Fault Diagnosis of Rolling Bearing using Deep Belief Networks

DOI

10.2991/ism3e-15.2015.136How to use a DOI?

Abstract

This paper presents an approach to implement vibration signals for fault diagnosis of the rolling bearing. Due to the noise and transient impacts, it is difficulty to accurately diagnosis the faults with traditional methods. So a new type of learning architecture for deep generative model called deep belief networks (DBN) is applied. Since the unsupervised learning ability in DBN, it can extract the features from the raw data layer by layer. This article mainly research how to construct the encoder using DBN which can minimize the energy between the output and input vibration signals. Compared with existing diagnosis techniques, the proposed method can learn a good representation of features with higher accuracy. The results show that DBN can more comprehensively retain the data features in pattern recognition.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Jie Tao
AU  - Yilun Liu
AU  - Dalian Yang
AU  - Fang Tang
AU  - Chi Liu
PY  - 2015/11
DA  - 2015/11
TI  - Fault Diagnosis of Rolling Bearing using Deep Belief Networks
BT  - Proceedings of the 2015 International Symposium on Material, Energy and Environment Engineering
PB  - Atlantis Press
SP  - 566
EP  - 569
SN  - 2352-5401
UR  - https://doi.org/10.2991/ism3e-15.2015.136
DO  - 10.2991/ism3e-15.2015.136
ID  - Tao2015/11
ER  -