IDEAS home Printed from https://ideas.repec.org/a/ids/ijrsaf/v13y2019i1-2p44-60.html
   My bibliography  Save this article

Multi-state system reliability analysis methods based on Bayesian networks merging dynamic and fuzzy fault information

Author

Listed:
  • Qin He
  • Ruijun Zhang
  • Tianyu Liu
  • Yabing Zha
  • Jie Liu

Abstract

Traditional Bayesian Networks (BNs) have limited abilities to analyse system reliability with fuzzy and dynamic information. To deal with such information in system reliability analysis, a new multi-state system reliability analysis method based on BNs was proposed. The proposed method effectively solved the deficiencies of existing reliability analysis methods based on BNs incorporating fuzziness and fault information. In this work, fuzzy set theory and changing failure probability function of components were introduced into BNs, and the dynamic fuzzy subset was introduced. The curve of the fuzzy dynamic fault probability of the leaf node fault state and fuzzy dynamic importance were developed and calculated. Finally, a case study of a truck system was employed to demonstrate the performance of the proposed methods in comparison with traditional fault tree and T-S fuzzy importance analysis methods. The proposed method proved to be feasible in capturing the fuzzy and dynamic information in real-world systems.

Suggested Citation

  • Qin He & Ruijun Zhang & Tianyu Liu & Yabing Zha & Jie Liu, 2019. "Multi-state system reliability analysis methods based on Bayesian networks merging dynamic and fuzzy fault information," International Journal of Reliability and Safety, Inderscience Enterprises Ltd, vol. 13(1/2), pages 44-60.
    • Handle: RePEc:ids:ijrsaf:v:13:y:2019:i:1/2:p:44-60
    as

    Download full text from publisher

    File URL: http://www.inderscience.com/link.php?id=97016
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

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

    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:ids:ijrsaf:v:13:y:2019:i:1/2:p:44-60. 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: Sarah Parker (email available below). General contact details of provider: http://www.inderscience.com/browse/index.php?journalID=98 .

    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.