Introduction to Computational Mathematics in Industrial Systems

Mohammad Yazdi ()
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Mohammad Yazdi: Macquarie University

Chapter Chapter 1 in Advances in Computational Mathematics for Industrial System Reliability and Maintainability, 2024, pp 1-16 from Springer

Abstract: Abstract The rapidly evolving landscape of industrial systems has witnessed significant advancements over the years, with computational mathematics emerging as a pivotal influencer in shaping their reliability and maintainability. This chapter looks into the multifaceted realm of industrial systems, offering a granular classification and tracing their evolutionary trajectory from rudimentary mechanization to sophisticated automation bolstered by computer technologies. A particular emphasis is placed on system reliability and maintainability, two crucial metrics that dictate a system's longevity and operational efficiency. In this context, computational mathematics carves out a niche for itself, showcasing its prowess in predictive maintenance, simulation-based decision-making, and probabilistic modelling, thereby enhancing the robustness of industrial systems. However, it is also imperative to acknowledge the potential limitations of these computational methodologies, including their dependency on data precision and the requisites of specialized expertise. Real-world applications further elucidate the transformative impact of computational strategies in global industrial conglomerates. Through this comprehensive overview, the chapter underscores the inextricable bond between computational mathematics and the future of reliable and maintainable industrial systems.

Keywords: Rapidly evolving; Industrial systems; Advancements; Computational mathematics; Reliability; Maintainability; Mechanization; Automation; Computer technologies; Predictive maintenance; Simulation; Probabilistic modelling; Robustness; Limitations; Data precision; Expertise; Real-world applications; Conglomerates; Transformative impact; Future (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:spr:ssrchp:978-3-031-53514-7_1

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DOI: 10.1007/978-3-031-53514-7_1

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