IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v249y2024ics0951832024003077.html
   My bibliography  Save this article

Probability-informed neural network-driven point-evolution kernel density estimation for time-dependent reliability analysis

Author

Listed:
  • Guo, Hongyuan
  • Zhang, Jiaxin
  • Dong, You
  • Frangopol, Dan M.

Abstract

Engineering structure under erosive agents, time-dependent loads, and material degradation, underscores the necessity of time-dependent reliability analysis (TDRA) for predicting safety within the service life. However, conventional TDRA often faces challenges in efficiency, accuracy, and generality, prompting the need for efficient and accurate TDRA methods. This study introduces a novel probability density function-informed method (PDFM), specifically designed for TDRA of time-dependent systems, known as probability-informed neural network-point-evolution kernel density estimation (PNPE). PNPE, founded on point evolution kernel density estimation (PKDE) and integrating Deep Neural Network (DNN) with the general density evolution equation, uniquely merges machine learning with physical equations. This integration addresses the shortcomings of traditional PDFM, enhancing efficiency in TDRA without requiring an extensive number of representative points for improved accuracy. PNPE is validated through four benchmark cases: a simple numerical case, two scenarios involving corroded steel beams, a hydrodynamic turbine blade, and the seismic performance of a multi-story shear frame. The results demonstrate the ability of PNPE to estimate time-dependent failure probability accurately and efficiently with a limited number of representative points.

Suggested Citation

  • Guo, Hongyuan & Zhang, Jiaxin & Dong, You & Frangopol, Dan M., 2024. "Probability-informed neural network-driven point-evolution kernel density estimation for time-dependent reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 249(C).
  • Handle: RePEc:eee:reensy:v:249:y:2024:i:c:s0951832024003077
    DOI: 10.1016/j.ress.2024.110234
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832024003077
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ress.2024.110234?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. Ling, Chunyan & Lu, Zhenzhou & Zhu, Xianming, 2019. "Efficient methods by active learning Kriging coupled with variance reduction based sampling methods for time-dependent failure probability," Reliability Engineering and System Safety, Elsevier, vol. 188(C), pages 23-35.
    2. Qian, Hua-Ming & Li, Yan-Feng & Huang, Hong-Zhong, 2021. "Time-variant system reliability analysis method for a small failure probability problem," Reliability Engineering and System Safety, Elsevier, vol. 205(C).
    3. Das, Sourav & Tesfamariam, Solomon, 2024. "Reliability assessment of stochastic dynamical systems using physics informed neural network based PDEM," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    4. Wang, Zequn & Chen, Wei, 2016. "Time-variant reliability assessment through equivalent stochastic process transformation," Reliability Engineering and System Safety, Elsevier, vol. 152(C), pages 166-175.
    5. Yan Shi & Zhenzhou Lu & Ruyang He, 2020. "Advanced time-dependent reliability analysis based on adaptive sampling region with Kriging model," Journal of Risk and Reliability, , vol. 234(4), pages 588-600, August.
    6. Du, Weiqi & Luo, Yuanxin & Wang, Yongqin, 2019. "Time-variant reliability analysis using the parallel subset simulation," Reliability Engineering and System Safety, Elsevier, vol. 182(C), pages 250-257.
    7. Li, Mingyang & Wang, Zequn, 2022. "LSTM-augmented deep networks for time-variant reliability assessment of dynamic systems," Reliability Engineering and System Safety, Elsevier, vol. 217(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. Wang, Dapeng & Qiu, Haobo & Gao, Liang & Jiang, Chen, 2021. "A single-loop Kriging coupled with subset simulation for time-dependent reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    2. Jiang, Chen & Qiu, Haobo & Gao, Liang & Wang, Dapeng & Yang, Zan & Chen, Liming, 2020. "EEK-SYS: System reliability analysis through estimation error-guided adaptive Kriging approximation of multiple limit state surfaces," Reliability Engineering and System Safety, Elsevier, vol. 198(C).
    3. Qian, Hua-Ming & Li, Yan-Feng & Huang, Hong-Zhong, 2021. "Time-variant system reliability analysis method for a small failure probability problem," Reliability Engineering and System Safety, Elsevier, vol. 205(C).
    4. Cao, Runan & Sun, Zhili & Wang, Jian & Guo, Fanyi, 2022. "A single-loop reliability analysis strategy for time-dependent problems with small failure probability," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
    5. Zhang, Yang & Xu, Jun & Beer, Michael, 2023. "A single-loop time-variant reliability evaluation via a decoupling strategy and probability distribution reconstruction," Reliability Engineering and System Safety, Elsevier, vol. 232(C).
    6. Zhang, Yang & Xu, Jun & Gardoni, Paolo, 2024. "A loading contribution degree analysis-based strategy for time-variant reliability analysis of structures under multiple loading stochastic processes," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    7. 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).
    8. Jiang, Chen & Yan, Yifang & Wang, Dapeng & Qiu, Haobo & Gao, Liang, 2021. "Global and local Kriging limit state approximation for time-dependent reliability-based design optimization through wrong-classification probability," Reliability Engineering and System Safety, Elsevier, vol. 208(C).
    9. Jian Wang & Xiang Gao & Zhili Sun, 2021. "An Importance Sampling Framework for Time-Variant Reliability Analysis Involving Stochastic Processes," Sustainability, MDPI, vol. 13(14), pages 1-16, July.
    10. Zhao, Zhao & Lu, Zhao-Hui & Zhang, Xuan-Yi & Zhao, Yan-Gang, 2022. "A nested single-loop Kriging model coupled with subset simulation for time-dependent system reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
    11. 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).
    12. Ouyang, Linhan & Che, Yushuai & Park, Chanseok & Chen, Yuejian, 2024. "A novel active learning Gaussian process modeling-based method for time-dependent reliability analysis considering mixed variables," Reliability Engineering and System Safety, Elsevier, vol. 244(C).
    13. Zhan, Hongyou & Xiao, Ning-Cong & Ji, Yuxiang, 2022. "An adaptive parallel learning dependent Kriging model for small failure probability problems," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    14. Jian Wang & Xiang Gao & Zhili Sun, 2021. "A Multilevel Simulation Method for Time-Variant Reliability Analysis," Sustainability, MDPI, vol. 13(7), pages 1-16, March.
    15. Das, Sourav & Tesfamariam, Solomon, 2024. "Reliability assessment of stochastic dynamical systems using physics informed neural network based PDEM," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    16. Dong, Y. & Teixeira, A.P. & Guedes Soares, C., 2020. "Application of adaptive surrogate models in time-variant fatigue reliability assessment of welded joints with surface cracks," Reliability Engineering and System Safety, Elsevier, vol. 195(C).
    17. Ameryan, Ala & Ghalehnovi, Mansour & Rashki, Mohsen, 2022. "AK-SESC: a novel reliability procedure based on the integration of active learning kriging and sequential space conversion method," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    18. Wang, Dapeng & Qiu, Haobo & Gao, Liang & Jiang, Chen, 2024. "A Subdomain uncertainty-guided Kriging method with optimized feasibility metric for time-dependent reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    19. Subramanian, Abhinav & Mahadevan, Sankaran, 2022. "Importance sampling for probabilistic prognosis of sector-wide flight separation safety," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    20. Ni, Pinghe & Li, Jun & Hao, Hong & Yan, Weimin & Du, Xiuli & Zhou, Hongyuan, 2020. "Reliability analysis and design optimization of nonlinear structures," Reliability Engineering and System Safety, Elsevier, vol. 198(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:249:y:2024:i:c:s0951832024003077. 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.