• DocumentCode
    3363901
  • Title

    Adaptive Neural Fuzzy Networks Model of Automobile Performance Monitoring

  • Author

    Lifang, Kong ; Dong, Li ; Ying, Zhao

  • Author_Institution
    Air Force Logistic Acad., Xuzhou, China
  • Volume
    2
  • fYear
    2012
  • fDate
    26-27 Aug. 2012
  • Firstpage
    72
  • Lastpage
    75
  • Abstract
    The model for automobile engine performance monitoring and fault detection was proposed based on adaptive neural fuzzy interference system. With recognition mechanism of the adaptive neural fuzzy interference system, according to the properties of entropy, this paper using entropy optimizes the input interface of adaptive neural fuzzy interference system , this model was combined with characteristic performance of automobile engine to attain the degrees of engine performance´s abnormal state for monitoring engine performance. The approach can sensitively and accurately reflect the whole performance of the engine. Meanwhile, this method improves the rate of identifying whether the performance of the engine is normal or not, finds out the potential forepart fault of engine and prevents the spread of the fault. The validity of this method is testified by monitoring certain type of cummins engine 6BT5.9.
  • Keywords
    automotive engineering; fuzzy neural nets; internal combustion engines; mechanical engineering computing; adaptive neural fuzzy interference system; adaptive neural fuzzy network model; automobile engine performance monitoring; automobile performance monitoring; entropy; fault detection; input interface; recognition mechanism; Adaptation models; Adaptive systems; Engines; Entropy; Indexes; Interference; Monitoring; Adaptive neural fuzzy interference system; Auto-engine; Fault detection; Performance monitoring;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Human-Machine Systems and Cybernetics (IHMSC), 2012 4th International Conference on
  • Conference_Location
    Nanchang, Jiangxi
  • Print_ISBN
    978-1-4673-1902-7
  • Type

    conf

  • DOI
    10.1109/IHMSC.2012.113
  • Filename
    6305727