Abstract:With the extensive deployment of industrial sensors and the rapid development of artificial intelligence algorithms, data-driven intelligent fault diagnosis technology has become a key part and a hot topic in Prognostics and Health Management of mechanical equipment. However, such methods rely on substantial labeled data and demand strict consistency in data distribution, thereby causing a significant drop in the accuracy and robustness of related methods in real industrial scenarios. Transfer learning, as an effective approach to tackling the problems of inconsistent data distribution and small-sample fault diagnosis, has drawn widespread attention from both academia and industry. It markedly enhances the generalization performance of the model in the target domain by transferring the knowledge acquired in the source domain to the target domain. To investigate the current state of transfer learning-driven intelligent fault diagnosis methods for mechanical equipment and the crucial technical challenges that urgently need to be resolved, an analysis and summary of the existing literature in this field have been carried out. Firstly, the research progress and current status of intelligent fault diagnosis for mechanical equipment in domestic and international studies have been systematically reviewed. Then, focusing on transfer learning technologies, the advantages and limitations of various transfer learning fault diagnosis methods have been analyzed and compared. From the perspectives of different application scenarios and key technical issues in the industry, the intelligent fault diagnosis technologies for mechanical equipment driven by transfer learning have been summarized and critically evaluated. Finally, current research hotspots have been explored and the technical bottlenecks have been thoroughly analyzed, and potential solutions to existing challenges along with future development trends are identified. Studies show that while transfer learning has garnered widespread attention in the field of intelligent fault diagnosis for mechanical equipment, many technical issues remain to be resolved. With the rapid development of artificial intelligence technologies and the continued efforts of experts and scholars in advancing transfer learning theories and applications, a solid theoretical and technical foundation can be established for the development of intelligent fault diagnosis methods for mechanical equipment.
