IDEAS home Printed from https://ideas.repec.org/a/sae/risrel/v222y2008i3p291-302.html
   My bibliography  Save this article

Modelling uncertainty in fault tree analyses using evidence theory

Author

Listed:
  • P Limbourg
  • R Savić
  • J Petersen
  • H-D Kochs

Abstract

The Dempster—Shafer Theory of Evidence (DST) has been considered as an alternative to probabilistic modelling if both a large amount of uncertainty and a conservative treatment of this uncertainty are necessary. Both requirements are normally met in early design stages. Expert estimates replace field data and hardly any accurate test results are available. Therefore, a conservative uncertainty treatment is beneficial to assure a reliable and safe design. The present paper explores the applicability of DST which merges interval-based and probabilistic uncertainty modelling on a fault tree analysis from the automotive area. The system under investigation, an automatic transmission from the ZF AS Tronic series is still in the development stage. Expert estimates and the Monte Carlo propagation of the resulting mass function through the system model are used to obtain the uncertainty on the system failure probability. An exploratory sensitivity based on a non-specifity measure indicates which components contribute to the overall model uncertainty. The results are used to predict if the system complies with a given target failure measure.

Suggested Citation

  • P Limbourg & R Savić & J Petersen & H-D Kochs, 2008. "Modelling uncertainty in fault tree analyses using evidence theory," Journal of Risk and Reliability, , vol. 222(3), pages 291-302, September.
    • Handle: RePEc:sae:risrel:v:222:y:2008:i:3:p:291-302
    DOI: 10.1243/1748006XJRR142
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1243/1748006XJRR142
    Download Restriction: no
    File URL: https://libkey.io/10.1243/1748006XJRR142?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
    ---><---

    Citations

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


    Cited by:

    1. Toppila, Antti & Salo, Ahti, 2017. "Selection of risk reduction portfolios under interval-valued probabilities," Reliability Engineering and System Safety, Elsevier, vol. 163(C), pages 69-78.
    2. Simon, Christophe & Bicking, Frédérique, 2017. "Hybrid computation of uncertainty in reliability analysis with p-box and evidential networks," Reliability Engineering and System Safety, Elsevier, vol. 167(C), pages 629-638.

    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:sae:risrel:v:222:y:2008:i:3:p:291-302. 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: SAGE Publications (email available below). General contact details of provider: .

    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.