Innovations in Fault Detection and Diagnosis Techniques for Enhanced Reliability of Electrical Machines in Modern Application
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Abstract
Electrical equipment dependability is essential to operational efficiency and cost-effectiveness across power and energy sectors. The development of an up-to-date Fault Detection and Diagnosis (FDD) method is essential to improve electrical equipment dependability in current modern applications. Advanced FDD approaches include signal processing, statistical modelling, and machine learning algorithms to analyze vibrations and monitor temperatures of the machine. This study examines essential concepts and situations related to electrical machines problems including rotor and stator breakdowns using numerical simulations. Furthermore, case studies and computer simulations demonstrate how these strategies enhance predictive maintenance and problem diagnosis. In fact, the presented work explains the advancements in FDD utilizing hybrid model-based data-driven methods. The findings show that AI, sensor technologies, and condition monitoring systems improve problem detection accuracy and efficiency, lowering downtime and maintenance costs. This paper advance’s reliability engineering by providing a solid foundation for FDD system enhancements, encouraging the utilization of more advanced techniques such as machine learning and AI to enhance the reliability of electric machines in modern power system applications.