Abstract
The integration of embedded systems with Industrial Internet of Things (IIoT) technologies has revolutionized predictive maintenance (PdM) practices across modern manufacturing and industrial domains. Embedded systems equipped with edge computing capabilities, smart sensors, and wireless communication interfaces enable real-time data acquisition, processing, and analysis. These systems contribute to the early detection of faults and degradation patterns in machinery, minimizing unplanned downtime and maintenance costs. This paper explores the architectural framework and functional applications of embedded systems in IIoT environments for predictive maintenance. It discusses sensor integration, data analytics models, communication protocols, and implementation challenges. The research highlights case studies demonstrating enhanced operational efficiency through PdM using embedded platforms.
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