Abstract
Predictive maintenance (PdM) has emerged as a transformative approach in industrial systems, leveraging the power of big data analytics to forecast equipment failures and optimize maintenance schedules. By integrating real-time sensor data, historical maintenance records, and machine learning algorithms, PdM minimizes unexpected downtime and enhances operational efficiency. This paper discusses the architectural framework of PdM systems, data acquisition and preprocessing challenges, and the role of predictive algorithms in decision-making. Case studies from manufacturing and energy sectors validate the effectiveness of big data-driven PdM in reducing costs and improving asset lifecycle management.
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