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Abstract
Predictive maintenance (PdM) represents a transformative shift in industrial operations, aiming to foresee and prevent equipment failures before they occur. Leveraging the convergence of Internet of Things (IoT) sensors and Machine Learning (ML) algorithms, industries can collect, analyze, and interpret large volumes of operational data in real time. This paper investigates the architecture, methodologies, and practical outcomes of integrating ML with IoT for predictive maintenance, evaluating performance improvement, cost reduction, and operational efficiency. Key insights are drawn from case studies and past literature, emphasizing scalable models and real-world deployments.
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