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Modeling safety instrumented systems with MooN voting architectures addressing system reconfiguration for testing

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  • Torres-Echeverría, A.C.
  • Martorell, S.
  • Thompson, H.A.

Abstract

This paper addresses the modeling of probability of dangerous failure on demand and spurious trip rate of safety instrumented systems that include MooN voting redundancies in their architecture. MooN systems are a special case of k-out-of-n systems. The first part of the article is devoted to the development of a time-dependent probability of dangerous failure on demand model with capability of handling MooN systems. The model is able to model explicitly common cause failure and diagnostic coverage, as well as different test frequencies and strategies. It includes quantification of both detected and undetected failures, and puts emphasis on the quantification of common cause failure to the system probability of dangerous failure on demand as an additional component. In order to be able to accommodate changes in testing strategies, special treatment is devoted to the analysis of system reconfiguration (including common cause failure) during test of one of its components, what is then included in the model. Another model for spurious trip rate is also analyzed and extended under the same methodology in order to empower it with similar capabilities. These two models are powerful enough, but at the same time simple, to be suitable for handling of dependability measures in multi-objective optimization of both system design and test strategies for safety instrumented systems. The level of modeling detail considered permits compliance with the requirements of the standard IEC 61508. The two models are applied to brief case studies to demonstrate their effectiveness. The results obtained demonstrated that the first model is adequate to quantify time-dependent PFD of MooN systems during different system states (i.e. full operation, test and repair) and different MooN configurations, which values are averaged to obtain the PFDavg. Also, it was demonstrated that the second model is adequate to quantify STR including spurious trips induced by internal component failure and by test itself. Both models were tested for different architectures with 1≤N≤5 and 2≤M≤5 subject to uniform staggered test. The results obtained also showed the effects that modifying M and N has on both PFDavg and STR, and also demonstrated the conflicting nature of these two measures with respect to one another.

Suggested Citation

  • Torres-Echeverría, A.C. & Martorell, S. & Thompson, H.A., 2011. "Modeling safety instrumented systems with MooN voting architectures addressing system reconfiguration for testing," Reliability Engineering and System Safety, Elsevier, vol. 96(5), pages 545-563.
    • Handle: RePEc:eee:reensy:v:96:y:2011:i:5:p:545-563
    DOI: 10.1016/j.ress.2010.12.003
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    References listed on IDEAS

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    1. Torres-Echeverría, A.C. & Martorell, S. & Thompson, H.A., 2009. "Design optimization of a safety-instrumented system based on RAMS+C addressing IEC 61508 requirements and diverse redundancy," Reliability Engineering and System Safety, Elsevier, vol. 94(2), pages 162-179.
    2. Dutuit, Y. & Innal, F. & Rauzy, A. & Signoret, J.-P., 2008. "Probabilistic assessments in relationship with safety integrity levels by using Fault Trees," Reliability Engineering and System Safety, Elsevier, vol. 93(12), pages 1867-1876.
    3. Oliveira, Luiz Fernando & Abramovitch, Rafael Nelson, 2010. "Extension of ISA TR84.00.02 PFD equations to KooN architectures," Reliability Engineering and System Safety, Elsevier, vol. 95(7), pages 707-715.
    4. 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.
    5. A. C. Torres-Echeverria & H. A. Thompson, 2007. "Multi-objective genetic algorithm for optimization of system safety and reliability based on IEC 61508 requirements: A practical approach," Journal of Risk and Reliability, , vol. 221(3), pages 193-205, September.
    6. Lu, Lixuan & Lewis, Gregory, 2008. "Configuration determination for k-out-of-n partially redundant systems," Reliability Engineering and System Safety, Elsevier, vol. 93(11), pages 1594-1604.
    7. Lu, Lixuan & Jiang, Jin, 2007. "Analysis of on-line maintenance strategies for k-out-of-n standby safety systems," Reliability Engineering and System Safety, Elsevier, vol. 92(2), pages 144-155.
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    7. Aboalkhair, Ahmad M. & Coolen, Frank P.A. & MacPhee, Iain M., 2014. "Nonparametric predictive inference for reliability of a k-out-of-m:G system with multiple component types," Reliability Engineering and System Safety, Elsevier, vol. 131(C), pages 298-304.
    8. Torres-Echeverría, A.C. & Martorell, S. & Thompson, H.A., 2012. "Multi-objective optimization of design and testing of safety instrumented systems with MooN voting architectures using a genetic algorithm," Reliability Engineering and System Safety, Elsevier, vol. 106(C), pages 45-60.
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    11. 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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