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A General Regression Neural Network Model for Gearbox Fault Detection using Motor Operating Parameters

Baqqar, Mabrouka, Wang, Tie, Ahmed, Mahmud, Gu, Fengshou, Lu, Joan and Ball, Andrew (2012) A General Regression Neural Network Model for Gearbox Fault Detection using Motor Operating Parameters. In: 18th International Conference On Automation And Computing (ICAC), 2012. IEEE, Cardiff, UK, pp. 584-588. ISBN 978-1-4673-1559-3

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    Abstract

    Condition monitoring of a gearbox is a very
    important activity because of the importance of gearboxes in
    power transmission in many industrial processes. Thus there
    has always been a constant pressure to improve measuring
    techniques and analytical tools for early detection of faults in
    gearboxes. This study forces on developing gearbox monitoring
    methods based on operating parameters which are available in
    machine control processes rather than using additional
    measurements such as vibration and acoustics used in many
    studies. To utilise these parameters for gearbox monitoring,
    this paper examines a model based approach in which a data
    model has been developed using a General Regression Neural
    Network (GRNN) to captures the nonlinear connections
    between the electrical current of driving motor and control
    parameters such as load settings and temperatures based on a
    two stage helical gearbox power transmission system. Using the
    model a direct comparison can be made between the measured
    and predicted values to find abnormal gearbox conditions of
    different gear tooth breakages based on a threshold setup in
    developing the model.

    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
    Related URLs:
    Depositing User: Sara Taylor
    Date Deposited: 20 Sep 2012 14:37
    Last Modified: 05 Jun 2013 12:31
    URI: http://eprints.hud.ac.uk/id/eprint/15003

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