IDEAS home Printed from https://ideas.repec.org/a/hin/complx/5851491.html
   My bibliography  Save this article

RAMS Analysis of Train Air Braking System Based on GO-Bayes Method and Big Data Platform

Author

Listed:
  • Guoqiang Cai
  • Yaofei Wang
  • Qiong Song
  • Chen Yang

Abstract

The RAMS (reliability, availability, maintainability, and security) of the air braking system is an important indicator to measure the safety performance of the system; it can reduce the life cycle cost (LCC) of the rail transit system. Existing safety analysis methods are limited to the level of relatively simple factual descriptions and statistical induction, failing to provide a comprehensive safety evaluation on the basis of system structure and accumulated data. In this paper, a new method of safety analysis is described for the failure mode of the air braking system, GO-Bayes. This method combines the structural modeling of the GO method with the probabilistic reasoning of Bayes methods, introduces the probability into the analysis process of GO, performs reliability analysis of the air braking system, and builds a big data platform for the air braking system to guide the system maintenance strategy. An automatic train air braking system is taken as an example to verify the usefulness and accuracy of the proposed method. Using ExtendSim software shows the feasibility of the method and its advantages in comparison with fault tree analysis.

Suggested Citation

  • Guoqiang Cai & Yaofei Wang & Qiong Song & Chen Yang, 2018. "RAMS Analysis of Train Air Braking System Based on GO-Bayes Method and Big Data Platform," Complexity, Hindawi, vol. 2018, pages 1-14, October.
    • Handle: RePEc:hin:complx:5851491
    DOI: 10.1155/2018/5851491
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/8503/2018/5851491.pdf
    Download Restriction: no
    File URL: http://downloads.hindawi.com/journals/8503/2018/5851491.xml
    Download Restriction: no
    File URL: https://libkey.io/10.1155/2018/5851491?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
    ---><---

    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:complx:5851491. 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.