Feature extraction and gating techniques for ultrasonic shaft signal classification

Applied Soft Computing Journal

Volumn 7, Issue 1, 2007, Pages 156-165

  Lee, Kyungmi ^a   Estivill Castro, Vladimir ^a  

^a GRIFFITH UNIVERSITY   (Australia)

Author keywords

Feature extraction; Non destructive evaluation; Pattern classification; Signal processing

Indexed keywords

NEURAL NETWORKS; PATTERN RECOGNITION; SIGNAL PROCESSING; TIME DOMAIN ANALYSIS; ULTRASONIC APPLICATIONS; WAVELET TRANSFORMS;

DISCRETE WAVELET TRANSFORM (DWT) COEFFICIENTS; NON-DESTRUCTIVE EVALUATION; PREPROCESSING TECHNIQUES; ULTRASONIC TEST SIGNALS;

FEATURE EXTRACTION;

EID: 33750966138     PISSN: 15684946     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.asoc.2005.05.003     Document Type: Article

References (20)

1. Cristianini N., and Shawe-Taylor J. An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods (2000), Cambridge University Press
2. Daubechies I. Orthogonal bases of compactly supported wavelets. Commun. Pure Appl. Math. 41 (1988) 909-996
3. El-Naqa I., Yang Y., Wernick M., Galatsanos N., and Nishikawa R. A support vector machine approach for detection of microcalcifications. IEEE Trans. Med. Imag. 21 (2002) 1552-1563
4. Gold C., and Sollich P. Model selection for support vector machine classification. Neurocomputing 55 (2003) 221-249
5. Chih-Wei Hsu, Chih-Chung Chang, Chih-Jen Lin A practical guide to support vector classification. Available at http://www.csie.ntu.edu.tw/∼cjlin/papers/guide/guide.pdf.
6. Iakovidis D.K., Maroulis D.E., Karkanis S.A., and Flaounas I.N. Color texture recognition in video sequences using wavelet covariance features and support vector machines. Proceedings of the 29th Euromicro Conference (2003) 199-205
7. Karras D.A., Karkanis S.A., Iakovidis D., Maroulis D.E., and Mertzios B.G. Support vector machines for improved defect detection in manufacturing using novel multidimensional wavelet feature extraction involving vector quantization and PCA techniques. Proceedings of NIMIA Advanced Study Institute on Neural Networks for Instrumentation, Measurement and Related Industrial Applications (2001) 139-144
8. Katragadda G., Nair S., and Singh G.P. Neuro-fuzzy systems in ultrasonic weld evaluation. Rev. Progr. Quant. Nondestructive Eval. 16 (1997) 765-772
9. Sathiya S., and Keerthi. Efficient tuning of SVM hyperparameters using RadiusMargin bound and iterative algorithms. IEEE Trans. Neural Networks 13 September (5) (2002) 1225-1229
10. Lee K., and Estivill-Castro V. Classification of ultrasonic shaft inspection data using discrete wavelet transform. Proceedings of the Third IASTED International Conferences on Artificial Intelligence and application (2003), ACTA Press 673-678
11. Obaidat M.S., Suhail M.A., and Sadoun B. An intelligent simulation methodology to characterize defects in materials. Inf. Sci. 137 (2001) 33-41
12. Ogaji S., and Singh R. Advanced engine diagnostics using artificial neural networks. Appl. Soft Comput. 3 November (3) (2003) 259-271
13. Osuna E., Freund R., and Girosi F. Training support vector machines:an application to face detection: an application to face detection. Proceedings of Conference on Computer Vision and Pattern Recognition (CVPR 97) (1997) 130-136
14. Pittner S., and Kamarthi S.V. Feature extraction from wavelet coefficients for pattern recognition tasks. IEEE Trans. Pattern Anal. Mach. Intell. 21 January (1) (1999) 83-88
15. Polikar R., Udpa L., Udpa S.S., and Taylor T. Frequency Invariant Classification of Ultrasonic Weld Inspection Signals. IEEE Trans. Ultrason. Ferroelectr. Freq. Control 45 May (3) (1998) 614-625
16. Matthias Seeger Bayesian model selection for Support Vector machines, Gaussian processes and other kernel classifiers. In Proceedings of the Neural Information Processing Systems, vol. 12, MIT Press, pp. 603-609, 2000.
17. Song S.J., Kim H.J., and Lee H. A systematic approach to ultrasonic pattern recognition for real-time intelligent flaw classification in weldments. Rev. Progr. Quant. Nondestructive Eval. 18 (1999) 865-872
18. Mekdeci Tansel C., and McLaughlin I.N.C. Detection of Tool Failure in End Milling with Wavelet Transformations and Neural Networks (WT-NN). Mach. Tools Manuf. 35 August (1) (1995) 137-147
19. Tong S., and Chang E. Support vector machine active learning for image retrieval. Proceedings of the Ninth ACM International Conference on Multimedia (2001), ACM Press 107-118
20. Zhu F., Guan S.U., and Li P. Feature selection for modular GA-based classification. Appl. Soft Comput. 4 September (4) (2004)