Artificial Intelligence-Based Approaches for Condition Monitoring and Fault Diagnosis of Rolling Element Bearings

To save money, improve dependability, and maintain system safety, rotating machinery must be regularly monitored. A variety of modern approach-based approaches are utilised to detect and predict faults in rolling element bearings. These techniques include data extraction, clever structures based on period and rate of recurrence, time-frequency domains and detail mix, sign/image processing, intelligent diagnostics, and statistics fusion. The prominence of AIML ideas has heightened interest in this subject. The application of artificial intelligence approaches to industrial equipment, mechanisation, and development represents the ultimate limit of AI adaptability. Signal and data processing techniques are employed to solve problems in a well-developed body of literature. This paper's main contribution is to provide a detailed review. Third, utilising emerging developments in artificial intelligence and techniques, fault detection methods employing time domain and frequency domain analysis, and the bearing's CM, which encompasses a variety of CM approaches.