Browse > Article

Structural Quality Defect Discrimination Enhancement using Vertical Energy-based Wavelet Feature Generation  

Kim, Joon-Seok (Department of Business Administration, School of Business, Sejong University)
Jung, Uk (Department of Management, School of Business, Dongguk University)
Publication Information
Journal of Korean Society for Quality Management / v.36, no.2, 2008 , pp. 36-44 More about this Journal
Abstract
In this paper a novel feature extraction and selection is carried out in order to improve the discriminating capability between healthy and damaged structure using vibration signals. Although many feature extraction and selection algorithms have been proposed for vibration signals, most proposed approaches don't consider the discriminating ability of features since they are usually in unsupervised manner. We proposed a novel feature extraction and selection algorithm selecting few wavelet coefficients with higher class discriminating capability for damage detection and class visualization. We applied three class separability measures to evaluate the features, i.e. T test statistics, divergence, and Bhattacharyya distance. Experiments with vibration signals from truss structure demonstrate that class separabilities are significantly enhanced using our proposed algorithm compared to other two algorithms with original time-based features and Fourier-based ones.
Keywords
Class separability; Damage detection; Feature generation; Fourier transform; Wavelet transform;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Jung, U., Jeong, M. K. and Lu, J, C. (2006), A vertical-energy-thresholding procedure for data reduction with multiple complex curves, IEEE Transactions on Systems, Man, and Cybernetics-Part B, 36(5), 1128-1138   DOI   ScienceOn
2 Guyon, I. and Elissee, A. (2003), An introduction to variable and feature selection, Journal of Machine Learning Research, 3, 1157-1182   DOI
3 Mallat, S. G. (1989), A Wavelet Tour of Signal Processing, Academic Press, San Diago
4 Sohn, H., Farrar, C.R., Hemez, F.M., Shunk, D.D., Stinemates, D.W., and Nadler, B.R. (2003), A review of structural health monitoring literature: 1996-2001, Technical Reports LA-13976-MS, Los Alamos National Laboratory
5 Antoniadis, A., Gijbels, I., and Gregoire, G. (1997), Model selection using wavelet decomposition and applications, Biometrika, vol. 42, no. 4, pp. 751-763
6 Duda, R. O., Hart,P.E. and Stork, D.G. (2001), Pattern Classification, 2nd edn., Wiley, New York, NY
7 Lie, J.S., Zhang, J.L., Palumbo, M.J. and Lawrence, C.E. (2003), Bayesian clustering with variable and transformation selection, Bayesian Statistics, 7, 249-275
8 Li,H., Deng,X., and Dai,H. (2007), Structural damage detection using the combination method of EMD and wavelet analysis, Mechanical Systems and Signal Processing, 21, 298-306   DOI   ScienceOn
9 Ambardar, A. (1995), Analog and Digital Signal Processing, PWS Publishing Company, Boston
10 Law,S.S., Li,X.Y., Zhu,X.Q., and Chan,S.L. (2005), Structural damage detection from wavelet packet sensitivity, Engineering Structures, 27, 1339-1348   DOI   ScienceOn
11 Swamidas, A.S.J. and Chen, Y. (1995), Monitoring crack growth through change of modal parameters, Journal of Sound and Vibration, Vol.186, No.2, 325-343   DOI   ScienceOn
12 Li,Z., Xia,S., Wang,J. and Su,X.(2006), Damage detection of cracked beams based on wavelet transform, International Journal of Impact Engineering, 32, 1190-1200   DOI   ScienceOn
13 Palacz, M., and Krawczuk, M. (2002), Vibration parameters for damage detection in structures, Journal of Sound and Vibration, Vol.249, No.5, 999-1010   DOI   ScienceOn