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Uncertainties and quantification of common cause failure rates and probabilities for system analyses

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  • Vaurio, Jussi K.

Abstract

Simultaneous failures of multiple components due to common causes at random times are modelled by constant multiple-failure rates. A procedure is described for quantification of common cause failure (CCF) basic event probabilities for system models using plant-specific and multiple-plant failure-event data. Methodology is presented for estimating CCF-rates from event data contaminated with assessment uncertainties. Generalised impact vectors determine the moments for the rates of individual systems or plants. These moments determine the effective numbers of events and observation times to be input to a Bayesian formalism to obtain plant-specific posterior CCF-rates. The rates are used to determine plant-specific common cause event probabilities for the basic events of explicit fault tree models depending on test intervals, test schedules and repair policies. Three methods are presented to determine these probabilities such that the correct time-average system unavailability can be obtained with single fault tree quantification. Recommended numerical values are given and examples illustrate different aspects of the methodology.

Suggested Citation

  • Vaurio, Jussi K., 2005. "Uncertainties and quantification of common cause failure rates and probabilities for system analyses," Reliability Engineering and System Safety, Elsevier, vol. 90(2), pages 186-195.
    • Handle: RePEc:eee:reensy:v:90:y:2005:i:2:p:186-195
    DOI: 10.1016/j.ress.2004.10.014
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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. Abou, Seraphin C., 2010. "Performance assessment of multi-state systems with critical failure modes: Application to the flotation metallic arsenic circuit," Reliability Engineering and System Safety, Elsevier, vol. 95(6), pages 614-622.
    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. Quigley, John & Bedford, Tim & Walls, Lesley, 2007. "Estimating rate of occurrence of rare events with empirical bayes: A railway application," Reliability Engineering and System Safety, Elsevier, vol. 92(5), pages 619-627.
    4. Zhou, Taotao & Droguett, Enrique López & Modarres, Mohammad, 2020. "A common cause failure model for components under age-related degradation," Reliability Engineering and System Safety, Elsevier, vol. 195(C).
    5. L Xing & P Boddu & Y Sun & W Wang, 2010. "Reliability analysis of static and dynamic fault-tolerant systems subject to probabilistic common-cause failures," Journal of Risk and Reliability, , vol. 224(1), pages 43-53, March.
    6. Levitin, Gregory & Xing, Liudong & Amari, Suprasad V. & Dai, Yuanshun, 2013. "Reliability of non-repairable phased-mission systems with propagated failures," Reliability Engineering and System Safety, Elsevier, vol. 119(C), pages 218-228.
    7. Taghipour, Sharareh & Banjevic, Dragan & Jardine, Andrew K.S., 2010. "Periodic inspection optimization model for a complex repairable system," Reliability Engineering and System Safety, Elsevier, vol. 95(9), pages 944-952.
    8. Ramirez-Marquez, Jose E. & Coit, David W., 2007. "Optimization of system reliability in the presence of common cause failures," Reliability Engineering and System Safety, Elsevier, vol. 92(10), pages 1421-1434.
    9. Min Zhang & Zhijian Zhang & Ali Mosleh & Sijuan Chen, 2017. "Common cause failure model updating for risk monitoring in nuclear power plants based on alpha factor model," Journal of Risk and Reliability, , vol. 231(3), pages 209-220, June.
    10. Gianpaolo Di Bona & Antonio Forcina & Domenico Falcone & Luca Silvestri, 2020. "Critical Risks Method (CRM): A New Safety Allocation Approach for a Critical Infrastructure," Sustainability, MDPI, vol. 12(12), pages 1-19, June.
    11. 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.
    12. Cui, Lirong & Li, Haijun, 2007. "Analytical method for reliability and MTTF assessment of coherent systems with dependent components," Reliability Engineering and System Safety, Elsevier, vol. 92(3), pages 300-307.
    13. Xing, Liudong & Meshkat, Leila & Donohue, Susan K., 2007. "Reliability analysis of hierarchical computer-based systems subject to common-cause failures," Reliability Engineering and System Safety, Elsevier, vol. 92(3), pages 351-359.
    14. Soga, Shota & Higo, Eishiro & Miura, Hiromichi, 2021. "A systematic approach to estimate an inter-unit common-cause failure probability," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    15. Quigley, John & Walls, Lesley, 2011. "Mixing Bayes and empirical Bayes inference to anticipate the realization of engineering concerns about variant system designs," Reliability Engineering and System Safety, Elsevier, vol. 96(8), pages 933-941.

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