Condition Monitoring and Fault Diagnosis of Rolling Contact Element Bearings Based on Artificial Intelligence Techniques: A Review Approach

Abstract

The current review paper is related to Condition Monitoring and Fault Diagnosis of Rolling Contact Element Bearings based on Artificial Intelligence Techniques. To save costs, boost dependability, and ensure system safety, rotating machinery must be constantly monitored. Numerous contemporary approach-based procedures identify and foretell defects in rolling element bearings. These techniques include data extraction, period & rate of recurrence clever structures, time-frequency domains, detail mix, sign/image processing, intelligent diagnostics, and statistics fusion. The popularity of AIML ideas has also increased attention in this field. Artificial intelligence approaches to industrial equipment, mechanisation, and development represent the net boundaries of AI adaptability. This paper's primary contribution is to give a thorough review of bearings. Secondly, signal and data processing techniques are used to approach the problems in a well-developed body of literature. Thirdly, with the aid of new trends in artificial intelligence and methodologies, defect detection methods employing time domain and frequency domain analysis, and the bearing's CM, which encompasses a variety of CM approaches.