"Predictive Maintenance in Industrial Internet of Things (IoT) Environments: A Machine Learning Approach

Thesis Abstract

Abstract


In the evolving landscape of industrial processes, this research addresses the imperative of predictive maintenance leveraging the Industrial Internet of Things (IIoT) and machine learning. The study explores the convergence of IIoT and predictive maintenance, emphasizing the role of machine learning models in enhancing efficiency and reducing downtime. The literature review scrutinizes the state-of-the-art in IIoT, predictive maintenance algorithms, and the integration of machine learning in industrial contexts. The core of the research involves the development and assessment of a predictive maintenance system within IIoT environments, covering sensor networks, communication protocols, and diverse machine learning approaches. The implementation and evaluation phases encompass integration with industrial processes, performance metrics, economic impact analysis, user interface considerations, and compliance with regulatory and security standards. The outcomes contribute to the discourse on leveraging IIoT and machine learning for proactive maintenance strategies in complex industrial settings.