The present work is the continuation of the research activity developed by the same authors in last years about the use of recent technologies (Artificial Neural Networks) for the set up of “software redundancy” modules to be implemented On Board for the use in Diagnostic Systems.In the present work, a system based on Artificial Neural Networks models for automotive engines Fault Diagnosis and Isolation purposes is set-up and analysed. Four sensors/actuators (throttle valve, rotational speed, torque and intake manifold pressure) are considered, and the respective acquired data are used to train and test four ANN modules correlating the different quantities.An FDI scheme is presented which generates fault codes sequences by suitably treating the primary residuals, obtained by comparing experimental data with the calculated ones by the ANN modules. The robust fault isolation capabilities of the proposed FDI system are presented and discussed. The proposed model architecture appears as suitable to solve either OBD problems or quality controls at factory engine production lines.


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    Titel :

    OBD Engine Fault Detection Using a Neural Approach


    Weitere Titelangaben:

    Sae Technical Papers


    Beteiligte:

    Kongress:

    SAE 2001 World Congress ; 2001



    Erscheinungsdatum :

    05.03.2001




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Print


    Sprache :

    Englisch




    2001-01-0559 OBD Engine Fault Detection Using a Neural Approach

    Grimaldi, C. N. / Mariani, F. / SAE | British Library Conference Proceedings | 2001


    Automobile engine fault diagnosis using neural network

    Kher, S. / Chande, P.K. / Sharma, P.C. | IEEE | 2001


    OBD engine default detection using a neural approach

    Grimaldi,C.N. / Mariani,F. / Univ.di Perugia,IT | Kraftfahrwesen | 2001


    Automobile Engine Fault Diagnosis Using Neural Network

    Kher, S. / Chande, P. K. / Sharma, P. C. et al. | British Library Conference Proceedings | 2001