Review of Research on Fault Diagnosis of Rolling Bearings Based on Deep Learning

Authors

  • Caidie Duan
  • Mingchuan Zhang

DOI:

https://doi.org/10.54097/jceim.v10i3.8760

Keywords:

Rolling bearing, Fault diagnosis, Deep belief networks, Convolutional Neural Networks, Long Short-Term Memory Networks

Abstract

Deep learning has powerful capabilities in deep feature extraction and expression, and has been successfully applied in equipment fault diagnosis, overcoming the shortcomings of traditional diagnostic methods that rely on expert experience. It can save costs while improving diagnostic accuracy. This article briefly introduces three commonly used neural networks: Deep Belief Networks (DBN), Convolutional Neural Networks (CNN), and Long Short-Term Memory Networks (LSTM), and points out the problems in rolling bearing diagnosis and analyzes future development directions.

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Published

24-05-2023

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Articles

How to Cite

Duan, C., & Zhang, M. (2023). Review of Research on Fault Diagnosis of Rolling Bearings Based on Deep Learning. Journal of Computing and Electronic Information Management, 10(3), 142-146. https://doi.org/10.54097/jceim.v10i3.8760

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