PHM-Chengdu 2016 -
Duration: 16 Jan 2017 → …

Conference

ConferenceProceedings of 2016 Prognostics and System Health Management Conference, PHM-Chengdu 2016
Period16/01/17 → …

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