Anant Singh
Independent Researcher
India
Abstract
Predictive maintenance (PdM) is a transformative approach in manufacturing aimed at optimizing equipment uptime and minimizing unplanned downtimes by forecasting potential failures before they occur. With the increasing deployment of Internet of Things (IoT) sensors, even legacy manufacturing equipment can be retrofitted to collect valuable operational data. This manuscript explores the integration of legacy IoT sensors with Python-based analytics frameworks to develop effective predictive maintenance models in manufacturing environments. The study reviews state-of-the-art techniques for sensor data acquisition, preprocessing, feature extraction, and predictive modeling using classical machine learning algorithms feasible with 2021 technologies. The methodology involves collecting real-time sensor data, performing signal processing and statistical feature engineering, and applying supervised learning models such as Random Forest and Support Vector Machines (SVM) to predict equipment health. Experimental results demonstrate the viability of legacy IoT sensor data combined with Python analytics to enhance maintenance decision-making, reduce downtime, and improve operational efficiency. The study concludes with a discussion on implementation challenges and future directions for integrating PdM solutions in legacy manufacturing setups.
Keywords
Predictive maintenance, Legacy IoT sensors, Manufacturing, Python analytics, Machine learning, Equipment health monitoring
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