Machine Learning and Deep Learning Algorithms for Bearing Fault Diagnostics - A Comprehensive Review

By Shen Zhang, Shibo Zhang, Bingnan Wang, Thomas G. Habetler. arXiv.org, 2019

477 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/1f1819af398f6aaf29265d5ebb95ccd748bc0859

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1. Zhibin Zhao
2. Ruqiang Yan
3. Rui Yang
4. Chuang Sun
5. Tianfu Li
6. Xuefeng Chen
7. Shibin Wang
8. Yihao Xue

Citations in the corpus

• Deep Learning Algorithms for Rotating Machinery Intelligent Diagnosis: An Open Source Benchmark Study (2020, methodology): “[13] systematically reviewed the machine learning and DL-based algorithms for bearing fault diagnosis and also provided a comparison of the classification accuracy of CWRU”
• Bearing fault diagnosis base on multi-scale CNN and LSTM model (2020, background): “Figure 2 shows the typical roller bearing structure comprises an outer race mounted in the bearing housing, an inner race mounted on the rotating shaft, rolling elements, and a supporting cage (Zhang et al. 2020).”
• Deep Transfer Learning for Bearing Fault Diagnosis: A Systematic Review Since 2016 (2023, background): “In order to reduce the maintenance cost and avoid casualties, it is necessary to diagnose the faults timely and accurately before the occurrence of immeasurable loss [9–13].”
• Bearing Fault Detection and Diagnosis Using Case Western Reserve University Dataset With Deep Learning Approaches: A Review (2020, methodology): “Among all the transfer learning approaches, domain adaptation is one of the most popular methods [2].”