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

Chen, Xiaohan, Yang, Rui ORCID: 0000-0002-5634-5476, Xue, Yihao, Huang, Mengjie, Ferrero, Roberto ORCID: 0000-0001-7820-9021 and Wang, Zidong (2023) Deep Transfer Learning for Bearing Fault Diagnosis: A Systematic Review Since 2016. IEEE Transactions on Instrumentation and Measurement, 72. p. 1.

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Official URL: http://dx.doi.org/10.1109/tim.2023.3244237

Abstract

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 a significant decline in fault diagnosis performance. In order to satisfy this assumption, the transfer learning concept is introduced in deep learning by transferring the knowledge learned from other data or models. Due to the excellent capability of feature learning and domain transfer, deep transfer learning methods have gained widespread attention in bearing fault diagnosis in recent years. This review presents a comprehensive review of the development of deep transfer learning-based bearing fault diagnosis approaches since 2016. In this review, a novel taxonomy of deep transfer learning-based bearing fault diagnosis methods is proposed from the perspective of target domain data properties divided by labels, machines, and faults. By covering the whole life cycle of deep transfer learning-based fault diagnosis and discussing the research challenges and opportunities, this review provides a systematic guideline for researchers and practitioners to efficiently identify suitable deep transfer learning models based on the actual problems encountered in bearing fault diagnosis.

Item Type:	Article
Uncontrolled Keywords:	Fault diagnosis, Transfer learning, Data models, Task analysis, Feature extraction, Deep learning, Hidden Markov models, Bearing fault, deep transfer learning, fault diagnosis
Divisions:	Faculty of Science and Engineering > School of Electrical Engineering, Electronics and Computer Science
Depositing User:	Symplectic Admin
Date Deposited:	01 Mar 2023 10:43
Last Modified:	15 Mar 2024 01:27
DOI:	10.1109/tim.2023.3244237
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3168654