IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v188y2019icp310-319.html
   My bibliography  Save this article

Time-variant reliability analysis based on high dimensional model representation

Author

Listed:
  • Cheng, Kai
  • Lu, Zhenzhou

Abstract

Time-variant reliability analysis aims at estimating the probability that an engineering system successfully performs intended missions over a certain period of time under various sources of uncertainty. In order to perform the time-variant reliability analysis efficiently, this paper presents a high dimensional model representation (HDMR) model combined with an active learning strategy to estimate the failure probability of dynamic problem. Firstly, the HDMR meta-model is established in the augmented input space (random variables and time) based on Gaussian process regression technique. Then, a reliability analysis approach incorporating epistemic uncertainty is proposed, and a learning function is introduced to update the experimental design sequentially. Finally, the Monte Carlo simulation method is applied for time-variant failure probability assessment based on the well-developed HDMR meta-model. Two engineering applications are used to demonstrate the effectiveness of the proposed method for time-variant reliability analysis.

Suggested Citation

  • Cheng, Kai & Lu, Zhenzhou, 2019. "Time-variant reliability analysis based on high dimensional model representation," Reliability Engineering and System Safety, Elsevier, vol. 188(C), pages 310-319.
    • Handle: RePEc:eee:reensy:v:188:y:2019:i:c:p:310-319
    DOI: 10.1016/j.ress.2019.03.041
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832018305842
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ress.2019.03.041?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. Sun, Zhili & Wang, Jian & Li, Rui & Tong, Cao, 2017. "LIF: A new Kriging based learning function and its application to structural reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 157(C), pages 152-165.
    2. 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.
    3. Jian, Wang & Zhili, Sun & Qiang, Yang & Rui, Li, 2017. "Two accuracy measures of the Kriging model for structural reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 167(C), pages 494-505.
    4. Hawchar, Lara & El Soueidy, Charbel-Pierre & Schoefs, Franck, 2017. "Principal component analysis and polynomial chaos expansion for time-variant reliability problems," Reliability Engineering and System Safety, Elsevier, vol. 167(C), pages 406-416.
    5. Bourinet, J.-M., 2016. "Rare-event probability estimation with adaptive support vector regression surrogates," Reliability Engineering and System Safety, Elsevier, vol. 150(C), pages 210-221.
    6. Lambert, Romain S.C. & Lemke, Frank & Kucherenko, Sergei S. & Song, Shufang & Shah, Nilay, 2016. "Global sensitivity analysis using sparse high dimensional model representations generated by the group method of data handling," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 128(C), pages 42-54.
    7. Xiao, Sinan & Lu, Zhenzhou & Wang, Pan, 2018. "Multivariate global sensitivity analysis for dynamic models based on wavelet analysis," Reliability Engineering and System Safety, Elsevier, vol. 170(C), pages 20-30.
    8. Wei, Pengfei & Song, Jingwen & Lu, Zhenzhou & Yue, Zhufeng, 2016. "Time-dependent reliability sensitivity analysis of motion mechanisms," Reliability Engineering and System Safety, Elsevier, vol. 149(C), pages 107-120.
    9. Cadini, F. & Gioletta, A., 2016. "A Bayesian Monte Carlo-based algorithm for the estimation of small failure probabilities of systems affected by uncertainties," Reliability Engineering and System Safety, Elsevier, vol. 153(C), pages 15-27.
    10. Wang, Zequn & Wang, Pingfeng, 2013. "A new approach for reliability analysis with time-variant performance characteristics," Reliability Engineering and System Safety, Elsevier, vol. 115(C), pages 70-81.
    11. Echard, B. & Gayton, N. & Lemaire, M. & Relun, N., 2013. "A combined Importance Sampling and Kriging reliability method for small failure probabilities with time-demanding numerical models," Reliability Engineering and System Safety, Elsevier, vol. 111(C), pages 232-240.
    12. 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.
    13. Wen, Zhixun & Pei, Haiqing & Liu, Hai & Yue, Zhufeng, 2016. "A Sequential Kriging reliability analysis method with characteristics of adaptive sampling regions and parallelizability," Reliability Engineering and System Safety, Elsevier, vol. 153(C), pages 170-179.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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).
    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. 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).
    4. 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).
    5. Wang, Lei & Liu, Yaru & Li, Min, 2022. "Time-dependent reliability-based optimization for structural-topological configuration design under convex-bounded uncertain modeling," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
    6. Saraygord Afshari, Sajad & Enayatollahi, Fatemeh & Xu, Xiangyang & Liang, Xihui, 2022. "Machine learning-based methods in structural reliability analysis: A review," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
    7. Hu, Yingshi & Lu, Zhenzhou & Jiang, Xia & Wei, Ning & Zhou, Changcong, 2021. "Time-dependent structural system reliability analysis model and its efficiency solution," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    8. Zhang, Jian & Gong, Weijie & Yue, Xinxin & Shi, Maolin & Chen, Lei, 2022. "Efficient reliability analysis using prediction-oriented active sparse polynomial chaos expansion," Reliability Engineering and System Safety, Elsevier, vol. 228(C).

    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, Junxiang & Chen, Jianqiao, 2019. "Solving time-variant reliability-based design optimization by PSO-t-IRS: A methodology incorporating a particle swarm optimization algorithm and an enhanced instantaneous response surface," Reliability Engineering and System Safety, Elsevier, vol. 191(C).
    2. Cheng, Kai & Lu, Zhenzhou, 2021. "Adaptive Bayesian support vector regression model for structural reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 206(C).
    3. Wang, Zeyu & Shafieezadeh, Abdollah, 2020. "Real-time high-fidelity reliability updating with equality information using adaptive Kriging," Reliability Engineering and System Safety, Elsevier, vol. 195(C).
    4. 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.
    5. Wang, Zeyu & Shafieezadeh, Abdollah, 2019. "REAK: Reliability analysis through Error rate-based Adaptive Kriging," Reliability Engineering and System Safety, Elsevier, vol. 182(C), pages 33-45.
    6. Chen, Weidong & Xu, Chunlong & Shi, Yaqin & Ma, Jingxin & Lu, Shengzhuo, 2019. "A hybrid Kriging-based reliability method for small failure probabilities," Reliability Engineering and System Safety, Elsevier, vol. 189(C), pages 31-41.
    7. Teixeira, Rui & Martinez-Pastor, Beatriz & Nogal, Maria & O’Connor, Alan, 2021. "Reliability analysis using a multi-metamodel complement-basis approach," Reliability Engineering and System Safety, Elsevier, vol. 205(C).
    8. Xu, Jun & Wang, Ding, 2019. "Structural reliability analysis based on polynomial chaos, Voronoi cells and dimension reduction technique," Reliability Engineering and System Safety, Elsevier, vol. 185(C), pages 329-340.
    9. Jian, Wang & Zhili, Sun & Qiang, Yang & Rui, Li, 2017. "Two accuracy measures of the Kriging model for structural reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 167(C), pages 494-505.
    10. Keshtegar, Behrooz & Chakraborty, Subrata, 2018. "An efficient-robust structural reliability method by adaptive finite-step length based on Armijo line search," Reliability Engineering and System Safety, Elsevier, vol. 172(C), pages 195-206.
    11. Keshtegar, Behrooz & Kisi, Ozgur, 2018. "RM5Tree: Radial basis M5 model tree for accurate structural reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 180(C), pages 49-61.
    12. Teixeira, Rui & Nogal, Maria & O’Connor, Alan & Martinez-Pastor, Beatriz, 2020. "Reliability assessment with density scanned adaptive Kriging," Reliability Engineering and System Safety, Elsevier, vol. 199(C).
    13. Wang, Jinsheng & Xu, Guoji & Li, Yongle & Kareem, Ahsan, 2022. "AKSE: A novel adaptive Kriging method combining sampling region scheme and error-based stopping criterion for structural reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
    14. Wang, Jian & Sun, Zhili & Cao, Runan, 2021. "An efficient and robust Kriging-based method for system reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    15. Wei, Pengfei & Liu, Fuchao & Tang, Chenghu, 2018. "Reliability and reliability-based importance analysis of structural systems using multiple response Gaussian process model," Reliability Engineering and System Safety, Elsevier, vol. 175(C), pages 183-195.
    16. Jing, Zhao & Chen, Jianqiao & Li, Xu, 2019. "RBF-GA: An adaptive radial basis function metamodeling with genetic algorithm for structural reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 189(C), pages 42-57.
    17. 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).
    18. Yang, Seonghyeok & Jo, Hwisang & Lee, Kyungeun & Lee, Ikjin, 2022. "Expected system improvement (ESI): A new learning function for system reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    19. Chengning Zhou & Ning-Cong Xiao & Ming J Zuo & Xiaoxu Huang, 2020. "AK-PDF: An active learning method combining kriging and probability density function for efficient reliability analysis," Journal of Risk and Reliability, , vol. 234(3), pages 536-549, June.
    20. Bolin Liu & Liyang Xie, 2020. "An Improved Structural Reliability Analysis Method Based on Local Approximation and Parallelization," Mathematics, MDPI, vol. 8(2), pages 1-13, February.

    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:188:y:2019:i:c:p:310-319. 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.