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Reliability performance of safety instrumented systems: A common approach for both low- and high-demand mode of operation

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  • Jin, Hui
  • Lundteigen, Mary Ann
  • Rausand, Marvin

Abstract

Safety instrumented systems (SISs) are usually divided into two modes of operation, low-demand and high-demand. Unfortunately, this classification is not easy to justify and the available formulas that are used to quantify the reliability performance in these two modes of operation are unable to capture combined effects of functional testing, spurious activations, and successful responses to demands. This article discusses some important modeling issues for SIS reliability performance quantification, and demonstrates their implementation in a Markov model. The accuracy of the Markov model for a simple case study of a pressure transmitter is verified through comparison with a scenario-based formula, and it is shown that the Markov approach gives a sufficiently accurate result for all demand rates, covering both low- and high-demand modes of operation.

Suggested Citation

  • Jin, Hui & Lundteigen, Mary Ann & Rausand, Marvin, 2011. "Reliability performance of safety instrumented systems: A common approach for both low- and high-demand mode of operation," Reliability Engineering and System Safety, Elsevier, vol. 96(3), pages 365-373.
    • Handle: RePEc:eee:reensy:v:96:y:2011:i:3:p:365-373
    DOI: 10.1016/j.ress.2010.11.007
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    References listed on IDEAS

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    1. Lundteigen, Mary Ann & Rausand, Marvin, 2008. "Spurious activation of safety instrumented systems in the oil and gas industry: Basic concepts and formulas," Reliability Engineering and System Safety, Elsevier, vol. 93(8), pages 1208-1217.
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    Cited by:

    1. Ding, Long & Wang, Hong & Jiang, Jin & Xu, Aidong, 2017. "SIL verification for SRS with diverse redundancy based on system degradation using reliability block diagram," Reliability Engineering and System Safety, Elsevier, vol. 165(C), pages 170-187.
    2. Alizadeh, Siamak & Sriramula, Srinivas, 2018. "Impact of common cause failure on reliability performance of redundant safety related systems subject to process demand," Reliability Engineering and System Safety, Elsevier, vol. 172(C), pages 129-150.
    3. 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.
    4. Jon T Selvik & Eirik B Abrahamsen, 2017. "On the meaning of accuracy and precision in a risk analysis context," Journal of Risk and Reliability, , vol. 231(2), pages 91-100, April.
    5. Hokstad, Per, 2014. "Demand rate and risk reduction for safety instrumented systems," Reliability Engineering and System Safety, Elsevier, vol. 127(C), pages 12-20.
    6. H. Metatla & M. Rouainia, 2022. "Functional and dysfunctional analysis of a safety instrumented system (SIS) through the common cause failures (CCFs) assessment. Case of high integrity protection pressure system (HIPPS)," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(4), pages 1932-1954, August.
    7. Redutskiy Yury & Balycheva Marina & Dybdahl Hendrik, 2022. "Employee scheduling and maintenance planning for safety systems at the remotely located oil and gas industrial facilities," Engineering Management in Production and Services, Sciendo, vol. 14(4), pages 1-21, December.
    8. Eisinger, S. & Oliveira, L.F., 2021. "Evaluating the safety integrity of safety systems for all values of the demand rate," Reliability Engineering and System Safety, Elsevier, vol. 210(C).
    9. Redutskiy, Yury & Camitz-Leidland, Cecilie M. & Vysochyna, Anastasiia & Anderson, Kristanna T. & Balycheva, Marina, 2021. "Safety systems for the oil and gas industrial facilities: Design, maintenance policy choice, and crew scheduling," Reliability Engineering and System Safety, Elsevier, vol. 210(C).
    10. 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.
    11. Ding, Long & Wang, Hong & Kang, Kai & Wang, Kai, 2014. "A novel method for SIL verification based on system degradation using reliability block diagram," Reliability Engineering and System Safety, Elsevier, vol. 132(C), pages 36-45.
    12. Cherfi, Abraham & Leeman, Michel & Meurville, Florent & Rauzy, Antoine, 2014. "Modeling automotive safety mechanisms: A Markovian approach," Reliability Engineering and System Safety, Elsevier, vol. 130(C), pages 42-49.

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