Condition monitoring of rolling elements bearings is investigated in this paper. Recently [12, 13], we have developed a diagnosis procedure that combines a signal processing tool, i.e. Time Synchronous Averaging (TSA), and a pattern recognition method, i.e. Support Vector Machines (SVM), for bearing fault detection and prediction. As the generalization performance of the SVM-boundaries was strongly affected by the signal transmission path, this paper is then concerned with the integration of Spectral Kurtosis (SK) analysis in the diagnosis procedure to improve efficiency in such cases. We validate the use of both Time Synchronous Averaging and Spectral Kurtosis analysis, as signal processing tools that will automatically highlight bearing defect frequencies in the envelope spectrum. Twenty-one features (rms, peak, crest factor, band spectral energy, etc...) are extracted from the envelope of signals obtained from these two analyses and are used in the learning scheme. Results show that the generalization performance is less affected by the signal transmission path and the faulty bearing location, thus demonstrating that the modified diagnosis procedure can actually find some underlying patterns that are common to each type of bearing failure. The case-dependency of the support decision tool can therefore be reduced.
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ASME 2008 Pressure Vessels and Piping Conference
July 27–31, 2008
Chicago, Illinois, USA
Conference Sponsors:
- Pressure Vessels and Piping
ISBN:
978-0-7918-4830-2
PROCEEDINGS PAPER
Application of Time Synchronous Averaging, Spectral Kurtosis and Support Vector Machines for Bearing Fault Identification
Christian N. Komgom,
Christian N. Komgom
Ecole Polytechnique de Montreal, Montreal, QC, Canada
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Njuki W. Mureithi,
Njuki W. Mureithi
Ecole Polytechnique de Montreal, Montreal, QC, Canada
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Aouni A. Lakis
Aouni A. Lakis
Ecole Polytechnique de Montreal, Montreal, QC, Canada
Search for other works by this author on:
Christian N. Komgom
Ecole Polytechnique de Montreal, Montreal, QC, Canada
Njuki W. Mureithi
Ecole Polytechnique de Montreal, Montreal, QC, Canada
Aouni A. Lakis
Ecole Polytechnique de Montreal, Montreal, QC, Canada
Paper No:
PVP2008-61601, pp. 137-146; 10 pages
Published Online:
July 24, 2009
Citation
Komgom, CN, Mureithi, NW, & Lakis, AA. "Application of Time Synchronous Averaging, Spectral Kurtosis and Support Vector Machines for Bearing Fault Identification." Proceedings of the ASME 2008 Pressure Vessels and Piping Conference. Volume 7: Operations, Applications and Components. Chicago, Illinois, USA. July 27–31, 2008. pp. 137-146. ASME. https://doi.org/10.1115/PVP2008-61601
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