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Integrated importance of multi-state fault tree based on multi-state multi-valued decision diagram

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  • Shumin Li
  • Shubin Si
  • Liudong Xing
  • Shudong Sun

Abstract

Integrated importance measures have been developed to study the effect of component state probabilities and state transition rates on the multi-state system performance, identifying the weakest component to facilitate the system maintenance and optimization activities. This article proposes an analytical method based on multi-state multi-valued decision diagram for computing the integrated importance measure values. Following a discussion of decomposition and physical meaning of integrated importance measures, the modeling method of multi-state multi-valued decision diagram based on multi-state fault tree analysis is introduced. A five-step integrated importance measure analysis approach based on multi-state multi-valued decision diagram is then proposed. Two case studies are implemented to demonstrate the presented methods. Complexity analysis shows that the multi-state multi-valued decision diagram–based method is more computationally efficient than the existing method using Markov–Bayesian networks.

Suggested Citation

  • Shumin Li & Shubin Si & Liudong Xing & Shudong Sun, 2014. "Integrated importance of multi-state fault tree based on multi-state multi-valued decision diagram," Journal of Risk and Reliability, , vol. 228(2), pages 200-208, April.
    • Handle: RePEc:sae:risrel:v:228:y:2014:i:2:p:200-208
    DOI: 10.1177/1748006X13508758
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    References listed on IDEAS

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    1. Ramirez-Marquez, Jose Emmanuel & Coit, David W., 2007. "Multi-state component criticality analysis for reliability improvement in multi-state systems," Reliability Engineering and System Safety, Elsevier, vol. 92(12), pages 1608-1619.
    2. Do Van, Phuc & Barros, Anne & Bérenguer, Christophe, 2008. "Reliability importance analysis of Markovian systems at steady state using perturbation analysis," Reliability Engineering and System Safety, Elsevier, vol. 93(11), pages 1605-1615.
    3. Do Van, Phuc & Barros, Anne & Bérenguer, Christophe, 2010. "From differential to difference importance measures for Markov reliability models," European Journal of Operational Research, Elsevier, vol. 204(3), pages 513-521, August.
    4. Borgonovo, E., 2007. "Differential, criticality and Birnbaum importance measures: An application to basic event, groups and SSCs in event trees and binary decision diagrams," Reliability Engineering and System Safety, Elsevier, vol. 92(10), pages 1458-1467.
    5. Ramirez-Marquez, Jose E. & Rocco, Claudio M. & Gebre, Bethel A. & Coit, David W. & Tortorella, Michael, 2006. "New insights on multi-state component criticality and importance," Reliability Engineering and System Safety, Elsevier, vol. 91(8), pages 894-904.
    6. Bent Natvig, 2011. "Measures of Component Importance in Nonrepairable and Repairable Multistate Strongly Coherent Systems," Methodology and Computing in Applied Probability, Springer, vol. 13(3), pages 523-547, September.
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    Cited by:

    1. Ruiz-Castro, Juan Eloy, 2016. "Markov counting and reward processes for analysing the performance of a complex system subject to random inspections," Reliability Engineering and System Safety, Elsevier, vol. 145(C), pages 155-168.

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