Abstract
Condition monitoring and faults diagnosis in rotating machinery is a current research field. In this direction the use of pattern recognition combined with non-destructive testing techniques such as’ vibration analysis and signal processing can be very helpful. In this paper is proposed, a diagnosis method of rotating machinery using vibration signatures using a Radial Basis Function classifier. Recorded signals were preprocessed with a Wavelet Decomposition and indicators were extracted both in temporal and frequency domains. To improve diagnosis performance, two techniques for dimension reduction of indicators space were combined; Principal Component Analysis and the ReliefF filter. The method was tested on real signatures from a vibration test rig, operating under several conditions, the results showed the interest to look closely at the choice of indicators in order to obtain best diagnosis performances.
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References
Tan, C., Irving, P., Mba, D.: A comparative experimental study on the diagnostic and prognostic capabilities of acoustics emission, vibration and spectrometric oil analysis for spur gears. Mechanical Systems and Signal Processing 21, 208–233 (2007); doi:10.1016/j.ymssp.2005.09.015
Renaudin, L., Bonnardot, F., Musy, O., et al.: Natural roller bearing fault detection by angular measurement of true instantaneous angular speed. Mechanical Systems and Signal Processing 24, 1998–2011 (2010); doi:10.1016/j.ymssp.2010.05.005
Jardine, A., Lin, D., Banjevic, D.: A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mechanical Systems and Signal Processing 20, 1483–1510 (2006); doi:10.1016/j.ymssp.2005.09.012
Bendjama, H., Bouhouche, S., Boucherit, M.S., Mansour, M.: Vibration signal analysis using Wavelet-PCA-NN technique for fault diagnosis in rotating machinery. Mediterranean Journal of Measurement and Control 6, 145–154 (2010)
Al-Badour, F., Sunar, M., Cheded, L.: Vibration analysis of rotating machinery using time–frequency analysis and wavelet techniques. Mechanical Systems and Signal Processing 25, 2083–2101 (2011); doi:10.1016/j.ymssp.2011.01.017
Khelf, I., Laouar, L.: Influence du choix des indicateurs lors du diagnostic des machines tournantes. In: Proceeding of the 4th International Congress Design and Modeling of Mechanical Systems (CMSM 2011), Sousse, Tunisia (2011) (in french)
Mallat, S.G.: A Theory for Multiresolution Signal Decomposition: The Wavelet Representation. IEEE Transactions on PAMI 11(7), 674–693 (1989)
Kira, K., Rendell, L.A.: A practical approach to feature selection. In: Proceedings of the Ninth International Workshop on Machine Learning, pp. 249–256. Morgan Kaufmann Publishers Inc. (1992)
Khelf, I., Laouar, L.: Amélioration du diagnostic d’une machine tournante par la sélection du vecteur d’entrée. In: Proceding of Congrès Français de Mécanique 2011, Besançon, France (2011) (in french)
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Khelf, I., Laouar, L., Bendjama, H., Bouchelaghem, A.M. (2012). Combining RBF-PCA-ReliefF Filter for a Better Diagnosis Performance in Rotating Machines. In: Fakhfakh, T., Bartelmus, W., Chaari, F., Zimroz, R., Haddar, M. (eds) Condition Monitoring of Machinery in Non-Stationary Operations. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28768-8_30
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DOI: https://doi.org/10.1007/978-3-642-28768-8_30
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-28767-1
Online ISBN: 978-3-642-28768-8
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