IDEAS home Printed from https://ideas.repec.org/a/rfa/setjnl/v7y2020i1p48-63.html
   My bibliography  Save this article

A Proposed Scheme for Fault Discovery and Extraction Using ANFIS: Application to Train Braking System

Author

Listed:
  • Tse Sparthan
  • Wolfgang Nzie
  • Bertin Sohfotsing
  • Olivier Garro
  • Tibi Beda

Abstract

This paper showcases the use of model oriented techniques for real time fault discovery and extraction on train track unit. An analytical system model is constructed and simulated in Mathlab to showcase the fair and unfair status of the system. The discovery and extraction phases are centered on a hybrid adaptive neuro-fuzzy inference feature extraction and segregated module. Output module interprites zero (0) as a good status of the traintrack unit and one (1) as an unpleasant status. Final results showcase the robustness and ability to discover and extract multitude of unpleasant scenarios that hinder the smooth operations of train track units due to its high selectivity and sensitivity quality.

Suggested Citation

  • Tse Sparthan & Wolfgang Nzie & Bertin Sohfotsing & Olivier Garro & Tibi Beda, 2020. "A Proposed Scheme for Fault Discovery and Extraction Using ANFIS: Application to Train Braking System," Studies in Engineering and Technology, Redfame publishing, vol. 7(1), pages 48-63, December.
    • Handle: RePEc:rfa:setjnl:v:7:y:2020:i:1:p:48-63
    as

    Download full text from publisher

    File URL: https://redfame.com/journal/index.php/set/article/download/4822/5135
    Download Restriction: no
    File URL: https://redfame.com/journal/index.php/set/article/view/4822
    Download Restriction: no
    ---><---

    More about this item

    JEL classification:

    • R00 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General - - - General
    • Z0 - Other Special Topics - - General

    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:rfa:setjnl:v:7:y:2020:i:1:p:48-63. 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: Redfame publishing (email available below). General contact details of provider: https://edirc.repec.org/data/cepflch.html .

    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.