Application of Machine Learning for Diagnosing Faults in Electrical Machines (Permanent Magnet Synchronous Motor, Switched Reluctance Motor or Induction Motor) Used in Electric Vehicles

  Coimbatore Campus

Research Topic - Application of Machine Learning for Diagnosing Faults in Electrical Machines

Electrical motors form an integral part of any Electric Vehicle (EV). Most EV manufacturers currently prefer induction motors (IM) or permanent magnet synchronous motors (PMSM) as propulsion motors because the former offers low cost, ruggedness, and less maintenance. The latter offers high power density with high efficiency to permanent magnets. Any problem with the motor’s operation will have an impact on the entire EV. As a result, detecting faults at an early stage will allow us to avoid catastrophic damage to the propulsion motor, take appropriate preventive action, and reduce financial losses. Bearing and eccentricity (69%) are the most common faults in propulsion motors (IM or PMSM), followed by stator winding (21%), rotor (7%), and other faults (3%).

Department & Campus

Department of Electrical and Electronics Engineering, Coimbatore

Skillsets Preferred from Applicants

Basics of Electrical Machines and SIgnal Processing

Faculty Profile

Praveenkumar N.

Associate Professor (Sr. Gr.)), Electrical and Electronics Engineering, School of Engineering, Coimbatore.


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