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Journal of Mechanical Science and Technology

, Volume 32, Issue 11, pp 5139–5145 | Cite as

Fault diagnosis method of rolling bearing based on deep belief network

  • Zhiwu Shang
  • Xiangxiang Liao
  • Rui Geng
  • Maosheng Gao
  • Xia Liu
Article
  • 9 Downloads

Abstract

A method based on the theory of deep learning and feature extraction and a fault diagnosis model of a rolling bearing based on deep belief network are proposed in this study considering the complex, nonlinear, and non-stationary vibration signal of the rolling bearing. To some extent, the method avoids the complex structure of deep neural network and can be easily trained. Experimental results show that the recognition rate of the method reaches 100 %. The method can identify various types of faults accurately and has good fault diagnosis capability, which can provide the convenience for maintenance.

Keywords

Deep belief network Fault diagnosis Restricted Boltzmann machine Rolling bearing 

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Copyright information

© The Korean Society of Mechanical Engineers and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Zhiwu Shang
    • 1
  • Xiangxiang Liao
    • 1
  • Rui Geng
    • 1
  • Maosheng Gao
    • 1
  • Xia Liu
    • 1
  1. 1.Tianjin Key Laboratory of Modern Mechatronics Equipment TechnologyTianjin Polytechnic UniversityTianjinChina

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