Model Based Bearing Fault Detection Using Support Vector Machines
##plugins.themes.bootstrap3.article.main##
##plugins.themes.bootstrap3.article.sidebar##
Abstract
This paper deals with the development of a model based method for bearing fault diagnostics. This method effectively combines the information available in the data and the model for efficient classification of the bearing and the type of defect. A four degrees of freedom nonlinear rigid rotor model is used to simulate the rotor bearing system. Precession of the shaft is measured using proximity probes. The deviation of the measurement from the model is used to classify the system. Typically proximity probe data by itself does not contain enough information for accurate classification. However, when the information from the model is incorporated the combined features provide excellent classification performance. Further the use of a model also enables better classification over varying parameters. A support vector machine is used for classification.
How to Cite
##plugins.themes.bootstrap3.article.details##
bearings, classification, diagnosis, features, model based diagnostics, support vector machines
N. Barkov. Condition assesment and life prediction of rolling element bearings. Sound and Vibration, 1995.
(Cade et al., 2005) Iain S. Cade, Patrick S. Keogh, and M. Necip Sahinkaya. Fault identification in rotor/ magnetic bearing systems using discrete time wavelet coefficients. IEEE/ ASME Transctions on Mechatronics, 10(6):648–657, December 2005 2005.
(Harris, 2002) Tedric A. Harris. Rolling Bearing Analysis. Wiley-Interscience, 2002.
(Harsha et al., 2004) S. P. Harsha, K. Sandeep, and R. Prakash. Nonlinear behaviors of rolling element bearings due to surface waviness. Journal of Sound and Vibration, 272:557–580, 2004.
(Haykin, 1999) Simon Haykin. Neural Networks: A comprehensive foundation. Prentice Hall, 1999.
(Holm-Hansen and Gao, 2000) B. T. Holm-Hansen and R. X. Gao. Vibration analysis of a sensor- integrated ball bearing. ASME Journal of Vibration and Acoustics, 122:384392, 2000.
(Liew et al., 2002) A. Liew, N. Feng, and E. J. Hahn. Transient rotordynamic modeling of rolling element bearing systems. Transactions of ASME, 124:984–991, October 2002.
(Mori et al., 1996) K. Mori, N. Kasashmi, T. Yosh- ioka, and Y. Ueno. Prediction of spalling on ball bearings by applying discrete wavelet transform to vibration signals. Wear, 8:195–162, 1996.
(Nataraj and Pietrusko, 2005) C. Nataraj and Robert Gerad Pietrusko. Dynamic response of rigid rotors supported on rolling element bearings with an outer raceway defect. In ASME, editor, Proceedings of IDETC/CIE 2005, Long Beach California, USA, September 2005.
(Randall and Gao, 1994) R. B. Randall and Y. Gao. Extraction of modal parameters from the response of power cepstrum. Journal of Sound and Vibration, 176:179–193, 1994.
(Sawalhi and Randall, 2008) N. Sawalhi and R. B. Randall. Simulating gear and bearing interactions in the presence of faults part1. the combined gear bearing model and simulation of localized bearing faults. Mechanical Systems and Signal Processing, 2008.
(Shawe-Taylor and Christianini, 2004) John Shawe- Taylor and Nello Christianini. Kernel Methods for Pattern Analysis. Cambridge University Press, 2004.
(Spectra Quest, 2009) Inc Spectra Quest. Http: www.spectraquest.com, 2009.
(Tandon, 1994) N. Tandon. A comparision of some vibration parameters for condition monitoring of rolling element bearings. Measurement, 12:285– 286, 1994.
(Vapnik,1998) V. N. Vapnik. Statistical Learning Theory. John Wiley & Sons, 1998.
(Ypma, 2001) A. Ypma. Learning Methods of Machine Vibration Analysis and Health Monitoring. PhD thesis, Delft University, 2001.
(Yu et al., 2002) J. J. Yu, D. E. Bently, P. Goldman, K. P. Dayton, and B. G. Van Slyke. Rolling element bearing defect detection and diagnostics using displacement transducers. Journal of Engineering for Gas Turbines and Power, 124:517–527, 2002.
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
The Prognostic and Health Management Society advocates open-access to scientific data and uses a Creative Commons license for publishing and distributing any papers. A Creative Commons license does not relinquish the author’s copyright; rather it allows them to share some of their rights with any member of the public under certain conditions whilst enjoying full legal protection. By submitting an article to the International Conference of the Prognostics and Health Management Society, the authors agree to be bound by the associated terms and conditions including the following:
As the author, you retain the copyright to your Work. By submitting your Work, you are granting anybody the right to copy, distribute and transmit your Work and to adapt your Work with proper attribution under the terms of the Creative Commons Attribution 3.0 United States license. You assign rights to the Prognostics and Health Management Society to publish and disseminate your Work through electronic and print media if it is accepted for publication. A license note citing the Creative Commons Attribution 3.0 United States License as shown below needs to be placed in the footnote on the first page of the article.
First Author et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 United States License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.