A comprehensive review of deep learning-based fault diagnosis approaches for rolling bearings: Advancements and challenges

Rolling bearing fault diagnosis is an important technology for health monitoring and pre-maintenance of mechanical equipment, which is of great significance for improving equipment operation reliability and reducing maintenance costs. This article reviews the research progress of fault diagnosis methods for rolling bearings, with a focus on analyzing the applications, advantages, and disadvantages of traditional data-driven methods, deep learning methods, graph embedding methods, and Transformer methods in this field. In addition, further analysis was conducted on the main issues of current research, including complex network structures, insufficient information attention, difficulties in graph data processing, and challenges in long-term dependency modeling. In response to these challenges, future research should focus on designing more lightweight and efficient models, improving computational efficiency, robustness of the models, and strengthening attention and deep mining of fault features.