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Reliability of safety-instrumented systems subject to partial testing and common-cause failures

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  • Jin, Hui
  • Rausand, Marvin

Abstract

Partial testing is sometimes used as a supplement to proof testing to improve the reliability of safety-instrumented systems (SISs) in low-demand mode of operation. This paper studies the effect of partial testing on SIS reliability. Simplified formulas are developed to include both partial and proof testing in the calculation of the average probability of failure on demand (PFDavg). The proposed formulas can handle situations where partial testing is performed periodically and non-periodically. Common-cause failures (CCFs) are treated by using the beta-factor model, and different β-factors can be included for different failure modes. The proposed formulas are compared with existing results for partial verification. A case study is presented to demonstrate the applicability. The proposed formulas can serve as a valuable tool for selecting a cost-effective strategy for partial testing.

Suggested Citation

  • Jin, Hui & Rausand, Marvin, 2014. "Reliability of safety-instrumented systems subject to partial testing and common-cause failures," Reliability Engineering and System Safety, Elsevier, vol. 121(C), pages 146-151.
    • Handle: RePEc:eee:reensy:v:121:y:2014:i:c:p:146-151
    DOI: 10.1016/j.ress.2013.08.006
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    References listed on IDEAS

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    1. Florent Brissaud & Anne Barros & Christophe Bérenguer, 2012. "Probability of failure on demand of safety systems: impact of partial test distribution," Journal of Risk and Reliability, , vol. 226(4), pages 426-436, August.
    2. Torres-Echeverría, A.C. & Martorell, S. & Thompson, H.A., 2009. "Modelling and optimization of proof testing policies for safety instrumented systems," Reliability Engineering and System Safety, Elsevier, vol. 94(4), pages 838-854.
    3. 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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    2. 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.
    3. Chuan Wang & Yupeng Liu & Wen Hou & Chao Yu & Guorong Wang & Yuyan Zheng, 2021. "Reliability and availability modeling of Subsea Autonomous High Integrity Pressure Protection System with partial stroke test by Dynamic Bayesian," Journal of Risk and Reliability, , vol. 235(2), pages 268-281, April.
    4. 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.
    5. 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.
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    8. Innal, Fares & Lundteigen, Mary Ann & Liu, Yiliu & Barros, Anne, 2016. "PFDavg generalized formulas for SIS subject to partial and full periodic tests based on multi-phase Markov models," Reliability Engineering and System Safety, Elsevier, vol. 150(C), pages 160-170.
    9. Fort, Ada & Mugnaini, Marco & Vignoli, Valerio & Gaggii, Vittorio & Pieralli, Moreno, 2015. "Fault tolerant design of a field data modular readout architecture for railway applications," Reliability Engineering and System Safety, Elsevier, vol. 142(C), pages 456-462.
    10. Chuan Wang & Jun Gou & Yingcheng Tian & Hao Jin & Chao Yu & Yupeng Liu & Jiajun Ma & Yong Xia, 2022. "Reliability and availability evaluation of subsea high integrity pressure protection system using stochastic Petri net," Journal of Risk and Reliability, , vol. 236(3), pages 508-521, June.
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