Developing an IoT-Enabled Predictive Maintenance System for Industrial Machines using Machine Learning: A Comprehensive Review

Abstract

Predictive maintenance has emerged as a transformative approach in industrial operations, shifting from reactive and preventive strategies to data-driven, proactive maintenance paradigms. The convergence of Internet of Things (IoT) technologies and Machine Learning (ML) algorithms has enabled real-time monitoring, analysis, and prediction of equipment failures before they occur. This review paper provides a comprehensive analysis of IoT-enabled predictive maintenance systems for industrial machines, examining the integration of sensor networks, communication protocols, data analytics platforms, and ML algorithms. We explore various ML techniques including supervised learning, unsupervised learning, deep learning, and ensemble methods applied to predictive maintenance tasks. The paper discusses architectural frameworks, implementation challenges, case studies, and future research directions. Through systematic analysis of recent literature, we identify key trends, gaps, and opportunities in this rapidly evolving field, providing insights for researchers and practitioners developing next-generation predictive maintenance solutions.