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Analysis of simplification in Markov-based models for performance assessment of Safety Instrumented System

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  • Azizpour, Hooshyar
  • Lundteigen, Mary Ann

Abstract

One of the challenges in the use of Markov model in reliability analysis is the exponential growth of number of states and transition rates when the system gets complex. In reliability analysis of a complex safety-instrumented system a simplified model is always desired for reducing the computational cost. In practice, the complex models can be simplified to a much simpler model, which can give almost similar results with some degree of deviation. Depending on the need for the level of accuracy of results, the deviation can be partly compensated with introducing correction factors. The approximated result from the simplified model may or may not influence decision making depending on the degree of deviation and expected accuracy from the analysis. This paper shows how a complex model is simplified through an analytical approach. Comparison of results from both complex and simplified models is presented to discuss the extent in which, this variation influences decision making.

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  • Azizpour, Hooshyar & Lundteigen, Mary Ann, 2019. "Analysis of simplification in Markov-based models for performance assessment of Safety Instrumented System," Reliability Engineering and System Safety, Elsevier, vol. 183(C), pages 252-260.
    • Handle: RePEc:eee:reensy:v:183:y:2019:i:c:p:252-260
    DOI: 10.1016/j.ress.2018.09.012
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    References listed on IDEAS

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    Cited by:

    1. Liang, Qingzhu & Yang, Yinghao & Zhang, Hang & Peng, Changhong & Lu, Jianchao, 2022. "Analysis of simplification in Markov state-based models for reliability assessment of complex safety systems," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
    2. Zeng, Ying & Huang, Tudi & Li, Yan-Feng & Huang, Hong-Zhong, 2023. "Reliability modeling for power converter in satellite considering periodic phased mission," Reliability Engineering and System Safety, Elsevier, vol. 232(C).
    3. Xie, Lin & Lundteigen, Mary Ann & Liu, Yiliu, 2021. "Performance analysis of safety instrumented systems against cascading failures during prolonged demands," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    4. Zaitseva, Elena & Levashenko, Vitaly & Rabcan, Jan, 2023. "A new method for analysis of Multi-State systems based on Multi-valued decision diagram under epistemic uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    5. Cai, Baoping & Li, Wenchao & Liu, Yiliu & Shao, Xiaoyan & Zhang, Yanping & Zhao, Yi & Liu, Zengkai & Ji, Renjie & Liu, Yonghong, 2021. "Modeling for evaluation of safety instrumented systems with heterogeneous components," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    6. Zhang, Aibo & Wu, Shengnan & Fan, Dongming & Xie, Min & Cai, Baoping & Liu, Yiliu, 2022. "Adaptive testing policy for multi-state systems with application to the degrading final elements in safety-instrumented systems," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
    7. Zhang, Aibo & Srivastav, Himanshu & Barros, Anne & Liu, Yiliu, 2021. "Study of testing and maintenance strategies for redundant final elements in SIS with imperfect detection of degraded state," Reliability Engineering and System Safety, Elsevier, vol. 209(C).
    8. Cheraghi, Morteza & Taghipour, Sharareh, 2024. "A mathematical optimization model for determining safety integrity levels in process facilities," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    9. Bistouni, Fathollah & Jahanshahi, Mohsen, 2019. "Reliability-aware ring protection link selection in Ethernet ring mesh networks," Reliability Engineering and System Safety, Elsevier, vol. 191(C).

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