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Reliability analysis under epistemic uncertainty

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  • Nannapaneni, Saideep
  • Mahadevan, Sankaran

Abstract

This paper proposes a probabilistic framework to include both aleatory and epistemic uncertainty within model-based reliability estimation of engineering systems for individual limit states. Epistemic uncertainty is considered due to both data and model sources. Sparse point and/or interval data regarding the input random variables leads to uncertainty regarding their distribution types, distribution parameters, and correlations; this statistical uncertainty is included in the reliability analysis through a combination of likelihood-based representation, Bayesian hypothesis testing, and Bayesian model averaging techniques. Model errors, which include numerical solution errors and model form errors, are quantified through Gaussian process models and included in the reliability analysis. The probability integral transform is used to develop an auxiliary variable approach that facilitates a single-level representation of both aleatory and epistemic uncertainty. This strategy results in an efficient single-loop implementation of Monte Carlo simulation (MCS) and FORM/SORM techniques for reliability estimation under both aleatory and epistemic uncertainty. Two engineering examples are used to demonstrate the proposed methodology.

Suggested Citation

  • Nannapaneni, Saideep & Mahadevan, Sankaran, 2016. "Reliability analysis under epistemic uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 155(C), pages 9-20.
    • Handle: RePEc:eee:reensy:v:155:y:2016:i:c:p:9-20
    DOI: 10.1016/j.ress.2016.06.005
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    Cited by:

    1. Tian, Wei & Heo, Yeonsook & de Wilde, Pieter & Li, Zhanyong & Yan, Da & Park, Cheol Soo & Feng, Xiaohang & Augenbroe, Godfried, 2018. "A review of uncertainty analysis in building energy assessment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 93(C), pages 285-301.
    2. Wang, Zhenqiang & Jia, Gaofeng, 2020. "Augmented sample-based approach for efficient evaluation of risk sensitivity with respect to epistemic uncertainty in distribution parameters," Reliability Engineering and System Safety, Elsevier, vol. 197(C).
    3. Izquierdo, J. & Márquez, A. Crespo & Uribetxebarria, J. & Erguido, A., 2020. "On the importance of assessing the operational context impact on maintenance management for life cycle cost of wind energy projects," Renewable Energy, Elsevier, vol. 153(C), pages 1100-1110.
    4. Scheu, Matti Niclas & Kolios, Athanasios & Fischer, Tim & Brennan, Feargal, 2017. "Influence of statistical uncertainty of component reliability estimations on offshore wind farm availability," Reliability Engineering and System Safety, Elsevier, vol. 168(C), pages 28-39.
    5. Xu, Jintao & Gui, Maolei & Ding, Rui & Dai, Tao & Zheng, Mengyan & Men, Xinhong & Meng, Fanpeng & Yu, Tao & Sui, Yang, 2023. "A new approach for dynamic reliability analysis of reactor protection system for HPR1000," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    6. Rocchetta, Roberto & Crespo, Luis G., 2021. "A scenario optimization approach to reliability-based and risk-based design: Soft-constrained modulation of failure probability bounds," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    7. Chang, Qi & Zhou, Changcong & Wei, Pengfei & Zhang, Yishang & Yue, Zhufeng, 2021. "A new non-probabilistic time-dependent reliability model for mechanisms with interval uncertainties," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    8. Morales-Torres, Adrián & Escuder-Bueno, Ignacio & Serrano-Lombillo, Armando & Castillo Rodríguez, Jesica T., 2019. "Dealing with epistemic uncertainty in risk-informed decision making for dam safety management," Reliability Engineering and System Safety, Elsevier, vol. 191(C).
    9. Li, Meng & Sadoughi, Mohammadkazem & Hu, Zhen & Hu, Chao, 2020. "A hybrid Gaussian process model for system reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 197(C).
    10. Izquierdo, J. & Crespo Márquez, A. & Uribetxebarria, J., 2019. "Dynamic artificial neural network-based reliability considering operational context of assets," Reliability Engineering and System Safety, Elsevier, vol. 188(C), pages 483-493.
    11. Tabandeh, Armin & Sharma, Neetesh & Gardoni, Paolo, 2022. "Uncertainty propagation in risk and resilience analysis of hierarchical systems," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
    12. Jung, Yongsu & Jo, Hwisang & Choo, Jeonghwan & Lee, Ikjin, 2022. "Statistical model calibration and design optimization under aleatory and epistemic uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    13. Song, Yufei & Mi, Jinhua & Cheng, Yuhua & Bai, Libing & Chen, Kai, 2020. "A dependency bounds analysis method for reliability assessment of complex system with hybrid uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    14. Wang, Zhonglai & Liu, Jing & Yu, Shui, 2020. "Time-variant reliability prediction for dynamic systems using partial information," Reliability Engineering and System Safety, Elsevier, vol. 195(C).
    15. Cui, L.X. & Du, Yi-Mu & Sun, C.P., 2023. "On system reliability for time-varying structure," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    16. Mi, Jinhua & Lu, Ning & Li, Yan-Feng & Huang, Hong-Zhong & Bai, Libing, 2022. "An evidential network-based hierarchical method for system reliability analysis with common cause failures and mixed uncertainties," Reliability Engineering and System Safety, Elsevier, vol. 220(C).
    17. Nannapaneni, Saideep & Mahadevan, Sankaran, 2020. "Probability-space surrogate modeling for fast multidisciplinary optimization under uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 198(C).
    18. Tohme, Tony & Vanslette, Kevin & Youcef-Toumi, Kamal, 2020. "A generalized Bayesian approach to model calibration," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    19. Gaspar, B. & Teixeira, A.P. & Guedes Soares, C., 2017. "Adaptive surrogate model with active refinement combining Kriging and a trust region method," Reliability Engineering and System Safety, Elsevier, vol. 165(C), pages 277-291.

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