Mechanical fault diagnosis is an important method to accurately identify the health condition of mechanical equipment and ensure its safe operation. Fault diagnosis based on transfer learning usually refers to the diagnostic method verified in simulation or laboratory that can be generalized to actual operating equipment. The implementation methods of transfer learning include feature-based transfer, model-based transfer, etc., through enhancement, fine-tuning, and other technical techniques.

This topic introduces the principle of transfer learning, summarizes the research and application of transfer learning in the field of fault diagnosis, discusses the shortcomings of transfer learning in the field of fault diagnosis, and discusses the future research direction of transfer learning in the field of fault diagnosis.

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