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A new uncertainty propagation method for problems with parameterized probability-boxes

Author

Listed:
  • Liu, H.B.
  • Jiang, C.
  • Jia, X.Y.
  • Long, X.Y.
  • Zhang, Z.
  • Guan, F.J.

Abstract

This paper proposes a new uncertainty propagation method for problems with parameterized probability-boxes (p-boxes), which could efficiently compute the probability bounds of system response function. In practical engineering, these probability bounds are often very important for reliability analysis or risk assessment of a structure or system. First, based on the univariate dimension reduction method (UDRM), an optimized UDRM (OUDRM) is presented to calculate the bounds on statistical moments of response function. Then, utilizing the bounds on moments, a family of Johnson distributions fitting to the distribution function of response can be acquired using the moment matching method. Finally, by using an optimization approach based on percentiles, the probability bounds of the response function can be successfully obtained. Four numerical examples are investigated to demonstrate the effectiveness of the proposed method.

Suggested Citation

  • Liu, H.B. & Jiang, C. & Jia, X.Y. & Long, X.Y. & Zhang, Z. & Guan, F.J., 2018. "A new uncertainty propagation method for problems with parameterized probability-boxes," Reliability Engineering and System Safety, Elsevier, vol. 172(C), pages 64-73.
    • Handle: RePEc:eee:reensy:v:172:y:2018:i:c:p:64-73
    DOI: 10.1016/j.ress.2017.12.004
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    References listed on IDEAS

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    1. Zaman, Kais & Rangavajhala, Sirisha & McDonald, Mark P. & Mahadevan, Sankaran, 2011. "A probabilistic approach for representation of interval uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 96(1), pages 117-130.
    2. Eldred, M.S. & Swiler, L.P. & Tang, G., 2011. "Mixed aleatory-epistemic uncertainty quantification with stochastic expansions and optimization-based interval estimation," Reliability Engineering and System Safety, Elsevier, vol. 96(9), pages 1092-1113.
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    Cited by:

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    2. Sun, Xiaocong & Bao, Minglei & Ding, Yi & Hui, Hengyu & Song, Yonghua & Zheng, Chenghang & Gao, Xiang, 2024. "Modeling and evaluation of probabilistic carbon emission flow for power systems considering load and renewable energy uncertainties," Energy, Elsevier, vol. 296(C).
    3. Salomon, Julian & Winnewisser, Niklas & Wei, Pengfei & Broggi, Matteo & Beer, Michael, 2021. "Efficient reliability analysis of complex systems in consideration of imprecision," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
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    5. Hongjie Tang & Shicheng Zhang & Jinhui Li & Lingwei Kong & Baoqiang Zhang & Fei Xing & Huageng Luo, 2023. "Imprecise P-Box Sensitivity Analysis of an Aero-Engine Combustor Performance Simulation Model Considering Correlated Variables," Energies, MDPI, vol. 16(5), pages 1-22, March.

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