Abstract
This article presents a thorough examination of hybrid machine learning methodologies employed to improve fault tolerance in Industrial Internet of Things (IIoT) systems. These hybrid models enhance fault detection precision and resilience by integrating various machine learning techniques, surpassing conventional methods. The research commences with a comprehensive literature review of contemporary fault tolerance techniques, subsequently presenting a comparison of various datasets and a proposal for hybrid predictive algorithms. The efficacy of these algorithms is subsequently assessed in comparison to cutting-edge techniques. Principal findings underscore the enhanced precision of hybrid models in real-time applications, their diminished computing complexity, and their scalability in dynamic industrial settings. Nonetheless, issues pertaining to integration, data reliance, and real-time implementation are also addressed. The article concluded by outlining prospective research methods, encompassing sophisticated integration techniques, automated model optimisation, adaptive self-learning frameworks, and methodologies to safeguard data privacy. This paper outlines a framework for future advancements in fault tolerance, with considerable implications for both academia and industry.
Published Version
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