• DocumentCode
    3501249
  • Title

    The Performance Parameter Fault Diagnosis for Automobile Engine Based on ANFIS

  • Author

    Zhang, Jian-Hua ; Kong, Li-Fang ; Tian, Zhang ; Hao, Wei

  • Author_Institution
    Basic Depts., Xuzhou Air Force Coll., Xuzhou, China
  • Volume
    2
  • fYear
    2010
  • fDate
    23-24 Oct. 2010
  • Firstpage
    261
  • Lastpage
    264
  • Abstract
    In order to solve the fault diagnosis problem of performance Parameter, Adaptive Neuro-Fuzzy inference system (ANFIS) was applied to build a fault diagnosis model of automobile engine and induce cloud model of fan-out, outputting results are continued. Through verification of the built diagnosis model with data of engine tests, it has been found that the recognition accuracy increase from 84.38% to 98.81%, training error falling from 0.001683 to 0.0011526. Simulation results show that the fitting ability, convergence speed and recognition accuracy of improved ANFIS model are all superior to ANFIS. So a contingent fault of automobile engine can be identified effectively. Moreover, it can effectively detect the performance parameter failure for the automobile engine.
  • Keywords
    automotive engineering; fault diagnosis; fuzzy neural nets; fuzzy reasoning; internal combustion engines; mechanical engineering computing; ANFIS; adaptive neuro fuzzy inference system; automobile engine; convergence speed; engine test data; fault diagnosis; fitting ability; performance parameter; recognition accuracy; ANFIS; Performance Parameter; cloud model; fault diagnosis; fuzzy recognication;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Web Information Systems and Mining (WISM), 2010 International Conference on
  • Conference_Location
    Sanya
  • Print_ISBN
    978-1-4244-8438-6
  • Type

    conf

  • DOI
    10.1109/WISM.2010.149
  • Filename
    5662400