Fault Diagnostics of Synchronous Motor Based on Artificial Intelligence - Samir Abood; Annamalai Annamalai; Mohamed Chouikha; Turk Nejress (Machines)
Electrical motors and drives are the unseen forces driving our modern world, powering everything from electric vehicles to industrial machinery. The efficiency, precision, and sustainability of these systems are very important. Unexpected motor failures can cause major disruptions, risk human lives, and cause costly downtime. This research aims to improve the efficiency and performance of three-phase synchronous machines using Artificial Intelligence (AI) strategies. The research uses real-time data and optimization techniques to explore advanced diagnostic techniques, fault diagnosis, fault tolerance, and condition monitoring schemes to enhance safety, reliability, and performance in electric synchronous operations. Experimental results demonstrated that AI-driven methods achieve higher fault detection accuracy than traditional techniques. The findings highlight the potential of AI in improving the reliability of industrial synchronous motors.