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Predictive Analytics in Industrial IoT (IIoT): Enhancing Efficiency and Reliability
Abstract
Industrial Internet of Things (IIoT) generates vast volumes of data from interconnected sensors and devices, creating opportunities for predictive analytics to enhance operational efficiency and reliability. This chapter reviews key predictive analytics models and techniques applied in IIoT, including traditional statistical methods, machine learning, and deep learning approaches. It explores essential IIoT data sources, infrastructure, and applications such as predictive maintenance, process optimization, and safety management. The chapter also discusses critical challenges including data quality, scalability, security, model interpretability, and integration with legacy systems. Finally, future research directions highlight advancements in edge AI, digital twins, explainable AI, and sustainable IIoT practices.
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