Uptime and maintenance planning are important issues for vehicle operators (e.g.operators of bus fleets). Unplanned downtime can cause a bus operator to be fined if the vehicle is not on time. Supervised classification methods for detecting faults in vehicles are compared in this thesis. Data has been collected by a vehicle manufacturer including three kinds of faulty states in vehicles (i.e. charge air cooler leakage, radiator and air filter clogging). The problem consists of differentiating between the normal data and the three different categories of faulty data. Evaluated methods include linear model, neural networks model, 1-nearest neighbor and random forest model. For every kind of model, a variable selection method should be used. In our thesis we try to find the best model for this problem, and also select the most important input signals. After we compare these four models, we found that the best accuracy (96.9% correct classifications) was achieved with the random forest model.


    Zugriff

    Download


    Exportieren, teilen und zitieren



    Titel :

    Supervised Methods for Fault Detection in Vehicle


    Beteiligte:
    Xiang, Gao (Autor:in) / Nan, Jiang (Autor:in)

    Erscheinungsdatum :

    2010-01-01


    Medientyp :

    Hochschulschrift


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Klassifikation :

    DDC:    629



    Vehicle fault detection system

    RICHARDS ADAM J | Europäisches Patentamt | 2021

    Freier Zugriff

    Vehicle ground fault detection

    ELSHAER MOHAMED / GALE ALLAN ROY / CHEN CHINGCHI | Europäisches Patentamt | 2020

    Freier Zugriff

    VEHICLE FAULT DETECTION SYSTEM

    RICHARDS ADAM J | Europäisches Patentamt | 2020

    Freier Zugriff

    Semi-supervised Detector Training with Prototypes for Vehicle Detection

    Waltner, Georg / Opitz, Michael / Krispel, Georg et al. | IEEE | 2019


    FAULT DETECTION METHOD, FAULT DETECTION APPARATUS, SERVER, AND VEHICLE

    YANG QIMO | Europäisches Patentamt | 2024

    Freier Zugriff