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Modulation Signal Bispectrum Analysis of Motor Current Signals for Stator Fault Diagnosis

Alwodai, Ahmed, Xia, X., Shao, Yimin, Gu, Fengshou and Ball, Andrew (2012) Modulation Signal Bispectrum Analysis of Motor Current Signals for Stator Fault Diagnosis. In: 18th International Conference on Automation and Computing (ICAC), 2012. IEEE, Loughborough, UK, pp. 1-6. ISBN 9781908549006

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    Abstract

    Induction motors are the most widely used electrical machines in industry. To diagnose any possible incipient faults, many techniques have been developed. Motor current signature analysis (MCSA) is a common practice in industry to find motor faults. However, because small modulations due to faults it is difficult to quantify it in the measured signals which predominates with supply frequency, higher order harmonics and noise. In this paper a modulation signal (MS) bispectrum is investigated to detect different severities of stator faults. It shows that MS bispectrum has the capability to accurately estimate modulation degrees and suppress the random and non-modulation components. Test results show that MS bispectrum has a better performance in differentiating spectrum amplitudes due to stator faults and hence produces better diagnosis performance, compared with that of conventional power spectrum analysis.

    Item Type: Book Chapter
    Subjects: T Technology > T Technology (General)
    T Technology > TA Engineering (General). Civil engineering (General)
    T Technology > TK Electrical engineering. Electronics Nuclear engineering
    Schools: School of Computing and Engineering
    School of Computing and Engineering > Automotive Engineering Research Group
    School of Computing and Engineering > Diagnostic Engineering Research Centre > Energy, Emissions and the Environment Research Group
    School of Computing and Engineering > Diagnostic Engineering Research Centre > Machinery Condition and Performance Monitoring Research Group
    School of Computing and Engineering > Informatics Research Group > XML, Database and Information Retrieval Research Group
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    Depositing User: Sara Taylor
    Date Deposited: 20 Sep 2012 12:40
    Last Modified: 02 Jan 2013 15:52
    URI: http://eprints.hud.ac.uk/id/eprint/14977

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