NDTnet 1998 Aug, Vol.3 No.8

Full-Text - Abstract

A method for detection and classification of defects originating from rivets holes in a riveted lapjoint using eddy current (EC) inspection is presented in this paper. The EC-signals where acquired using a tailor-made deep penetrating probe, and a digital single frequency EC-instrument from a number of lapjoints. A number of rivets in the lapjoints had artificial defects located in the second layer of the lapjoint, some with known geometry, and some unknown. The problem consisted in detecting and classifying the flaw or defect signals in presence of strong interference due to rivet responses. The preprocessing before detection consisted of median filtering, a sliding window algorithm and an optimal rotation of the eddy current pattern. The rotation was performed so that the signal energy from the rivet responses was maximized along the quadrature direction, and the defect response energy was maximized in the in-phase direction of the EC-signal. Feature extraction was then performed using waveless and block-mean values of the defect signal. The classification was performed using a standard multi-layer perceptron neural network.

Abstract Source:
Book of Abstracts, 7th European Conference on Non-Destructive Testing, 26-29 May 1998, ISBN: 87-986898-0-00

Full-Text Source:
Proceedings of the 7th European Conference on Non-Destructive Testing, 26-29 May 1998, ISBN:

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