Complex anomaly for enhanced machine independent condition monitoring

Muhammad Amar, Iqbal Gondal, Campbell Wilson

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


    Safety in machine applications requires tracking machine health during the time of operations. Anomaly detection techniques are used to model normal behavior of the machines and raise an alarm if any anomaly is observed. But traditional anomaly detection techniques do not identify type and severity of aberrance in terms of amplitude, pattern or both. Once the anomalous behavior is observed then fault detection techniques are applied to diagnose faults. For machine independent condition monitoring (MICM) a range of features transforms are needed for autonomous learning of the fault classifiers for different parameters to identify variety of fault types which requires huge amount of time. In this paper a novel complex anomaly plan (CAP) representation has been proposed with amplitude anomalies on real and pattern anomalies on imaginary axis. To plot amplitude and pattern anomalies in the CAP, normal state vibrations frequency features are used to train Gaussian models for each of the frequency. The dynamic location of the anomaly plotted in the CAP gives a measure of the intensity of the anomaly, where real and imaginary axis components help the fault classifier to make an appropriate selection of the transform and thus enhances the efficiency of MICM framework.

    Original languageEnglish
    Title of host publicationICOSST 2015 - 2015 International Conference on Open Source Systems and Technologies, Proceedings
    Subtitle of host publication17-19 December, 2015, Lahore, Pakistan
    Place of PublicationPiscataway, NJ
    PublisherIEEE, Institute of Electrical and Electronics Engineers
    Number of pages6
    ISBN (Electronic)9781479978120, 9781479978113
    Publication statusPublished - 1 Feb 2016
    Event9th International Conference on Open Source Systems and Technologies - Lahore, Pakistan
    Duration: 17 Dec 201519 Dec 2015
    Conference number: 9


    Conference9th International Conference on Open Source Systems and Technologies
    Abbreviated titleICOSST 2015


    • anomaly detection
    • bearing faults
    • Machine Health Monitoring (MHM)

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