IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v240y2023ics0951832023005288.html
   My bibliography  Save this article

A new active learning method for system reliability analysis with multiple failure modes

Author

Listed:
  • Xu, Chunlong
  • Yang, Ya
  • Wu, Huajun
  • Zhou, Jianping

Abstract

For practical system problems, all response values of the component performance function can be obtained by running an expensive computational model once. In current adaptive Kriging-based methods for system reliability analysis, only one or all the component Kriging models are updated in each iteration. The former may waste computational resources, whereas the latter has the problem of overfitting. To improve the efficiency and stability of these problems, this study proposes a new active learning method for system reliability analysis, where a specific number of component Kriging models are refined in each iteration, rather than updating only one or all the component kriging models, as in the existing methods. First, a new learning function based on an approximate estimation of the error probability of the system is proposed. Subsequently, two strategies are proposed to stabilize the adaptive Kriging-based algorithms. Finally, five examples are used to compare the proposed approach with the other existing active Kriging-based methods. Practical validations show that the proposed method outperforms the other methods in terms of accuracy, efficiency, and stability.

Suggested Citation

  • Xu, Chunlong & Yang, Ya & Wu, Huajun & Zhou, Jianping, 2023. "A new active learning method for system reliability analysis with multiple failure modes," Reliability Engineering and System Safety, Elsevier, vol. 240(C).
  • Handle: RePEc:eee:reensy:v:240:y:2023:i:c:s0951832023005288
    DOI: 10.1016/j.ress.2023.109614
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832023005288
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ress.2023.109614?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.

    Citations

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


    Cited by:

    1. 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).

    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:240:y:2023:i:c:s0951832023005288. 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.

    We have no bibliographic references for this item. You can help adding them by using 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.