Hidden Defects Diagnosis Using Parameter Optimization

Abstract:

In this paper, the issue of composite defects diagnosis by applying the support vector machine (SVM) was addressed. The component analysis was performed initially to extract the features and to reduce the dimensionality of original data features. Kernel parameters selection of support vector machine which has great influence on the performance of defects classification has been discussed in this work. Precisely, we focus on wavelet transform to extract the feature from the original signals, adopt component analysis to do feature selection and apply support vector machine to classify the defects. This paper exploits the parameter optimization procedure to ensure the generalization ability of SVM. The result shows that multi-class SVM produces promising results and has the potential for use in fault diagnosis.

• Periodical: Applied Mechanics and Materials (Volumes 152-154)
• Pages: 1691-1697
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.152-154.1691
• Online since: January 2012
• Authors: X. C. Wen, W.Y. Leong
• Keywords: Kernel Parameter, Pitch-Catch Method, Principal Component Analysis (PCA), Support Vector Machine (SVM), Wavelet Transform (WT)

References

[1] Huang YD, Froyen L, Wevers M. Quality control and nondestructive test in metal matrix composites. J Nondestr Eval 2001; 20(3): 113–32.J. Clerk Maxwell, A Treatise on Electricity and Magnetism, 3rd ed., vol. 2. Oxford: Clarendon, 1892, p.68–73.

[2] Vary, A., Acoustic-ultrasonics: An update, in Acoustic Emission: Current Practice and Future Directions (Sachse, W., Roget, J., and Yamaguchi, K., eds. ), pp.95-104, (1989).

DOI: 10.1520/stp19087s

[3] Kautz, H. E., Acoustic-ultrasonics to assess material and structural proper- ties, tech. rep., NASA/CR-2002-211881, (2002).

[4] Kwon, O. -Y. and Lee, S. -H., Acoustic-ultrasonic evaluation of adhesively bonded CFRP-aluminum joints, NDT&E International, vol. 32, pp.153-160, (1999).

DOI: 10.1016/s0963-8695(98)00066-8

[5] Widodo, A., & Yang, B. S. (2007). Application of nonlinear feature extraction and support vector machines for fault diagnosis of induction motor. Expert Systems with Applications, 33(1), 241-250.

DOI: 10.1016/j.eswa.2006.04.020

[6] D. Meyer, F. Leisch, K. Hornik, The support vector machine under test, Neurocomputing 55 (2003) 169–186.

DOI: 10.1016/s0925-2312(03)00431-4

[7] V. Kecman, Learning and Soft Computing, Support Vector Machines, Neural Networks and Fuzzy Logic Models, The MIT Press, Cambridge, MA, USA, (2001).

DOI: 10.1016/s0925-2312(01)00685-3

[8] Bottou, L., Cortes, C., Denker, J., Drucker, H., Guyon, I., Jackel, L., et al. (1994).

[9] KreBel, U. (1999). Pairwise classification and support vector machines. In Advances in kernel methods: Support vector learning (p.255–268). Cambridge, MA: MIT Press.

DOI: 10.7551/mitpress/1130.003.0020

[10] Vapnik, V. (1998). Statistical learning theory. New York: Wiley.

[11] Hsu, C. W., & Lin, C. J. (2002). A comparison of methods for multi-class support vector machines. IEEE Transactions on Neural Networks, 13(2), 415–425.

DOI: 10.1109/72.991427

[12] Chih-Wei Hsu, Chih-Chung Chang, Chih-Jen Lin, A Practical Guide to Support Vector Classification, Technical Report, Department of Computer Science, National Taiwan University, (2008).

DOI: 10.1109/72.857780

[13] Y.H. Lu, J.E. Michaels, Feature Extraction and Sensor Fusion for Ultrasonic Structural Health Monitoring Under Changing Environmental Conditions, IEEE Sensors Journal, Vol. 9,  Iss. 11, Nov. 2009 pp.1462-1471.

DOI: 10.1109/jsen.2009.2019339