Smart Predictive Maintenance of Induction Motor using ML

Abstract

Induction motors are vital components in industrial and commercial systems, where unexpected failures can lead to costly downtime and reduced productivity. Traditional maintenance strategies such as corrective and preventive maintenance are often inefficient, either reacting too late or performing unnecessary servicing. Predictive maintenance, powered by machine learning (ML) techniques, offers a smarter approach by forecasting motor health conditions based on real-time data analysis. This review paper presents an overview of recent advancements in predictive maintenance for induction motors using ML algorithms. Various techniques such as support vector machines (SVM), artificial neural networks (ANN), random forests, and deep learning models are discussed for fault detection, diagnosis, and remaining useful life (RUL) estimation. The paper also highlights the importance of feature extraction from vibration, current, and temperature signals, as well as the integration of Internet of Things (IoT) and cloud computing for real-time monitoring. Comparative analysis of different ML approaches is provided to identify their strengths, limitations, and potential for industrial application. Finally, the review outlines current challenges and future research directions for developing efficient, scalable, and interpretable predictive maintenance frameworks for induction motors.