Comparison of different classification algorithms for fault detection and fault isolation in complex systems

Marcel Jung, Octavian Niculita, Zakwan Skaf

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

    30 Citations (Scopus)
    188 Downloads (Pure)


    Due to the lack of sufficient results seen in literature, feature extraction and classification methods of hydraulic systems appears to be somewhat challenging. This paper compares the performance of three classifiers (namely linear support vector machine (SVM), distance-weighted k-nearest neighbor (WKNN), and decision tree (DT) using data from optimized and non-optimized sensor set solutions. The algorithms are trained with known data and then tested with unknown data for different scenarios characterizing faults with different degrees of severity. This investigation is based solely on a data-driven approach and relies on data sets that are taken from experiments on the fuel system. The system that is used throughout this study is a typical fuel delivery system consisting of standard components such as a filter, pump, valve, nozzle, pipes, and two tanks. Running representative tests on a fuel system are problematic because of the time, cost, and reproduction constraints involved in capturing any significant degradation. Simulating significant degradation requires running over a considerable period; this cannot be reproduced quickly and is costly.
    Original languageEnglish
    Title of host publicationProcedia Manufacturing
    PublisherElsevier B.V.
    Number of pages8
    ISBN (Electronic)2351-9789
    Publication statusPublished - Mar 2018


    • classification algorithms
    • fault detection
    • Maintenance
    • Machine Learning
    • Monitoring

    ASJC Scopus subject areas

    • Artificial Intelligence
    • Industrial and Manufacturing Engineering


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