A survey on Deep Learning based bearing fault diagnosis

By Duy-Tang Hoang, Hee-Jun Kang. Neurocomputing, 2019

769 citations recorded by the source corpus.

Abstract Nowadays, Deep Learning is the most attractive research trend in the area of Machine Learning. With the ability of learning features from raw data by deep architectures with many layers of non-linear data processing units, Deep Learning has become a promising tool for intelligent bearing fault diagnosis. This survey paper intends to provide a systematic review of Deep Learning based bearing fault diagnosis. The three popular Deep Learning algorithms for bearing fault diagnosis including Autoencoder, Restricted Boltzmann Machine, and Convolutional Neural Network are briefly introduced. And their applications are reviewed through publications and research works on the area of bearing fault diagnosis. Further applications and challenges in this research area are also discussed.

Subfields: Control and Systems Engineering, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/850565325986cc4f541ff81666ce0c7dd6cc8b2c

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Control and Systems Engineering

1. Chuang Sun
2. Xuefeng Chen
3. Ruqiang Yan
4. Shibin Wang
5. Zhibin Zhao

In Mechanical Engineering

1. Shibin Wang
2. Chuang Sun
3. Ruqiang Yan
4. Xuefeng Chen
5. Zhibin Zhao

Citations in the corpus

• Deep Transfer Learning for Bearing Fault Diagnosis: A Systematic Review Since 2016 (2023, neutral): “bearing fault diagnosis [1], [2], [3], [4], [5], [6], [7], [8].”
• Deep Learning Algorithms for Rotating Machinery Intelligent Diagnosis: An Open Source Benchmark Study (2020, background): “[12] provided a comprehensive review of three popular DL algorithms (AE, DBN, and CNN) for bearing fault diagnosis.”