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Fault detection in rotating machinery using kernel-based probability density estimation

Desforges, M J, Jacob, P J and Ball, Andrew (2000) Fault detection in rotating machinery using kernel-based probability density estimation. International Journal of Systems Science, 31 (11). pp. 1411-1426. ISSN 0020-7721

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Abstract

In this paper, a method is presented which allows abnormal or unexpected operating conditions to be identified from measured response data. Potential applications of such a technique cover a wide range of engineering situations where a definite, early warning of an abnormal state is essential, but where classification of the particular abnormality is of lesser importance. In the technique described, unexpected operating conditions are identified by the presence of measured data which are significantly different from those known to correspond to normal operating conditions or responses. The proposed approach to the detection of such novel data is based upon probability density function (PDF) estimation using a kernel method. The theory behind this approach is presented for the multivariate case, and the need for data pre-processing in practical applications of PDF estimation is highlighted. The kernel method is demonstrated on simulated data sets. Finally, vibration response data from an electric motor with three levels of phase imbalance are used to illustrate the application of the PDF estimation method to the detection of abnormal conditions. The proposed method yields a clear indication of the existence of a fault in the AC motor, with no prior knowledge of the particular fault state

Item Type: Article
Subjects: T Technology > T Technology (General)
T Technology > TJ Mechanical engineering and machinery
Schools: School of Computing and Engineering
School of Computing and Engineering > Diagnostic Engineering Research Centre
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
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Depositing User: Sharon Beastall
Date Deposited: 20 Jan 2010 12:47
Last Modified: 03 Dec 2010 12:36
URI: http://eprints.hud.ac.uk/id/eprint/6802

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