Electric Motor Fault Detection using Artificial Intelligence

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Faiz Ahmad, Shahzad Ahsan, Ajay Kumar, Gholam Sarwer, Sankalp Sonu


Electric motors are crucial components in various industrial applications, and their reliability is paramount for ensuring continuous operation and minimizing downtime. This paper presents a comprehensive review and analysis of artificial intelligence (AI) techniques applied to electric motor fault detection. We explore various AI methods, including machine learning, deep learning, and hybrid approaches, evaluating their effectiveness in identifying and classifying different types of motor faults. The study encompasses a wide range of motor types and fault scenarios, providing insights into the current state-of-the-art and future directions in this field. Our findings indicate that AI-based fault detection systems offer significant improvements in accuracy, early detection capabilities, and adaptability compared to traditional methods, paving the way for more reliable and efficient industrial operations.

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