• DocumentCode
    2917911
  • Title

    The Fault Diagnosis System of Electric Locomotive Based on MAS

  • Author

    Cao, Hongyu ; Wu, Min ; Peng, Jun

  • Author_Institution
    Sch. of the Inf. Sci. & Eng., Central South Univ., Changsha, China
  • Volume
    3
  • fYear
    2009
  • fDate
    21-22 Nov. 2009
  • Firstpage
    248
  • Lastpage
    251
  • Abstract
    The efficiency and the effect of fault diagnosis are increasingly important for electric locomotive. However, due to the complexity of the locomotive system configuration and the locomotive inferior running condition, it is much difficult to realize the fault diagnosis system on electric locomotive. In this paper, a multi-objective fault diagnosis algorithm based on multi-agent is proposed. It considers the correlation of different fault diagnosis and takes advantage of the capabilities of the multiple agents in communication and cooperation to estimate and recognize particular faults. Furthermore, a fault diagnosis system architecture is designed based on multi-agent system to support diagnosing fault online for train drivers. The practical application results show the efficiency of the proposed system.
  • Keywords
    electric locomotives; fault diagnosis; multi-agent systems; railways; MAS; electric locomotive; fault diagnosis system architecture; fault recognition; locomotive inferior running condition; locomotive system configuration complexity; multi-agent system; multiobjective fault diagnosis algorithm; online fault diagnosis; railway; train drivers; Circuit faults; Diagnostic expert systems; Electrical fault detection; Fault diagnosis; Large-scale systems; Multiagent systems; Rail transportation; Railway safety; Real time systems; Vehicle safety;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Information Technology Application, 2009. IITA 2009. Third International Symposium on
  • Conference_Location
    Nanchang
  • Print_ISBN
    978-0-7695-3859-4
  • Type

    conf

  • DOI
    10.1109/IITA.2009.254
  • Filename
    5369462