Improvement of defect detection in shearography by using principal component analysis

Jean-François Vandenrijt; Nicolas Lièvre; Marc P. Georges

doi:10.1117/12.2062831

18 August 2014 Improvement of defect detection in shearography by using principal component analysis

Jean-François Vandenrijt, Nicolas Lièvre, Marc P. Georges

Proceedings Volume 9203, Interferometry XVII: Techniques and Analysis; 92030L (2014) https://doi.org/10.1117/12.2062831
Event: SPIE Optical Engineering + Applications, 2014, San Diego, California, United States

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.

Citation Download Citation

Jean-François Vandenrijt, Nicolas Lièvre, and Marc P. Georges "Improvement of defect detection in shearography by using principal component analysis", Proc. SPIE 9203, Interferometry XVII: Techniques and Analysis, 92030L (18 August 2014); https://doi.org/10.1117/12.2062831

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