Machine Learning Applications for Predictive Maintenance in IoT

Abstract

Predictive maintenance has emerged as a pivotal strategy in the Internet of Things (IoT), leveraging machine learning algorithms to anticipate equipment failures before they occur. This paper explores the significance of predictive maintenance in IoT, its reliance on data analytics and machine learning, and the role of IoT sensors in enhancing efficiency. The discussion encompasses key components such as data collection and processing, feature engineering, anomaly detection, failure prediction, root cause analysis, and health monitoring, all essential for the success of predictive maintenance initiatives. The application of predictive maintenance extends beyond industrial IoT to smart buildings and the transportation industry, promising increased efficiency, reduced downtime, and lower maintenance costs. Despite challenges, the adoption of predictive maintenance is facilitated by solutions addressing data management complexities. Looking ahead, the integration of AI, machine learning, IoT, and cloud computing foretells a promising future for predictive maintenance, making it more cost-effective and efficient across diverse industries. The benefits of increased productivity, reduced breakdowns, and lower maintenance costs position predictive maintenance as a transformative approach to equipment upkeep in the evolving landscape of IoT.