Classification of Defects by the SVM Method and the Principal Component Analysis (PCA)

Analyses carried out on examples of detected defects echoes showed clearly that one can describe these detected forms according to a whole of characteristic parameters in order to be able to make discrimination between a planar defect and a volumic defect. This work answers to a problem of ultrasonics NDT like Identification of the defects. The problems as well as the objective of this realized work, are divided in three parts: Extractions of the parameters of wavelets from the ultrasonic echo of the detected defect - the second part is devoted to principal components analysis (PCA) for optimization of the attributes vector. And finally to establish the algorithm of classification (SVM, Support Vector Machine) which allows discrimination between a plane defect and a volumic defect. We have completed this work by a conclusion where we draw up a summary of the completed works, as well as the robustness of the various algorithms proposed in this study.

Authors:

• M. Khelil
• M. Boudraa
• A. Kechida
• R. Drai

Keywords:

• NDT
• PCA
• SVM
• ultrasonics
• wavelet

References:

[1] R. Drai, M. Khelil and A. Benchaala "Time-frequency and wavelet transform applied to selected problems in ultrasonics NDE ".NDT & E
International Volume 35, Issue 8, Décembre 2002, Pages 567-572.
[2] A.sophian & G.tian & D.taylor & J.rudlin A "feature extraction technique based on pricipal component analysis for pulsed Eddy current NDT". NDT&E international 36 (2003) 37-41.
[3] Olivier bousquet "Introduction aux Support Vector machine"(SVM).
Orsay, 15 Novembre 2001
[4] R. Polikar & T. Taylor & L. Udpa & S Udpa "frequency invariant
classification of ultrasonic weld inspection signals." IEEE transactions
on ultrasonics, ferroelectrics, and frequency control. Vol. 45, may1998.
[5] Drai R, Khelil M & Benchaala A, "Elaboration of some signal
processing algorithms in ultrasonic techniques : Application to
materials NDT.". ULTRASONICS, VOL.38 (1-8) 2000, pp.503-507
[6] Burrus, C.S., Gopinath, R.A. & Guo, H. "Introducing to wavelets and
wavelet transformation", Prentice-Hall, 1998
[7] Mallat, S., "A Theory for Multiresolution Signal Processing: The
Wavelet Representation," IEEE Transactions on Pattern Analysis and
Machine Intelligence, July 1989, Vol. 11, pp 674-693.
[8] Rashmi, M., Bilgutay N. M. and Kagan Kaya, O. "Detection of
ultrasonic anomaly signals using wavelet decomposition." Materials
evaluation (Nov. 1997), 1274-1279.