Using a combination of RBFN, MLP and kNN classifiers for engine misfire detection

Yuhua Li, MJ Pont, CR Parikh, NB Jones

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    In this paper, we apply radial basis function networks (RBFN), multilayer Perceptron (MLP) and a conventional statistical classifier, k-nearest neighbour (kNN), to the detection of misfires in a petrol engine. Used alone, each classifier is shown to provide a similar level of performance. We then demonstrate that by combining these techniques using a simple `majority voting' algorithm, the overall performance of the system is improved by approximately 10%.
    Original languageEnglish
    Title of host publicationUnknown Host Publication
    Pages46-51
    Number of pages6
    Publication statusPublished - 2000
    EventSOFT COMPUTING TECHNIQUES AND APPLICATIONS -
    Duration: 1 Jan 2000 → …

    Publication series

    NameADVANCES IN SOFT COMPUTING

    Conference

    ConferenceSOFT COMPUTING TECHNIQUES AND APPLICATIONS
    Period1/01/00 → …

    Keywords

    • engine misfire detection
    • neural networks
    • multi-layer Perceptron
    • radial basis function
    • condition monitoring
    • fault classification

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