IDEAS home Printed from https://ideas.repec.org/a/hin/jnlmpe/5858272.html
   My bibliography  Save this article

Weighted Evidence Combination Rule Based on Evidence Distance and Uncertainty Measure: An Application in Fault Diagnosis

Author

Listed:
  • Lei Chen
  • Ling Diao
  • Jun Sang

Abstract

Conflict management in Dempster-Shafer theory (D-S theory) is a hot topic in information fusion. In this paper, a novel weighted evidence combination rule based on evidence distance and uncertainty measure is proposed. The proposed approach consists of two steps. First, the weight is determined based on the evidence distance. Then, the weight value obtained in first step is modified by taking advantage of uncertainty. Our proposed method can efficiently handle high conflicting evidences with better performance of convergence. A numerical example and an application based on sensor fusion in fault diagnosis are given to demonstrate the efficiency of our proposed method.

Suggested Citation

  • Lei Chen & Ling Diao & Jun Sang, 2018. "Weighted Evidence Combination Rule Based on Evidence Distance and Uncertainty Measure: An Application in Fault Diagnosis," Mathematical Problems in Engineering, Hindawi, vol. 2018, pages 1-10, January.
    • Handle: RePEc:hin:jnlmpe:5858272
    DOI: 10.1155/2018/5858272
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/MPE/2018/5858272.pdf
    Download Restriction: no
    File URL: http://downloads.hindawi.com/journals/MPE/2018/5858272.xml
    Download Restriction: no
    File URL: https://libkey.io/10.1155/2018/5858272?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. Lei Chen & Ling Diao & Jun Sang, 2019. "A novel weighted evidence combination rule based on improved entropy function with a diagnosis application," International Journal of Distributed Sensor Networks, , vol. 15(1), pages 15501477188, January.
    2. Sachan, Swati & Almaghrabi, Fatima & Yang, Jian-Bo & Xu, Dong-Ling, 2024. "Human-AI collaboration to mitigate decision noise in financial underwriting: A study on FinTech innovation in a lending firm," International Review of Financial Analysis, Elsevier, vol. 93(C).

    More about this item

    Statistics

    Access and download statistics

    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:hin:jnlmpe:5858272. 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: Mohamed Abdelhakeem (email available below). General contact details of provider: https://www.hindawi.com .

    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.