Reinforcement Learning for Rolling Bearing Fault Diagnosis--A Comprehensive Review.

Automatic fault detection and machine diagnosis play a crucial role in preventive maintenance. This study highlights the importance of fault diagnosis in machinery and emphasizes the benefits of preventive and predictive maintenance strategies. The overviews machine and deep learning techniques, and feature extraction methods for automatic fault diagnosis in rolling bearings. The study discusses the challenges machine and deep learning approaches face, including their limited adaptability to different operational conditions and environmental variations. It also suggests reinforcement learning as a potential automatic rolling bearing fault detection solution. The study differentiates between various reinforcement learning methods, including model-based and model-free approaches, and underscores the advantages of deep reinforcement learning. Furthermore, it evaluates several studies that utilized reinforcement learning for feature optimization, parameter optimization, and addressing class imbalance in rolling bearing fault diagnosis. Lastly, the paper summarizes key findings and proposes future research directions, including integrating reinforcement learning with other machine or deep learning methods and developing new algorithms better suited for large datasets and real-time applications. [ABSTRACT FROM AUTHOR]