Machine Learning-Driven Predictive Maintenance: Enhancing Equipment Reliability
Abstract
Machine learning (ML) combined with IoT is revolutionizing predictive maintenance by analyzing sensor data to forecast equipment failures before they occur. This paper explores ML models such as deep learning, decision trees, and anomaly detection for predictive maintenance. It discusses challenges like data quality, model interpretability, and real-time processing constraints. Case studies highlight successful implementations of ML-driven predictive maintenance in industries like manufacturing, transportation, and energy, demonstrating reduced downtime, cost savings, and increased operational efficiency.
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