Abstract
This paper deals with the important task of feature selection for the detection of faulty bearings in a rotor-bearing system. Various time, frequency and time-frequency based features are obtained from signals measured from bearings with and without outer race defect. The features are divided into a training set, a validation set and a test set. The task is to develop an optimal subset of features for a pattern classification algorithm which can efficiently and accurately classify the state of the machine as healthy or faulty. The features are ranked based on the mutual information content between the feature subset and the state of the machine. A validation set from the measured data is then used to obtain the optimal subset for classification. The performance of the method is evaluated using the test set.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Avci, E., Avci, D.: An expert discrete wavelet adaptive network based fuzzy inference system for digital signal processing. Expert Syst. Appl. 33, 582–589 (2006)
Barkova, A., Barkov, N.: Condition assesment and life prediction of rolling element bearings. Sound Vibr. 28, 10–17 (1995)
Battiti, R.: Using mutual information for selecting features in supervised neural network learning. IEEE Trans. Neur. Networks 5, 537–550 (1994)
Cade, I.S., Keogh, P.S., Sahinkaya, M.N.: Fault identification in rotor/ magnetic bearing systems using discrete time wavelet coefficients. IEEE/ASME Trans. Mechatron. 10(6), 648–657 (2005)
Cover, T.M., Thomas, J.A.: Elements of Information Theory. Wiley, New York (1991)
Djebala, A., Ouelaa, D., Hamzaoui, N.: Detection of rolling bearing defects using discrete wavelet analysis. Mechanica 43, 339–348 (2008)
Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. Wiley-Interscience, New York (2001)
Feng, Y., Schlindwein, F.S.: Normalized wavelet packets quantifiers for condition monitoring. Mech. Syst. Signal Proces. 23, 712–723 (2009)
Mori, K., Kasashmi, N., Yoshioka, T., Ueno, Y.: Prediction of spalling on ball bearings by applying discrete wavelet transform to vibration signals. Wear 8, 195–162 (1996)
Nataraj, C., Pietrusko, R.G.: Dynamic response of rigid rotors supported on rolling element bearings with an outer raceway defect. In: ASME (ed.) Proceedings of IDETC/CIE 2005. Long Beach, CA, USA (2005)
Ocak, H., Loparo, K.A., Discenzo, F.M.: Online tracking of bearing wear using wavelet packet decomposition and probabilistic modeling. J. Sound Vibr. 302, 951–961 (2007)
Pan, Y., Chen, J., Guo, L.: Robust bearing performance degradation assessment method based on improved wavelet packet -support vector data descriptions. Mech. Syst. Signal Proces. 23, 669–681 (2009)
Peng, H., Long, F., Ding, C.: Feature selection based on mutual information: Criteria of max-dependency, max-relevance and min-redundancy. IEEE Trans. Pattern Anal. Mach. Intel. 27, 1226–1238 (2005)
Randall, R.B., Gao, Y.: Extraction of modal parameters from the response of power cepstrum. J. Sound Vibr. 176, 179–193 (1994)
Spectra Quest: http://www.spectraquest.com (2009)
Tandon, N.: A comparision of some vibration parameters for condition monitoring of rolling element bearings. Measurement 12, 285–286 (1994)
Wu, J.D., Liu, C.H.: Investigation of engine fault diagnosis using discrete wavelet transform and neural network. Expert Syst. Appl. 35, 1200–1213 (2008)
Ypma, A.: Learning methods of machine vibration analysis and health monitoring. Ph.D. thesis, Delft University (2001)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer Science+Business Media B.V.
About this paper
Cite this paper
Kappaganthu, K., Nataraj, C., Samanta, B. (2011). Feature Selection for Bearing Fault Detection Based on Mutual Information. In: Gupta, K. (eds) IUTAM Symposium on Emerging Trends in Rotor Dynamics. IUTAM Bookseries, vol 1011. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-0020-8_44
Download citation
DOI: https://doi.org/10.1007/978-94-007-0020-8_44
Published:
Publisher Name: Springer, Dordrecht
Print ISBN: 978-94-007-0019-2
Online ISBN: 978-94-007-0020-8
eBook Packages: EngineeringEngineering (R0)