• DocumentCode
    3007944
  • Title

    Application of fuzzy neural network in fault diagnosis for scraper conveyor vibration

  • Author

    Xiaofeng Gong ; Xianmin Ma ; Yongqiang Zhang ; Jianxiang Yang

  • Author_Institution
    Coll. of Electr. & Control Eng., Xi´an Univ. of Sci. & Technol., Xi´an, China
  • fYear
    2013
  • fDate
    26-28 Aug. 2013
  • Firstpage
    1135
  • Lastpage
    1140
  • Abstract
    In order to avoid losses, which is caused by electromechanical failure in the coal large transport equipments, this article introduced a model that integrated by breakdown extraction, failure diagnosis and fault analysis in the large-scale scraper chain conveyor breakdown. The weak signal of mechanical vibration detection by ANFIS is adopted. And it is also used for Troubleshooting effectively and accurately. Lastly, an effective diagnosis suggestion is given through the instrumentation KS-2000. And by this way, we not only proved the feasibility and superiority of this plan, but also achieved predictive maintenance in the true sense.
  • Keywords
    conveyors; failure analysis; fault diagnosis; fuzzy neural nets; vibrations; ANFIS; breakdown extraction; coal large transport equipments; electromechanical failure; fault analysis; fault diagnosis; fuzzy neural network; instrumentation KS-2000; large scale scraper chain conveyor breakdown; mechanical vibration detection; predictive maintenance; scraper conveyor vibration; weak signal; Electric breakdown; Fault diagnosis; Monitoring; Rotors; Training; Vibration measurement; Vibrations; ANFIS; coal scraper conveyor; fault diagnosis; vibration;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Information and Automation (ICIA), 2013 IEEE International Conference on
  • Conference_Location
    Yinchuan
  • Type

    conf

  • DOI
    10.1109/ICInfA.2013.6720466
  • Filename
    6720466