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 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…

• NN
  National Natural Science Foundation of China

  Awards: KSF-E-34, RDF-18-02-30, 61603223
• XJ
  Xi’an Jiaotong-Liverpool University

  Award: KSF-E-34
• NS
  Natural Science Research of Jiangsu Higher Education Institutions of China

  Award: 20KJB520034
• SA
  Science and Technology Program of Suzhou

  Award: SYG202106