Preventative automotive maintenance is important to detect faults in the vehicle early enough before they become failures. With vehicles becoming connected, we are able to obtain streams of big data from vehicles which can be processed in the cloud, then deep learning models can be used for fault detection in real time and executed in the vehicle as an IoT edge. The challenge is to determine which model is appropriate to use in the vehicle, accounting for tradeoff in accuracy, prediction time and memory. In this paper, we address this challenge by extending our previous work [1] in data-driven automotive diagnostics and use a cloud IoT edge framework for development and deployment on a Raspberry Pi 3B+. Using pre- ignition data, our results demonstrate that there is a tradeoff between F1-score and computational cost. A model based on our previous work DADN (Deep Automotive Diagnostic Network) achieves the highest F1-score but does not execute the fastest. Simple models such as 1 CNN (Convolutional Neural Network) and 0 LSTM (Long Short-Term Memory) may be adequate enough for pre-ignition detection.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    A Cloud IoT Edge Framework for Efficient Data-Driven Automotive Diagnostics


    Beteiligte:
    Chin, Alvin (Autor:in) / Wolf, Peter (Autor:in) / Tian, Jilei (Autor:in)


    Erscheinungsdatum :

    01.09.2019


    Format / Umfang :

    614229 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    REMOTE AUTOMOTIVE DIAGNOSTICS

    JINGLE CURT / TERLEP FRANK | Europäisches Patentamt | 2020

    Freier Zugriff

    REMOTE AUTOMOTIVE DIAGNOSTICS

    CURT JINGLE / FRANK TERLEP | Europäisches Patentamt | 2020

    Freier Zugriff

    Pre-ignition Detection Using Deep Neural Networks: A Step Towards Data-driven Automotive Diagnostics

    Wolf, Peter / Mrowca, Artur / Nguyen, Tam Thanh et al. | IEEE | 2018


    Automotive Diagnostics Using WEB

    Alves, J.B. / Konzen, A. / Society of Automotive Engineers | British Library Conference Proceedings | 2008