Model Based Bearing Fault Detection Using Support Vector Machines



Published Mar 26, 2021
Karthik Kappaganthu C. Nataraj Biswanath Samanta


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

Kappaganthu , K. ., Nataraj , C. ., & Samanta, B. . (2021). Model Based Bearing Fault Detection Using Support Vector Machines. Annual Conference of the PHM Society, 1(1). Retrieved from
Abstract 211 | PDF Downloads 138



bearings, classification, diagnosis, features, model based diagnostics, support vector machines

(Barkova and Barkov, 1995) A.
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:, 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.
Technical Research Papers

Most read articles by the same author(s)