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Global sensitivity analysis for model with random inputs characterized by probability-box

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Listed:
  • Jingwen Song
  • Zhenzhou Lu
  • Pengfei Wei
  • Yanping Wang

Abstract

Global sensitivity analysis techniques for computational models with precise random inputs have been studied widely in the past few decades. However, in real engineering application, due to the lack of information, the distributions of input variables cannot be specified uniquely, and other models such as probability-box ( p -box) need to be introduced to characterize the uncertainty of model inputs. Based on the classical variance-based indices and global reliability sensitivity analysis indices, we develop the corresponding sensitivity indices for the p -box type of uncertainty so as to measure the relative importance of each input and propose an efficient computational procedure called extended Monte Carlo simulation, to compute the developed sensitivity indices. The developed sensitivity indices are well interpreted, and the extended Monte Carlo simulation procedure is efficient as the computational cost is the same with the classical Monte Carlo estimators for Sobol’s indices. Two numerical test examples and two engineering applications are introduced for illustrating the developed sensitivity indices and demonstrating the efficiency and effectiveness of the extended Monte Carlo simulation procedure.

Suggested Citation

  • Jingwen Song & Zhenzhou Lu & Pengfei Wei & Yanping Wang, 2015. "Global sensitivity analysis for model with random inputs characterized by probability-box," Journal of Risk and Reliability, , vol. 229(3), pages 237-253, June.
    • Handle: RePEc:sae:risrel:v:229:y:2015:i:3:p:237-253
    DOI: 10.1177/1748006X15578571
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    References listed on IDEAS

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    1. Allaire, Douglas L. & Willcox, Karen E., 2012. "A variance-based sensitivity index function for factor prioritization," Reliability Engineering and System Safety, Elsevier, vol. 107(C), pages 107-114.
    2. Helton, J.C. & Johnson, J.D. & Sallaberry, C.J. & Storlie, C.B., 2006. "Survey of sampling-based methods for uncertainty and sensitivity analysis," Reliability Engineering and System Safety, Elsevier, vol. 91(10), pages 1175-1209.
    3. Borgonovo, E., 2007. "A new uncertainty importance measure," Reliability Engineering and System Safety, Elsevier, vol. 92(6), pages 771-784.
    4. F. Owen Hoffman & Jana S. Hammonds, 1994. "Propagation of Uncertainty in Risk Assessments: The Need to Distinguish Between Uncertainty Due to Lack of Knowledge and Uncertainty Due to Variability," Risk Analysis, John Wiley & Sons, vol. 14(5), pages 707-712, October.
    5. Krzykacz-Hausmann, Bernard, 2006. "An approximate sensitivity analysis of results from complex computer models in the presence of epistemic and aleatory uncertainties," Reliability Engineering and System Safety, Elsevier, vol. 91(10), pages 1210-1218.
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    Cited by:

    1. Wei, Pengfei & Song, Jingwen & Lu, Zhenzhou & Yue, Zhufeng, 2016. "Time-dependent reliability sensitivity analysis of motion mechanisms," Reliability Engineering and System Safety, Elsevier, vol. 149(C), pages 107-120.
    2. Cheng, Jin & Wang, Jian & Wu, Xuezhou & Wang, Shuo, 2019. "An improved polynomial-based nonlinear variable importance measure and its application to degradation assessment for high-voltage transformer under imbalance data," Reliability Engineering and System Safety, Elsevier, vol. 185(C), pages 175-191.

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