Mathematical Models for Industrial System Reliability

Mohammad Yazdi ()
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Mohammad Yazdi: Macquarie University

Chapter Chapter 2 in Advances in Computational Mathematics for Industrial System Reliability and Maintainability, 2024, pp 17-42 from Springer

Abstract: Abstract In today’s technologically driven industrial environments, ensuring system reliability has become paramount. The chapter examines the various mathematical models employed to assess, predict, and enhance the reliability of industrial systems. Beginning with a foundational understanding of reliability, the chapter talks about traditional probabilistic models, such as exponential and Weibull distributions, and modern stochastic processes and Bayesian approaches. Special attention is given to the suitability and accuracy of models in addressing real-world industrial challenges. Practical applications are highlighted through case studies, demonstrating how these models have been instrumental in mitigating system failures, reducing downtimes, and optimizing maintenance strategies. The chapter also explores the intersection of data analytics and reliability modelling, emphasizing the increasing role of machine learning and artificial intelligence in forecasting and improving industrial system reliability. The study concludes with a forward-looking perspective on emerging trends in reliability modelling and the potential avenues for future research.

Keywords: Reliability; Mathematical models; Probabilistic models; Exponential distribution; Weibull distribution; Stochastic processes; Bayesian approaches; Industrial systems; System failures; Maintenance optimization; Data analytics; Machine learning; Artificial intelligence; Forecasting; Emerging trends (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_2

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DOI: 10.1007/978-3-031-53514-7_2

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