This paper describes a fuzzy model that learns automotive diagnostic knowledge through machine learning techniques. The fuzzy model contains the algorithms for automatically generating fuzzy rules and optimizing fuzzy membership functions. The fuzzy model has been implemented to detect a vacuum leak in the electronic engine controller (EEC) as part of the end-of-line test at automotive assembly plants. The implemented system has been tested extensively, and its performance is presented.


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    Title :

    A fuzzy system for automotive fault diagnosis: fast rule generation and self-tuning


    Contributors:
    Yi Lu (author) / Tie Qi Chen (author) / Hamilton, B. (author)

    Published in:

    Publication date :

    2000


    Size :

    10 Seiten, 17 Quellen




    Type of media :

    Article (Journal)


    Type of material :

    Print


    Language :

    English




    A Fuzzy System for Automotive Fault Diagnosis

    Lu, Y. / Chen, T.-Q. / Hamilton, B. et al. | British Library Conference Proceedings | 1998


    A fuzzy system for automotive fault diagnosis

    Lu,Y. / Chen,T.Q. / Hamilton,B. et al. | Automotive engineering | 1998


    A Fuzzy System for Automotive Fault Diagnosis

    Hamilton, Brennan / Chen, Tie-Qi / Lu, Yi | SAE Technical Papers | 1998