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Consistent mapping of common cause failure rates and alpha factors

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  • Vaurio, Jussi K.

Abstract

The problem addressed is how to combine event experience data from multiple source plants to estimate common cause failure (CCF) rates for a target plant. Alternative models are considered for transforming CCF parameters from systems with different numbers of similar components to obtain CCF-rates for a specific group of components. Two sets of rules are reviewed and compared for transforming rates and assessment uncertainties from larger to smaller systems, i.e. mapping down. Mapping down equations are presented also for the alpha-factors and for the variances of CCF rates. Consistent rules are developed for mapping up CCF-rates and uncertainties from smaller to larger systems. These mapping up rules are not limited to a binomial CCF model. It is shown how consistency requirements set certain limits to possible parametric values. Empirical alpha factors are used to estimate robust mapping parameters, and mapping up equations are derived for alpha factors as well. An assessment uncertainty procedure is presented for treating incomplete or vague information when estimating CCF-rates. Numerical studies illustrate mapping rules and procedures. Recommendations are made for practical applications.

Suggested Citation

  • Vaurio, Jussi K., 2007. "Consistent mapping of common cause failure rates and alpha factors," Reliability Engineering and System Safety, Elsevier, vol. 92(5), pages 628-645.
    • Handle: RePEc:eee:reensy:v:92:y:2007:i:5:p:628-645
    DOI: 10.1016/j.ress.2006.02.008
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    References listed on IDEAS

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    1. Jussi K. Vaurio, 1994. "Estimation of Common Cause Failure Rates Based on Uncertain Event Data," Risk Analysis, John Wiley & Sons, vol. 14(4), pages 383-387, August.
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    Cited by:

    1. 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.
    2. KanÄ ev, DuÅ¡ko & ÄŒepin, Marko, 2012. "A new method for explicit modelling of single failure event within different common cause failure groups," Reliability Engineering and System Safety, Elsevier, vol. 103(C), pages 84-93.
    3. Nicola Pedroni & Enrico Zio & Alberto Pasanisi & Mathieu Couplet, 2017. "A critical discussion and practical recommendations on some issues relevant to the non-probabilistic treatment of uncertainty in engineering risk assessment," Post-Print hal-01652230, HAL.
    4. Atwood, Corwin L., 2013. "Consequences of mapping data or parameters in Bayesian common-cause analysis," Reliability Engineering and System Safety, Elsevier, vol. 118(C), pages 118-131.
    5. Mechri, Walid & Simon, Christophe & BenOthman, Kamel, 2015. "Switching Markov chains for a holistic modeling of SIS unavailability," Reliability Engineering and System Safety, Elsevier, vol. 133(C), pages 212-222.
    6. W Mechri & C Simon & K Ben Othman, 2011. "Uncertainty analysis of common cause failure in safety instrumented systems," Journal of Risk and Reliability, , vol. 225(4), pages 450-460, December.
    7. Nicola Pedroni & Enrico Zio, 2013. "Uncertainty Analysis in Fault Tree Models with Dependent Basic Events," Risk Analysis, John Wiley & Sons, vol. 33(6), pages 1146-1173, June.
    8. Li, Chun-yang & Chen, Xun & Yi, Xiao-shan & Tao, Jun-yong, 2010. "Heterogeneous redundancy optimization for multi-state series–parallel systems subject to common cause failures," Reliability Engineering and System Safety, Elsevier, vol. 95(3), pages 202-207.
    9. Hoepfer, V.M. & Saleh, J.H. & Marais, K.B., 2009. "On the value of redundancy subject to common-cause failures: Toward the resolution of an on-going debate," Reliability Engineering and System Safety, Elsevier, vol. 94(12), pages 1904-1916.
    10. Nicola Pedroni & Enrico Zio & Alberto Pasanisi & Mathieu Couplet, 2017. "A Critical Discussion and Practical Recommendations on Some Issues Relevant to the Nonprobabilistic Treatment of Uncertainty in Engineering Risk Assessment," Risk Analysis, John Wiley & Sons, vol. 37(7), pages 1315-1340, July.

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