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Automatic creation of Markov models for reliability assessment of safety instrumented systems

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  • Guo, Haitao
  • Yang, Xianhui

Abstract

After the release of new international functional safety standards like IEC 61508, people care more for the safety and availability of safety instrumented systems. Markov analysis is a powerful and flexible technique to assess the reliability measurements of safety instrumented systems, but it is fallible and time-consuming to create Markov models manually. This paper presents a new technique to automatically create Markov models for reliability assessment of safety instrumented systems. Many safety related factors, such as failure modes, self-diagnostic, restorations, common cause and voting, are included in Markov models. A framework is generated first based on voting, failure modes and self-diagnostic. Then, repairs and common-cause failures are incorporated into the framework to build a complete Markov model. Eventual simplification of Markov models can be done by state merging. Examples given in this paper show how explosively the size of Markov model increases as the system becomes a little more complicated as well as the advancement of automatic creation of Markov models.

Suggested Citation

  • Guo, Haitao & Yang, Xianhui, 2008. "Automatic creation of Markov models for reliability assessment of safety instrumented systems," Reliability Engineering and System Safety, Elsevier, vol. 93(6), pages 829-837.
    • Handle: RePEc:eee:reensy:v:93:y:2008:i:6:p:829-837
    DOI: 10.1016/j.ress.2007.03.029
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    1. Guo, Haitao & Yang, Xianhui, 2007. "A simple reliability block diagram method for safety integrity verification," Reliability Engineering and System Safety, Elsevier, vol. 92(9), pages 1267-1273.
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    4. Lijie, Chen & Tao, Tang & Xianqiong, Zhao & Schnieder, Eckehard, 2012. "Verification of the safety communication protocol in train control system using colored Petri net," Reliability Engineering and System Safety, Elsevier, vol. 100(C), pages 8-18.
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    6. 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.
    7. Wu, Shengnan & Zhang, Laibin & Zheng, Wenpei & Liu, Yiliu & Lundteigen, Mary Ann, 2019. "Reliability modeling of subsea SISs partial testing subject to delayed restoration," Reliability Engineering and System Safety, Elsevier, vol. 191(C).
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    13. Son, Kwang Seop & Kim, Dong Hoon & Kim, Chang Hwoi & Kang, Hyun Gook, 2016. "Study on the systematic approach of Markov modeling for dependability analysis of complex fault-tolerant features with voting logics," Reliability Engineering and System Safety, Elsevier, vol. 150(C), pages 44-57.
    14. Å nipas, Mindaugas & Radziukynas, Virginijus & ValakeviÄ ius, Eimutis, 2017. "Modeling reliability of power systems substations by using stochastic automata networks," Reliability Engineering and System Safety, Elsevier, vol. 157(C), pages 13-22.
    15. 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.
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