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Application of Reliability, Availability, and Maintainability Simulation to Process Industries: a Case Study

In: Simulation Methods for Reliability and Availability of Complex Systems

Author

Listed:
  • Aijaz Shaikh

    (ReliaSoft Corporation)

  • Adamantios Mettas

    (ReliaSoft Corporation)

Abstract

This chapter demonstrates the application of RAM (reliability, availability, and maintainability) analysis to process industries by providing a case study of a natural-gas processing plant. The goal of the chapter is to present RAM analysis as a link between the widely researched theoretical concepts related to reliability simulation and their application to complex industrial systems. It is hoped that the concepts and techniques illustrated in the chapter will help spawn new ideas to tackle real-world problems faced by practitioners of various industries, particularly the process industry.

Suggested Citation

  • Aijaz Shaikh & Adamantios Mettas, 2010. "Application of Reliability, Availability, and Maintainability Simulation to Process Industries: a Case Study," Springer Series in Reliability Engineering, in: Javier Faulin & Angel A. Juan & Sebastián Martorell & José-Emmanuel Ramírez-Márquez (ed.), Simulation Methods for Reliability and Availability of Complex Systems, chapter 0, pages 173-197, Springer.
    • Handle: RePEc:spr:ssrchp:978-1-84882-213-9_8
    DOI: 10.1007/978-1-84882-213-9_8
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    Citations

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    Cited by:

    1. Nguyen, Kim-Anh & Do, Phuc & Grall, Antoine, 2017. "Joint predictive maintenance and inventory strategy for multi-component systems using Birnbaum’s structural importance," Reliability Engineering and System Safety, Elsevier, vol. 168(C), pages 249-261.
    2. Nguyen, Kim-Anh & Do, Phuc & Grall, Antoine, 2015. "Multi-level predictive maintenance for multi-component systems," Reliability Engineering and System Safety, Elsevier, vol. 144(C), pages 83-94.
    3. Zhou, Yifan & Li, Bangcheng & Lin, Tian Ran, 2022. "Maintenance optimisation of multicomponent systems using hierarchical coordinated reinforcement learning," Reliability Engineering and System Safety, Elsevier, vol. 217(C).

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