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Uncertainty propagation and sensitivity analysis in system reliability assessment via unscented transformation

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  • Rocco Sanseverino, Claudio M.
  • Ramirez-Marquez, José Emmanuel

Abstract

The reliability of a system, notwithstanding it intended function, can be significantly affected by the uncertainty in the reliability estimate of the components that define the system. This paper implements the Unscented Transformation to quantify the effects of the uncertainty of component reliability through two approaches. The first approach is based on the concept of uncertainty propagation, which is the assessment of the effect that the variability of the component reliabilities produces on the variance of the system reliability. This assessment based on UT has been previously considered in the literature but only for system represented through series/parallel configuration. In this paper the assessment is extended to systems whose reliability cannot be represented through analytical expressions and require, for example, Monte Carlo Simulation.

Suggested Citation

  • Rocco Sanseverino, Claudio M. & Ramirez-Marquez, José Emmanuel, 2014. "Uncertainty propagation and sensitivity analysis in system reliability assessment via unscented transformation," Reliability Engineering and System Safety, Elsevier, vol. 132(C), pages 176-185.
    • Handle: RePEc:eee:reensy:v:132:y:2014:i:c:p:176-185
    DOI: 10.1016/j.ress.2014.07.024
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    References listed on IDEAS

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    1. Li, Junye, 2013. "An unscented Kalman smoother for volatility extraction: Evidence from stock prices and options," Computational Statistics & Data Analysis, Elsevier, vol. 58(C), pages 15-26.
    2. Fan, Jiajie & Yung, Kam-Chuen & Pecht, Michael, 2014. "Prognostics of lumen maintenance for High power white light emitting diodes using a nonlinear filter-based approach," Reliability Engineering and System Safety, Elsevier, vol. 123(C), pages 63-72.
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    Cited by:

    1. Yeh, Wei-Chang, 2021. "A quick BAT for evaluating the reliability of binary-state networks," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    2. Cheng, Lei & Lu, Zhenzhou & Zhang, Leigang, 2015. "Application of Rejection Sampling based methodology to variance based parametric sensitivity analysis," Reliability Engineering and System Safety, Elsevier, vol. 142(C), pages 9-18.
    3. de Araujo, Matheus Soares & da Silva, Leandro Dias & Sobrinho, Ã lvaro & Cunha, Paulo & Montecchi, Leonardo, 2022. "Reliability analysis of multi-parameter monitoring systems for Intensive Care Units," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    4. Zhang, Long-Wen & Dang, Chao & Zhao, Yan-Gang, 2023. "An efficient method for accessing structural reliability indexes via power transformation family," Reliability Engineering and System Safety, Elsevier, vol. 233(C).
    5. Yeh, Wei-Chang, 2023. "Novel recursive inclusion-exclusion technology based on BAT and MPs for heterogeneous-arc binary-state network reliability problems," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    6. Rocco S., Claudio M. & Emmanuel Ramirez-Marquez, José, 2015. "Assessment of the transition-rates importance of Markovian systems at steady state using the unscented transformation," Reliability Engineering and System Safety, Elsevier, vol. 142(C), pages 212-220.
    7. Cao, Ran & Coit, David W. & Hou, Wei & Yang, Yushu, 2020. "Game theory based solution selection for multi-objective redundancy allocation in interval-valued problem parameters," Reliability Engineering and System Safety, Elsevier, vol. 199(C).
    8. Yeh, Wei-Chang, 2023. "QB-II for evaluating the reliability of binary-state networks," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    9. Medeiros, C.P. & Alencar, M.H. & de Almeida, A.T., 2017. "Multidimensional risk evaluation of natural gas pipelines based on a multicriteria decision model using visualization tools and statistical tests for global sensitivity analysis," Reliability Engineering and System Safety, Elsevier, vol. 165(C), pages 268-276.
    10. Xiang Peng & Xiaoqing Xu & Jiquan Li & Shaofei Jiang, 2021. "A Sampling-Based Sensitivity Analysis Method Considering the Uncertainties of Input Variables and Their Distribution Parameters," Mathematics, MDPI, vol. 9(10), pages 1-18, May.

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