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Mathematical Models for Industrial System Reliability

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Advances in Computational Mathematics for Industrial System Reliability and Maintainability

Part of the book series: Springer Series in Reliability Engineering ((RELIABILITY))

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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.

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  1. School of Engineering, Macquarie University, Sydney, NSW, Australia

    Mohammad Yazdi

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Yazdi, M. (2024). Mathematical Models for Industrial System Reliability. In: Advances in Computational Mathematics for Industrial System Reliability and Maintainability. Springer Series in Reliability Engineering. Springer, Cham. https://doi.org/10.1007/978-3-031-53514-7_2

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  • DOI: https://doi.org/10.1007/978-3-031-53514-7_2

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