A Comprehensive Review of AI-Based Predictive Maintenance Techniques for Industrial Induction Motors

Abstract

Industrial induction motors are critical components in manufacturing industries and their reliable operation is essential for maintaining productivity and reducing operational costs. The increasing demand for intelligent maintenance systems has accelerated the adoption of Artificial Intelligence (AI)-based predictive maintenance approaches. This review paper presents a comprehensive analysis of conventional maintenance strategies, condition monitoring techniques, and advanced AI-driven predictive maintenance methods for industrial induction motors. Various fault diagnosis approaches based on vibration analysis, Motor Current Signature Analysis (MCSA), machine learning, deep learning, and prognostics are critically examined. The study also reviews the role of Industry 4.0 technologies, including Industrial Internet of Things (IIoT), cyber-physical systems, cloud computing, and digital twins in enhancing maintenance decision-making. A comparative assessment of existing literature highlights recent research trends, technological advancements, and performance improvements achieved through AI integration. Furthermore, research gaps related to real-time implementation, multi-sensor data fusion, explainable AI, and Remaining Useful Life (RUL) prediction are identified. The review concludes that AI-driven predictive maintenance has significant potential to improve reliability, reduce downtime, optimize maintenance costs, and support the development of intelligent manufacturing systems.

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