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A Robust Measure of Uncertainty Importance for Use in Fault Tree System Analysis

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  • Ronald L. Iman
  • Stephen C. Hora

Abstract

The analysis of probabilistic fault trees often involves the investigation of events that contribute both to the frequency of the top event and to the uncertainty in this frequency. This paper provides a discussion of three measures of the contribution of an event to the total uncertainty in the top event. These measures are known as uncertainty importance measures. Two of these measures are new developments. Each of the measures is shown to have unique advantages and disadvantages. The three measures are based on, respectively, the expected reduction in the variance of the top‐event frequency should the uncertainty in an event be resolved, the same measure based on the log frequency, and a measure based on shifts in the quantiles of the distribution of top‐event frequency.

Suggested Citation

  • Ronald L. Iman & Stephen C. Hora, 1990. "A Robust Measure of Uncertainty Importance for Use in Fault Tree System Analysis," Risk Analysis, John Wiley & Sons, vol. 10(3), pages 401-406, September.
    • Handle: RePEc:wly:riskan:v:10:y:1990:i:3:p:401-406
    DOI: 10.1111/j.1539-6924.1990.tb00523.x
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    References listed on IDEAS

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    1. Ronald L. Iman, 1987. "A Matrix‐Based Approach to Uncertainty and Sensitivity Analysis for Fault Trees," Risk Analysis, John Wiley & Sons, vol. 7(1), pages 21-33, March.
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    Cited by:

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    3. Kucherenko, Sergei & Song, Shufang & Wang, Lu, 2019. "Quantile based global sensitivity measures," Reliability Engineering and System Safety, Elsevier, vol. 185(C), pages 35-48.
    4. Emanuele Borgonovo, 2006. "Measuring Uncertainty Importance: Investigation and Comparison of Alternative Approaches," Risk Analysis, John Wiley & Sons, vol. 26(5), pages 1349-1361, October.
    5. Borgonovo, Emanuele & Hazen, Gordon B. & Jose, Victor Richmond R. & Plischke, Elmar, 2021. "Probabilistic sensitivity measures as information value," European Journal of Operational Research, Elsevier, vol. 289(2), pages 595-610.
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    8. Ronald L. Iman & Mark E. Johnson & Charles C. Watson, 2005. "Uncertainty Analysis for Computer Model Projections of Hurricane Losses," Risk Analysis, John Wiley & Sons, vol. 25(5), pages 1299-1312, October.
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    16. Xing Liu & Enrico Zio & Emanuele Borgonovo & Elmar Plischke, 2024. "A Systematic Approach of Global Sensitivity Analysis and Its Application to a Model for the Quantification of Resilience of Interconnected Critical Infrastructures," Energies, MDPI, vol. 17(8), pages 1-24, April.
    17. Barry Anderson & Emanuele Borgonovo & Marzio Galeotti & Roberto Roson, 2014. "Uncertainty in Climate Change Modeling: Can Global Sensitivity Analysis Be of Help?," Risk Analysis, John Wiley & Sons, vol. 34(2), pages 271-293, February.
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    19. Takeda, Satoshi & Kitada, Takanori, 2023. "Importance measure evaluation based on sensitivity coefficient for probabilistic risk assessment," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    20. Emanuele Borgonovo & William Castaings & Stefano Tarantola, 2011. "Moment Independent Importance Measures: New Results and Analytical Test Cases," Risk Analysis, John Wiley & Sons, vol. 31(3), pages 404-428, March.
    21. Wu, Zeping & Wang, Wenjie & Wang, Donghui & Zhao, Kun & Zhang, Weihua, 2019. "Global sensitivity analysis using orthogonal augmented radial basis function," Reliability Engineering and System Safety, Elsevier, vol. 185(C), pages 291-302.
    22. Mirko Ginocchi & Ferdinanda Ponci & Antonello Monti, 2021. "Sensitivity Analysis and Power Systems: Can We Bridge the Gap? A Review and a Guide to Getting Started," Energies, MDPI, vol. 14(24), pages 1-59, December.
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    24. Luyi Li & Zhenzhou Lu, 2016. "A new algorithm for importance analysis of the inputs with distribution parameter uncertainty," International Journal of Systems Science, Taylor & Francis Journals, vol. 47(13), pages 3065-3077, October.

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