• DocumentCode
    530711
  • Title

    The performance parameter fault diagnosis for automobile engine based on ANFIS

  • Author

    Kong, Li-Fang ; Wang, Jun ; Wang, Zhong-Hua

  • Author_Institution
    Basic Depts., Xuzhou Air Force Coll., Xuzhou, China
  • Volume
    3
  • fYear
    2010
  • fDate
    24-26 Aug. 2010
  • Firstpage
    554
  • Lastpage
    557
  • Abstract
    This paper builds the fault diagnosis model and optimizes the input interface of the model by normalizing the initial data of the performance parameter for the automobile engine, carrying on information fusion and adopting the Adaptive Neural Fuzzy Interference System (ANFIS). The recognition rate of the model reaches 94.38% under the test of field test data. Corresponding BP neural network modeling and fuzzy recognition modeling, the model enjoys reliability, strong generalization ability, and high failure recognition rate. Moreover, it can effectively detect the performance parameter failure for the automobile engine.
  • Keywords
    adaptive systems; automobiles; automotive components; backpropagation; fault diagnosis; fuzzy neural nets; fuzzy reasoning; sensor fusion; ANFIS; BP neural network modeling; adaptive neural fuzzy interference system; automobile engine; failure recognition rate; field test data; fuzzy recognition modeling; information fusion; model input interface; performance parameter fault diagnosis; Automotive engineering; Engines; MATLAB; Mathematical model; Petroleum; ANFIS; Performance Parameter; fault diagnosis; fuzzy recognication;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computer, Mechatronics, Control and Electronic Engineering (CMCE), 2010 International Conference on
  • Conference_Location
    Changchun
  • Print_ISBN
    978-1-4244-7957-3
  • Type

    conf

  • DOI
    10.1109/CMCE.2010.5610247
  • Filename
    5610247