IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v239y2023ics095183202300443x.html
   My bibliography  Save this article

Novel Kriging based learning function for system reliability analysis with correlated failure modes

Author

Listed:
  • Feng, Kaixuan
  • Lu, Zhenzhou
  • Yang, Yixin
  • Ling, Chunyan
  • He, Pengfei
  • Dai, Ying

Abstract

Since some identical model inputs are contained in the limit state functions of different failure modes in system reliability analysis, these failure modes are correlated in general. However, the correlations of the failure modes are not considered in constructing the Kriging based learning function for system reliability analysis in most of current publications, which may damage the efficiency of system reliability analysis. To overcome this disadvantage, a novel Kriging based learning function for system reliability analysis is proposed in this paper by considering the correlations of the failure modes. At first, this paper derives the lower and upper bounds of the probability that the Kriging model misjudges the state (safety or failure) of the system with correlated failure modes at each candidate sample. Then, the reduction of the upper bound of misjudging probability is also deduced when adding a given candidate sample to the training set of a certain failure mode. Thereafter, a novel learning strategy is proposed by simultaneously selecting a new training sample and the corresponding updating failure mode to mostly reduce the upper bound of misjudging probability. Finally, several examples are employed to illustrate the performance of the proposed learning function in system reliability analysis.

Suggested Citation

  • Feng, Kaixuan & Lu, Zhenzhou & Yang, Yixin & Ling, Chunyan & He, Pengfei & Dai, Ying, 2023. "Novel Kriging based learning function for system reliability analysis with correlated failure modes," Reliability Engineering and System Safety, Elsevier, vol. 239(C).
    • Handle: RePEc:eee:reensy:v:239:y:2023:i:c:s095183202300443x
    DOI: 10.1016/j.ress.2023.109529
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S095183202300443X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ress.2023.109529?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. 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).
    3. Yuan, Kai & Xiao, Ning-Cong & Wang, Zhonglai & Shang, Kun, 2020. "System reliability analysis by combining structure function and active learning kriging model," Reliability Engineering and System Safety, Elsevier, vol. 195(C).
    4. 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).
    5. Zhang, Long-Wen & Dang, Chao & Zhao, Yan-Gang, 2023. "An efficient method for accessing structural reliability indexes via power transformation family," Reliability Engineering and System Safety, Elsevier, vol. 233(C).
    6. Yang, Xufeng & Liu, Yongshou & Mi, Caiying & Tang, Chenghu, 2018. "System reliability analysis through active learning Kriging model with truncated candidate region," Reliability Engineering and System Safety, Elsevier, vol. 169(C), pages 235-241.
    7. Zhang, Yu & Dong, You & Xu, Jun, 2023. "An accelerated active learning Kriging model with the distance-based subdomain and a new stopping criterion for reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    8. Song, Shufang & Lu, Zhenzhou & Qiao, Hongwei, 2009. "Subset simulation for structural reliability sensitivity analysis," Reliability Engineering and System Safety, Elsevier, vol. 94(2), pages 658-665.
    9. Fauriat, W. & Gayton, N., 2014. "AK-SYS: An adaptation of the AK-MCS method for system reliability," Reliability Engineering and System Safety, Elsevier, vol. 123(C), pages 137-144.
    10. Zhou, Changcong & Shi, Zhuangke & Kucherenko, Sergei & Zhao, Haodong, 2022. "A unified approach for global sensitivity analysis based on active subspace and Kriging," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    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, Yu & Dong, You & Frangopol, Dan M., 2024. "An error-based stopping criterion for spherical decomposition-based adaptive Kriging model and rare event estimation," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    2. Moustapha, Maliki & Parisi, Pietro & Marelli, Stefano & Sudret, Bruno, 2024. "Reliability analysis of arbitrary systems based on active learning and global sensitivity analysis," Reliability Engineering and System Safety, Elsevier, vol. 248(C).
    3. Yu, Shui & Ren, Yuyao & Wu, Xiao & Guo, Peng & Li, Yun, 2024. "Dynamic pruning-based Bayesian support vector regression for reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 244(C).
    4. Yuan, Kai & Sui, Xi & Zhang, Shijie & Xiao, Ning-cong & Hu, Jinghan, 2024. "AK-SYS-IE: A novel adaptive Kriging-based method for system reliability assessment combining information entropy," Reliability Engineering and System Safety, Elsevier, vol. 246(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. Zhang, Jinhao & Gao, Liang & Xiao, Mi, 2020. "A composite-projection-outline-based approximation method for system reliability analysis with hybrid uncertainties," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    2. 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).
    3. Cheng, Kai & Lu, Zhenzhou, 2021. "Adaptive Bayesian support vector regression model for structural reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 206(C).
    4. 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).
    5. Yuan, Kai & Sui, Xi & Zhang, Shijie & Xiao, Ning-cong & Hu, Jinghan, 2024. "AK-SYS-IE: A novel adaptive Kriging-based method for system reliability assessment combining information entropy," Reliability Engineering and System Safety, Elsevier, vol. 246(C).
    6. Yang, Seonghyeok & Lee, Mingyu & Lee, Ikjin, 2023. "A new sampling approach for system reliability-based design optimization under multiple simulation models," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    7. 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).
    8. Wang, Zeyu & Shafieezadeh, Abdollah, 2020. "On confidence intervals for failure probability estimates in Kriging-based reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 196(C).
    9. Zhou, Yicheng & Lu, Zhenzhou & Yun, Wanying, 2020. "Active sparse polynomial chaos expansion for system reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 202(C).
    10. 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).
    11. Zuhal, Lavi Rizki & Faza, Ghifari Adam & Palar, Pramudita Satria & Liem, Rhea Patricia, 2021. "On dimensionality reduction via partial least squares for Kriging-based reliability analysis with active learning," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    12. 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.
    13. Wang, Yanzhong & Xie, Bin & E, Shiyuan, 2022. "Adaptive relevance vector machine combined with Markov-chain-based importance sampling for reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 220(C).
    14. Huang, Shi-Ya & Zhang, Shao-He & Liu, Lei-Lei, 2022. "A new active learning Kriging metamodel for structural system reliability analysis with multiple failure modes," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
    15. Moustapha, Maliki & Parisi, Pietro & Marelli, Stefano & Sudret, Bruno, 2024. "Reliability analysis of arbitrary systems based on active learning and global sensitivity analysis," Reliability Engineering and System Safety, Elsevier, vol. 248(C).
    16. Zhang, Xufang & Wang, Lei & Sørensen, John Dalsgaard, 2019. "REIF: A novel active-learning function toward adaptive Kriging surrogate models for structural reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 185(C), pages 440-454.
    17. 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.
    18. 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).
    19. Wang, Lei & Hu, Zhuo & Dang, Chao & Beer, Michael, 2024. "Refined parallel adaptive Bayesian quadrature for estimating small failure probabilities," Reliability Engineering and System Safety, Elsevier, vol. 244(C).
    20. Zhang, Yu & Dong, You & Frangopol, Dan M., 2024. "An error-based stopping criterion for spherical decomposition-based adaptive Kriging model and rare event estimation," Reliability Engineering and System Safety, Elsevier, vol. 241(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:239:y:2023:i:c:s095183202300443x. 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.