IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v234y2023ics0951832023001072.html
   My bibliography  Save this article

Robustness evaluation of the reliability of penstocks combining line sampling and neural networks

Author

Listed:
  • Ajenjo, Antoine
  • Ardillon, Emmanuel
  • Chabridon, Vincent
  • Cogan, Scott
  • Sadoulet-Reboul, Emeline

Abstract

This work aims at proposing a methodology to analyze the robustness of reliability analysis under epistemic uncertainty. Motivated by a real industrial problem, the main contribution relies on the coupling of the info-gap framework with advanced failure probability estimators for robustness evaluations on the reliability assessment of penstocks. In order to improve the induced optimization searches, three original adapted line sampling procedures are proposed in order to address the complex limit-state function on which the failure probability depends. The proposed algorithms are proven to be well suited for the search of the multiple roots involved in the line sampling technique. Then, a classification and a regression artificial neural network are combined for predicting the roots in an aleatory and epistemic augmented space in order to reduce the computational time engendered by robustness evaluations.

Suggested Citation

  • Ajenjo, Antoine & Ardillon, Emmanuel & Chabridon, Vincent & Cogan, Scott & Sadoulet-Reboul, Emeline, 2023. "Robustness evaluation of the reliability of penstocks combining line sampling and neural networks," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    • Handle: RePEc:eee:reensy:v:234:y:2023:i:c:s0951832023001072
    DOI: 10.1016/j.ress.2023.109192
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832023001072
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ress.2023.109192?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Jim W. Hall & Robert J. Lempert & Klaus Keller & Andrew Hackbarth & Christophe Mijere & David J. McInerney, 2012. "Robust Climate Policies Under Uncertainty: A Comparison of Robust Decision Making and Info‐Gap Methods," Risk Analysis, John Wiley & Sons, vol. 32(10), pages 1657-1672, October.
    2. Sarazin, Gabriel & Morio, Jérôme & Lagnoux, Agnès & Balesdent, Mathieu & Brevault, Loïc, 2021. "Reliability-oriented sensitivity analysis in presence of data-driven epistemic uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    3. Beck, André T. & Rodrigues da Silva, Lucas A. & Miguel, Leandro F.F., 2023. "The latent failure probability: A conceptual basis for robust, reliability-based and risk-based design optimization," Reliability Engineering and System Safety, Elsevier, vol. 233(C).
    4. Meng, Zeng & Zhao, Jingyu & Chen, Guohai & Yang, Dixiong, 2022. "Hybrid uncertainty propagation and reliability analysis using direct probability integral method and exponential convex model," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
    5. McKeand, Austin M. & Gorguluarslan, Recep M. & Choi, Seung-Kyum, 2021. "Stochastic analysis and validation under aleatory and epistemic uncertainties," Reliability Engineering and System Safety, Elsevier, vol. 205(C).
    6. 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).
    7. Valdebenito, Marcos A. & Wei, Pengfei & Song, Jingwen & Beer, Michael & Broggi, Matteo, 2021. "Failure probability estimation of a class of series systems by multidomain Line Sampling," Reliability Engineering and System Safety, Elsevier, vol. 213(C).
    8. Donald R. Jones & Joaquim R. R. A. Martins, 2021. "The DIRECT algorithm: 25 years Later," Journal of Global Optimization, Springer, vol. 79(3), pages 521-566, March.
    9. Chabridon, Vincent & Balesdent, Mathieu & Bourinet, Jean-Marc & Morio, Jérôme & Gayton, Nicolas, 2018. "Reliability-based sensitivity estimators of rare event probability in the presence of distribution parameter uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 178(C), pages 164-178.
    10. Flage, Roger & Aven, Terje & Berner, Christine L., 2018. "A comparison between a probability bounds analysis and a subjective probability approach to express epistemic uncertainties in a risk assessment context – A simple illustrative example," Reliability Engineering and System Safety, Elsevier, vol. 169(C), pages 1-10.
    11. Zio, E. & Pedroni, N., 2010. "An optimized Line Sampling method for the estimation of the failure probability of nuclear passive systems," Reliability Engineering and System Safety, Elsevier, vol. 95(12), pages 1300-1313.
    12. Valdebenito, M.A. & Jensen, H.A. & Hernández, H.B. & Mehrez, L., 2018. "Sensitivity estimation of failure probability applying line sampling," Reliability Engineering and System Safety, Elsevier, vol. 171(C), pages 99-111.
    13. Pan, Yan & Jing, Yunteng & Wu, Tonghai & Kong, Xiangxing, 2021. "An Integrated Data and Knowledge Model Addressing Aleatory and Epistemic Uncertainty for Oil Condition Monitoring," Reliability Engineering and System Safety, Elsevier, vol. 210(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Li, Shen & Kim, Do Kyun & Benson, Simon, 2021. "A probabilistic approach to assess the computational uncertainty of ultimate strength of hull girders," Reliability Engineering and System Safety, Elsevier, vol. 213(C).
    2. Xiao, Tianli & Park, Chanseok & Lin, Chenglong & Ouyang, Linhan & Ma, Yizhong, 2023. "Hybrid reliability analysis with incomplete interval data based on adaptive Kriging," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    3. Dang, Chao & Valdebenito, Marcos A. & Wei, Pengfei & Song, Jingwen & Beer, Michael, 2024. "Bayesian active learning line sampling with log-normal process for rare-event probability estimation," Reliability Engineering and System Safety, Elsevier, vol. 246(C).
    4. Yuan, Xiukai & Zheng, Weiming & Zhao, Chaofan & Valdebenito, Marcos A. & Faes, Matthias G.R. & Dong, Yiwei, 2024. "Line sampling for time-variant failure probability estimation using an adaptive combination approach," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    5. Straub, Daniel & Ehre, Max & Papaioannou, Iason, 2022. "Decision-theoretic reliability sensitivity," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
    6. Meng, Zeng & Zhao, Jingyu & Chen, Guohai & Yang, Dixiong, 2022. "Hybrid uncertainty propagation and reliability analysis using direct probability integral method and exponential convex model," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
    7. S. Cucurachi & E. Borgonovo & R. Heijungs, 2016. "A Protocol for the Global Sensitivity Analysis of Impact Assessment Models in Life Cycle Assessment," Risk Analysis, John Wiley & Sons, vol. 36(2), pages 357-377, February.
    8. Liu, Zhichen & Li, Ying & Zhang, Zhaoyi & Yu, Wenbo, 2022. "A new evacuation accessibility analysis approach based on spatial information," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    9. Zio, E. & Pedroni, N., 2012. "Monte Carlo simulation-based sensitivity analysis of the model of a thermal–hydraulic passive system," Reliability Engineering and System Safety, Elsevier, vol. 107(C), pages 90-106.
    10. William Mobley & Kayode O. Atoba & Wesley E. Highfield, 2020. "Uncertainty in Flood Mitigation Practices: Assessing the Economic Benefits of Property Acquisition and Elevation in Flood-Prone Communities," Sustainability, MDPI, vol. 12(5), pages 1-14, March.
    11. Li, Weijun & Sun, Qiqi & Zhang, Jiwang & Zhang, Laibin, 2024. "Quantitative risk assessment of industrial hot work using Adaptive Bow Tie and Petri Nets," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    12. Zheng, Shuwen & Wang, Chong & Zio, Enrico & Liu, Jie, 2024. "Fault detection in complex mechatronic systems by a hierarchical graph convolution attention network based on causal paths," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    13. Zaitseva, Elena & Levashenko, Vitaly & Rabcan, Jan, 2023. "A new method for analysis of Multi-State systems based on Multi-valued decision diagram under epistemic uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    14. Jiang, Chen & Qiu, Haobo & Yang, Zan & Chen, Liming & Gao, Liang & Li, Peigen, 2019. "A general failure-pursuing sampling framework for surrogate-based reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 183(C), pages 47-59.
    15. Ben-Haim, Yakov, 2021. "Feedback for energy conservation: An info-gap approach," Energy, Elsevier, vol. 223(C).
    16. Yuan, Xiukai & Qian, Yugeng & Chen, Jingqiang & Faes, Matthias G.R. & Valdebenito, Marcos A. & Beer, Michael, 2023. "Global failure probability function estimation based on an adaptive strategy and combination algorithm," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    17. Shang, Yue & Nogal, Maria & Teixeira, Rui & Wolfert, A.R. (Rogier) M., 2024. "Extreme-oriented sensitivity analysis using sparse polynomial chaos expansion. Application to train–track–bridge systems," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    18. Marrel, Amandine & Chabridon, Vincent, 2021. "Statistical developments for target and conditional sensitivity analysis: Application on safety studies for nuclear reactor," Reliability Engineering and System Safety, Elsevier, vol. 214(C).
    19. Huirong Yu, 2022. "A multi-scale approach to mapping conservation priorities for rural China based on landscape context," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 24(9), pages 10803-10828, September.
    20. Paredes-Vergara, Matías & Palma-Behnke, Rodrigo & Haas, Jannik, 2024. "Characterizing decision making under deep uncertainty for model-based energy transitions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 192(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:reensy:v:234:y:2023:i:c:s0951832023001072. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/reliability-engineering-and-system-safety .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.