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Bispectrum Analysis of Motor Current Signals for Fault Diagnosis of Reciprocating Compressors

Naid, A., Gu, Fengshou, Shao, Yimin, Al-Arbi, Salem and Ball, Andrew (2009) Bispectrum Analysis of Motor Current Signals for Fault Diagnosis of Reciprocating Compressors. Key Engineering Materials, 413-41. pp. 505-511. ISSN 1013-9826

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    Abstract

    The induction motor is the most common driver in industry and has been previously
    proposed as a means of inferring the condition of an entire equipment train, predominantly through
    the measurement and processing of power supply parameters. This has obvious advantages in terms
    of being non-intrusive or remote, less costly to apply and improved safety. This paper describes the
    use of the induction motor current to identify and quantify a number of common faults seeded on a
    two-stage reciprocating compressor. An analysis of the compressor working cycle leads to current
    signal the components that are sensitive to the common faults seeded to compressor system, and
    second- and third-order signal processing tools are used to analyse the current signals. It is shown
    that the developed diagnostic features: the bispectral peak value from the amplitude modulation
    bispectrum and the kurtosis from the current gives rise to reliable fault classification results. The
    low feature values can differentiate the belt looseness from other fault cases and valve leakage and
    inter-cooler leakage can be separated easily using two linear classifiers. This work provides a novel
    approach to the analysis stator current data for the diagnosis of motor drive faults.

    Item Type: Article
    Additional Information: (c) Trans Tech Publications
    Uncontrolled Keywords: Reciprocating compressor, Bispectrum, Kurtosis, Motor Current Signature Analysis
    Subjects: T Technology > TJ Mechanical engineering and machinery
    Schools: School of Computing and Engineering
    School of Computing and Engineering > Automotive Engineering Research Group
    School of Computing and Engineering > Diagnostic Engineering Research Centre
    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 > Diagnostic Engineering Research Centre > Measurement System and Signal Processing Research Group
    School of Computing and Engineering > Informatics Research Group
    School of Computing and Engineering > Informatics Research Group > XML, Database and Information Retrieval Research Group
    Related URLs:
    Depositing User: Graham Stone
    Date Deposited: 27 May 2009 16:54
    Last Modified: 08 Dec 2010 13:11
    URI: http://eprints.hud.ac.uk/id/eprint/4536

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