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Proceedings Paper

Improvement of defect detection in shearography by using principal component analysis
Author(s): Jean-François Vandenrijt; Nicolas Lièvre; Marc P. Georges
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Paper Abstract

A post-processing technique based on principal components analysis (PCA) is proposed for shearography for defect detection. PCA allows decomposing a time series of images into a set of images called Empirical Orthogonal Functions (EOF), each showing features with a given variability in the time series. We have applied PCA on composite samples containing various defects at different depths and which undergo transient thermal wave. Analyzing the temporal series shows the shallow defects appearing first whereas the deeper ones appear later. With PCA all the defects appear in one or two of the EOF, easing the identification of defects.

Paper Details

Date Published: 18 August 2014
PDF: 6 pages
Proc. SPIE 9203, Interferometry XVII: Techniques and Analysis, 92030L (18 August 2014); doi: 10.1117/12.2062831
Show Author Affiliations
Jean-François Vandenrijt, Univ. de Liège (Belgium)
Nicolas Lièvre, Univ. de Liège (Belgium)
Marc P. Georges, Univ. de Liège (Belgium)

Published in SPIE Proceedings Vol. 9203:
Interferometry XVII: Techniques and Analysis
Katherine Creath; Jan Burke; Joanna Schmit, Editor(s)

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