With the swift progression of the automotive industry, real-time monitoring of vehicular status has become increasingly paramount. Existing monitoring methodologies are, however, constrained by their inherent limitations. This paper presents an automobile status monitoring platform rooted in multi-sensor data fusion. Data processing and analysis are executed through a synergistic approach, employing the LSTM model combined with a rule engine. The research discerns that this methodology proficiently extracts pertinent information from abundant sensor data, monitoring the automobile's operational state and potential issues both real-time and with precision. This investigation proffers robust backing for vehicular health management and fault prognosis, demonstrating significant practical relevance.


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    Titel :

    Design of an Automobile Status Monitoring Platform Based on Multi-Sensor Data Fusion


    Beteiligte:
    Chen, Shengjiao (Autor:in) / Hu, Chenyu (Autor:in)


    Erscheinungsdatum :

    25.11.2023


    Format / Umfang :

    349556 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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