Journal of Control & Instrumentation Review Article
Real-Time Fault Detection and Diagnosis in Switched Reluctance Motor Using Advance Deep Neural Network for Electrical Vehicles and Industrial Applications
Abstract
In today world, electric motor play very big role in industry and daily life. Switched reluctance motor (SRM) is one of the important types of motor because it is simple in construction, low cost and high efficiency but SRM also faces many problems like noise, vibration and fault in power converter or winding. When these faults come, performance of motor goes down and sometime full system stops working. To solve this problem, many researchers’ study fault diagnosis and fault tolerant control for SRM drives. in early time, people use traditional method like current and voltage observation for finding fault. but now, new digital and AI based method come which can detect fault fast and more accurate. deep learning, neural network and data driven system are now use for automatic fault detection in motor. This literature review collects and explains different research work done on SRM fault diagnosis and control. It shows how technology changes from simple manual checks to advanced intelligent systems. The review also emphasizes the potential of real-time intelligent diagnosis to improve motor reliability, reduce maintenance requirements, minimize unexpected downtime, enhance operational safety, and support the development of efficient and dependable SRM-based systems for electric vehicles, industrial automation, renewable energy, and other modern applications.
Keywords
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