A Comprehensive Review of Deep Transfer Learning for Intelligent Fault Diagnosis in Rotating Machines

ABSTRACT Rotating machinery serves as a critical backbone for national economic growth and is extensively utilized as mechanical equipment across diverse industrial domains. However, failures in these machines can cause significant operational disruptions, financial losses, and safety risks. This has led to an increased focus on advancing intelligent fault diagnosis techniques, with industrial big data and artificial intelligence (AI) emerging as powerful tools. Recently, deep transfer learning (DTL) has gained significant attention as a promising approach for cross‐domain and cross‐machine diagnosis, particularly in cases with limited faulty data and complex conditions. Despite significant progress, a comprehensive review of recent advancements in DTL remains absent, and well‐defined future research directions for its development are lacking. To address this gap, this review paper provides an extensive analysis of the existing literature regarding the recent applications of DTL in the fault diagnosis of rotating machines. This review categorizes existing DTL methods for machine fault diagnosis into four key aspects: instance‐based, feature‐based, parameter‐based, and adversarial‐based transfer. The survey first introduces the basic concepts of DTL, then explores recent research applications in depth and provides valuable perspectives in the field. In addition, the review examines the strengths and weaknesses of different DTL types in the fault diagnosis of rotating machines. Building on recent research progress, this review outlines emerging challenges and potential future research directions in the field. Thus, the review article aims to serve as a valuable resource and inspire further exploration among researchers, policymakers, and industry experts in the domain.