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A multiphase dynamic Bayesian networks methodology for the determination of safety integrity levels

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  • Cai, Baoping
  • Liu, Yu
  • Fan, Qian

Abstract

A novel safety integrity levels (SILs) determination methodology based on multiphase dynamic Bayesian networks (MDBNs) for safety instrumented systems is proposed. Proof test interval phase and proof test phase are modeled separately using dynamic Bayesian networks, and integrated together to form the MDBNs. The unified structure models of MDBNs for k-out-of-n architectures are constructed, and the procedures of automatic creation of conditional probability tables are developed. The target failure measures, that is, probability of failure on demand, average probability of failure on demand, probability of failing safely, average probability of failing safely, and SIL of safety instrumented systems operating in a low demand mode, are evaluated using the proposed MDBNs. The effects of time interval of MDBNs, common cause weight, imperfect proof test and repair on model precision are researched. User-friendly SIL determination software is developed by using MATLAB GUI to assist engineers in determining the SIL value.

Suggested Citation

  • Cai, Baoping & Liu, Yu & Fan, Qian, 2016. "A multiphase dynamic Bayesian networks methodology for the determination of safety integrity levels," Reliability Engineering and System Safety, Elsevier, vol. 150(C), pages 105-115.
    • Handle: RePEc:eee:reensy:v:150:y:2016:i:c:p:105-115
    DOI: 10.1016/j.ress.2016.01.018
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    References listed on IDEAS

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    Cited by:

    1. Khalil, Y.F., 2019. "New statistical formulations for determination of qualification test plans of safety instrumented systems (SIS) subject to low/high operational demands," Reliability Engineering and System Safety, Elsevier, vol. 189(C), pages 196-209.
    2. Zheng Tang & Yijia Li & Xiaofeng Hu & Huanggang Wu, 2019. "Risk Analysis of Urban Dirty Bomb Attacking Based on Bayesian Network," Sustainability, MDPI, vol. 11(2), pages 1-12, January.
    3. 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.
    4. Kiswendsida Abel Ouedraogo & Julie Beugin & El‐Miloudi El‐Koursi & Joffrey Clarhaut & Dominique Renaux & Frederic Lisiecki, 2018. "Toward an Application Guide for Safety Integrity Level Allocation in Railway Systems," Risk Analysis, John Wiley & Sons, vol. 38(8), pages 1634-1655, August.
    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. Gabriel, Angelito & Ozansoy, Cagil & Shi, Juan, 2018. "Developments in SIL determination and calculation," Reliability Engineering and System Safety, Elsevier, vol. 177(C), pages 148-161.
    7. Wu, Shengnan & Zhang, Laibin & Barros, Anne & Zheng, Wenpei & Liu, Yiliu, 2018. "Performance analysis for subsea blind shear ram preventers subject to testing strategies," Reliability Engineering and System Safety, Elsevier, vol. 169(C), pages 281-298.
    8. Meng, Huixing & Kloul, Leïla & Rauzy, Antoine, 2018. "Modeling patterns for reliability assessment of safety instrumented systems," Reliability Engineering and System Safety, Elsevier, vol. 180(C), pages 111-123.
    9. Amrin, Andas & Zarikas, Vasileios & Spitas, Christos, 2018. "Reliability analysis and functional design using Bayesian networks generated automatically by an “Idea Algebra†framework," Reliability Engineering and System Safety, Elsevier, vol. 180(C), pages 211-225.

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