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Computational Intelligence and Neuroscience
Volume 2012, Article ID 582453, 12 pages
http://dx.doi.org/10.1155/2012/582453
Research Article

Evaluation of Effectiveness of Wavelet Based Denoising Schemes Using ANN and SVM for Bearing Condition Classification

1Department of Mechanical and Manufacturing Engineering, Manipal Institute of Technology, Manipal University, Karnataka, Manipal 576104, India
2Department of Mechanical Engineering, NMAM Institute of Technology, Karnataka, Nitte 574104, India
3Department of Mechanical Engineering, Vidya Vikas Institute of Engineering and Technology, Karnataka, Mysore 570028, India
4COMADEM International, Birmingham B29 6DA, UK

Received 31 March 2012; Revised 27 September 2012; Accepted 11 October 2012

Academic Editor: Karim Oweiss

Copyright © 2012 Vijay G. S. et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

The wavelet based denoising has proven its ability to denoise the bearing vibration signals by improving the signal-to-noise ratio (SNR) and reducing the root-mean-square error (RMSE). In this paper seven wavelet based denoising schemes have been evaluated based on the performance of the Artificial Neural Network (ANN) and the Support Vector Machine (SVM), for the bearing condition classification. The work consists of two parts, the first part in which a synthetic signal simulating the defective bearing vibration signal with Gaussian noise was subjected to these denoising schemes. The best scheme based on the SNR and the RMSE was identified. In the second part, the vibration signals collected from a customized Rolling Element Bearing (REB) test rig for four bearing conditions were subjected to these denoising schemes. Several time and frequency domain features were extracted from the denoised signals, out of which a few sensitive features were selected using the Fisher’s Criterion (FC). Extracted features were used to train and test the ANN and the SVM. The best denoising scheme identified, based on the classification performances of the ANN and the SVM, was found to be the same as the one obtained using the synthetic signal.