Damage identification and external effects removal for roller bearing diagnostics



Published Jul 3, 2012
Pirra M. Fasana A. Garibaldi L. Marchesiello S.


In this paper we introduce a method to identify if a bearing is damaged by removing the effects of speed and load. In fact, such conditions influence vibration data during acquisitions in rotating machinery and may lead to biased results when diagnostic techniques are applied. This method combines Empirical Mode Decomposition (EMD) and Support Vector Machine classification method. The vibration signal acquired is decomposed into a finite number of Intrinsic Mode Functions (IMFs) and their energy is evaluated. These features are then used to train a particular type of SVM, namely One-Class Support Vector Machine (OCSVM), where only one class of data is known. Data acquisition is done both for a healthy bearing and for one whose rolling element presents a 450 micron damage. We consider three speeds and three different radial loads for both bearings, so nine conditions are acquired for each type of bearing overall. Feature evaluation is done using EMD and then healthy data belonging to the various conditions are taken into account to train the OCSVM. The remaining data are analysed by the classifier as test object. The real class each element belongs to is known, so the efficiency of the method can be measured by counting the errors made by the labelling procedure. These evaluations are performed by applying different kinds of SVM kernel.

How to Cite

M., P., A., F., L., G., & S., M. (2012). Damage identification and external effects removal for roller bearing diagnostics. PHM Society European Conference, 1(1). https://doi.org/10.36001/phme.2012.v1i1.1447
Abstract 114 | PDF Downloads 106



Empirical Mode Decomposition, One-Class SVM, speed and load effect removal, bearing diagnostics

Antoni, J. (2006). The spectral kurtosis: a useful tool for characterising non-stationary signals. Mechanical System and Signal Processing, 20, 282307.
Bartelmus, W., & Zimroz, R. (2009). Vibration condition monitoring of planetary gearbox under varying external load. Mechanical Systems and Signal Processing, 23, 246257.
Chebil, J., Noel, G., Mesbah, M., & Deriche, M. (2009). Wavelet decomposition for the detection and diagnosis of fault in rolling element bearings. Jordan Journal of Mechanical and Industrial Engineering, 3, 260-267.
Cocconcelli, M., & Rubini. (2011). Support Vector Machines for condition monitoring of bearings in a varying-speed machinery. In Proceeding International Conference on Condition Monitoring, Cardiff, UK.
Cocconcelli, M., Rubini, R., Zimroz, R., & Bartelmus, W. (2011). Diagnostics of ball bearings in varying-speed motors by means of Artificial Neural Networks. In Proceeding International Conference on Condition Monitoring, Cardiff, UK.
Flandrin, P., & Rilling, G. (2004). Empirical Mode Decomposition as a filter bank. IEEE Signal Processing Letters, 11(2), 112-114.
Gao, Q., Duan, C., Fan, H., & Meng, Q. (2008). Rotating machine fault diagnosis using empirical mode decomposition. Mechanical System and Signal Processing, 22, 1072-1081.
Huang, N. E., & Shen, S. (Eds.). (2005). Hilbert-Huang Transform and Its Applications. World Scientific, Singapore.
Huang, N. E., Shen, Z., Long, S. R., Wu, M. C., Shih, H. H., Zheng, Q., et al. (1998). The empirical mode decomposition and the Hilbert spectrum for nonlinear and nonstationary time series analysis. In Proceedings of the Royal Society (p. 903-995).
Junsheng, C., Deije, Y., & Yu, Y. (2006). A fault diagnosis approach for roller bearings based on EMD method anda AR model. Mechanical System and Signal Processing, 20, 350-362.
Machorro-López, J., Bellino, A., Garibaldi, L., & Adams, D. (2011). PCA-based techniques for detecting cracked rotating shafts including the effects of temperature variations. In Proceeding 6th International Conference on Acoustical and Vibratory Surveillance Methods and Diagnostic Techniques, Compigne, France.
Pirra, M., Gandino, E., Torri, A., Garibaldi, L., & Machorro-López, J. M. (2011). PCA algorithm for detection, localisation and evolution of damages in gearbox bearings. Journal of Physics. Conference series, 305(1).
Randall, R. B., & Antoni, J. (2011). Rolling element bearing diagnostics - A tutorial. Mechanical System and Signal Processing, 25, 485-520.
Rojas, A., & Nandi, A. B. (2006). Practical scheme for fast detection and classification of rolling-element bearing faults using support vector machines. Mechanical Systems and Signal Processing, 20, 1523-1536.
Schlkopf, B.,Williamson, R. C., Smola, A. J., Taylor, J. S., & Platt, J. C. (2000). Support vector method for novelty detection. Advances in Neural Information Processing Systems, 12, 582-586.
Shin, H. J., Eom, D.-H., & Kim, S.-S. (2005). One-class support vector machines - an application in machine fault detection and classification. Computer & Industrial Engineering, 48, 395-408.
Vapnik, V. N. (Ed.). (1982). Estimation of dependences based on empirical data. Springer-Verlag, New York.
Widodo, A., & Yang, B. (2006). Support vector machine in machine condition monitoring and fault diagnosis. Mechanical Systems and Signal Processing, 21, 2560-2574.
Worden, K., Staszewski, W. J., & Hensman, J. J. (2011). Natural computing for mechanical systems research: A tutorial overview. Mechanical Systems and Signal Processing, 25, 4-111.
Yu, Y., Deije, Y., & Junsheng, C. (2006). A roller bearing fault diagnosis method based on EMD energy entropy and ANN. Journal of Sound and Vibration, 294, 269-277.
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