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

DOI: 10.1155/2012/582453

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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. 1. Introduction The detection of fault in the machinery, in its incipient stage itself, has gained prime importance as it avoids machine down time, catastrophic failure of the machinery, threat to human life, high maintenance costs, and so forth. The fault diagnostic techniques based on the vibration signal analysis have become popular in recent times [1, 2]. The problem of the strong noise components masking the weak characteristic signals has always posed challenges to the condition monitoring expert. Several wavelet based signal processing techniques aiming at denoising the measured signal so as to increase the Signal-to-Noise Ratio (SNR) and reduce the Root-Mean-Square Error (RMSE) have been proposed and tried by several researchers [3–7]. The details of the techniques used by some of the researchers have been explained in Section 2.2. The wavelet based denoising technique has gained popularity due to its effectiveness and ease of application [8]. It overcomes the difficulty of determining the resonant frequency of the system. Therefore, the wavelet technique has been adopted in this work for denoising the bearing vibration signals. The detail coefficients, obtained from the Discrete Wavelet Transform (DWT), generally include a large proportion of the high-frequency noise components along with some of the characteristic

References

[1]  N. Tandon and A. Choudhury, “Review of vibration and acoustic measurement methods for the detection of defects in rolling element bearings,” Tribology International, vol. 32, no. 8, pp. 469–480, 1999.
[2]  M. S. Patil, J. Mathew, and P. K. R. Kumar, “Bearing signature analysis as a medium for fault detection: a review,” Journal of Tribology, vol. 130, no. 1, Article ID 014001, 2008.
[3]  Z. Huaigang, W. Zhibin, and Z. Ying, “Analysis of signal de-noising method based on an improved wavelet thresholding,” in Proceedings of the 9th International Conference on Electronic Measurement and Instruments (ICEMI '09), pp. 1987–1990, IEEE, August 2009.
[4]  H. T. Fang and D. S. Huang, “Wavelet de-noising by means of trimmed thresholding,” in Proceedings of the 5th World Congress on Intelligent Control and Automation (WCICA '04), pp. 1621–1624, Hangzhou, China, June 2004.
[5]  Y. Lin and J. Cai, “A new threshold function for signal denoising based on wavelet transform,” in Proceedings of the International Conference on Measuring Technology and Mechatronics Automation (ICMTMA '10), pp. 200–203, IEEE.
[6]  G. Zhang, J. Wu, and Z. Cui, “Application of wavelet thresholding de-noising in DSA,” in Proceedings of the International Symposium on Information Science and Engineering (ISISE '08), pp. 130–134, IEEE, December 2008.
[7]  L. Cai-lian, S. Ji-xiang, and K. Yao-hong, “Adaptive image denoising by a new thresholding function,” in Proceedings of the 6th International Conference on Wireless Communications, Networking and Mobile Computing, pp. 1–5, IEEE, September 2010.
[8]  Z. K. Peng and F. L. Chu, “Application of the wavelet transform in machine condition monitoring and fault diagnostics: a review with bibliography,” Mechanical Systems and Signal Processing, vol. 18, no. 2, pp. 199–221, 2004.
[9]  M. Michel, Y. Misiti, G. Oppenheim, and J. M. Poggi, “Wavelet Toolbox 4 User’s Guide,” 2010.
[10]  X. Wang, Y. Zi, and Z. He, “Multiwavelet denoising with improved neighboring coefficients for application on rolling bearing fault diagnosis,” Mechanical Systems and Signal Processing, vol. 25, no. 1, pp. 285–304, 2011.
[11]  D. L. Donoho, “De-noising by soft-thresholding,” IEEE Transactions on Information Theory, vol. 41, no. 3, pp. 613–627, 1995.
[12]  C. C. Wang, Y. Kang, P. C. Shen, Y. P. Chang, and Y. L. Chung, “Applications of fault diagnosis in rotating machinery by using time series analysis with neural network,” Expert Systems with Applications, vol. 37, no. 2, pp. 1696–1702, 2010.
[13]  J. Zarei, “Induction motors bearing fault detection using pattern recognition techniques,” Expert Systems with Applications, vol. 39, no. 1, pp. 68–73, 2012.
[14]  P. K. Kankar, S. C. Sharma, and S. P. Harsha, “Fault diagnosis of ball bearings using machine learning methods,” Expert Systems with Applications, vol. 38, no. 3, pp. 1876–1886, 2011.
[15]  V. N. Vapnik, “An overview of statistical learning theory,” IEEE Transactions on Neural Networks, vol. 10, no. 5, pp. 988–999, 1999.
[16]  Y. Yang, D. Yu, and J. Cheng, “A fault diagnosis approach for roller bearing based on IMF envelope spectrum and SVM,” Measurement, vol. 40, no. 9-10, pp. 943–950, 2007.
[17]  V. Sugumaran, V. Muralidharan, and K. I. Ramachandran, “Feature selection using decision tree and classification through proximal support vector machine for fault diagnostics of roller bearing,” Mechanical Systems and Signal Processing, vol. 21, no. 2, pp. 930–942, 2007.
[18]  B. Sreejith, A. K. Verma, and A. Srividya, “Fault diagnosis of rolling element bearing using time-domain features and neural networks,” in Proceedings of the IEEE Region 10 Colloquium and 3rd International Conference on Industrial and Information Systems (ICIIS '08), vol. 409, pp. 1–6, Kharagpur, India, December 2008.
[19]  Y. Lei, Z. He, and Y. Zi, “A new approach to intelligent fault diagnosis of rotating machinery,” Expert Systems with Applications, vol. 35, no. 4, pp. 1593–1600, 2008.
[20]  G. G. Yen and K.-C. Lin, “Wavelet packet feature extraction for vibration monitoring,” IEEE Transactions on Industrial Electronics, vol. 47, no. 3, pp. 650–667, 2000.
[21]  M. J. Fuente, D. Garcia-Alvarez, G. I. Sainz-Palmero, and T. Villegas, “Fault detection and identification method based on multivariate statistical techniques,” in Proceedings of the IEEE Conference on Emerging Technologies and Factory Automation (ETFA '09), esp, September 2009.
[22]  L. H. Chiang, M. E. Kotanchek, and A. K. Kordon, “Fault diagnosis based on Fisher discriminant analysis and support vector machines,” Computers and Chemical Engineering, vol. 28, no. 8, pp. 1389–1401, 2004.
[23]  X. C. Tang and Y. Li, “Monitoringand fault diagnosis using fisher discrimnant analysis,” in Proceedings of the 6th International Conference on Machine Learning and Cybernetics (ICMLC '07), pp. 1100–1105, Hong Kong, August 2007.
[24]  L. B. Jack and A. K. Nandi, “Fault detection using support vector machines and artificial neural networks, augmented by genetic algorithms,” Mechanical Systems and Signal Processing, vol. 16, no. 2-3, pp. 373–390, 2002.
[25]  B. Samanta, K. R. Al-Balushi, and S. A. Al-Araimi, “Artificial neural networks and support vector machines with genetic algorithm for bearing fault detection,” Engineering Applications of Artificial Intelligence, vol. 16, no. 7-8, pp. 657–665, 2003.
[26]  A. Saxena and A. Saad, “Evolving an artificial neural network classifier for condition monitoring of rotating mechanical systems,” Applied Soft Computing Journal, vol. 7, no. 1, pp. 441–454, 2007.
[27]  J. A. K. Suykens, T. Van Gestel, J. De Brabanter, B. De Moor, and J. Vandewalle, “LS-SVMlab: a MATLAB/C toolbox for least squares support vector machines,” 2002, http://www.esat.kuleuven.ac.be/sista/lssvmlab.

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