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Fault Detection and Diagnosis using Principal Component Analysis of Vibration Data from a Reciprocating Compressor

Ahmed, M., Gu, Fengshou and Ball, Andrew (2012) Fault Detection and Diagnosis using Principal Component Analysis of Vibration Data from a Reciprocating Compressor. In: 18th International Conference On Automation And Computing (ICAC), 2012. IEEE, Cardiff, UK, pp. 461-466. ISBN 978-1-4673-1559-3

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    Abstract

    This paper investigates the use of time domain vibration features for detection and diagnosis of different faults from a multi stage reciprocating compressor. Principal Component Analysis (PCA) is based to develop a detection and diagnosis framework in that the effective diagnostic features are selected from the PCA of 14 potential features and a PCA model based detection method using and statistics is subsequently developed to detect various faults including valve leakage, suction valve leakage, inter-cooler leakage, loose drive belt, discharge valve leakage combined with suction valve leakage, suction valve leakage combined with intercooler leakage and discharge valve leakage combined with intercooler leakage. Moreover a study of Q-contributions has found two original features: Histogram Lower Bound and Normal Negative log- likelihood which allow full classification of different simulated faults.

    Item Type: Book Chapter
    Subjects: T Technology > T Technology (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 > Diagnostic Engineering Research Centre > Measurement System and Signal Processing 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 14:41
    Last Modified: 05 Jun 2013 12:28
    URI: http://eprints.hud.ac.uk/id/eprint/15004

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