Deep Transfer Learning for Bearing Fault Diagnosis: A Systematic Review Since 2016

The traditional deep learning-based bearing fault diagnosis approaches assume that the training and test data follow the same distribution. This assumption, however, is not always true for the bearing data collected in practical scenarios, leading to significant decline to fault diagnosis performanc...

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Veröffentlicht in:	IEEE transactions on instrumentation and measurement 2023-01, Vol.72, p.1-1
Hauptverfasser:	Chen, Xiaohan, Yang, Rui, Xue, Yihao, Huang, Mengjie, Ferrero, Roberto, Wang, Zidong
Format:	Artikel
Sprache:	eng
Schlagworte:	Bearing fault Data models Deep learning deep transfer learning Domains Fault diagnosis Feature extraction Hidden Markov models Task analysis Taxonomy Transfer learning
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