Partial least square regression: an analysis tool for quantitative non-destructive testing

Yann Le Bihan 1 József Pávó Claude Marchand 1
1 LGEP - Laboratoire de génie électrique de Paris
Abstract : The scanning of parts in eddy current testing can lead to a large amount of measurement data (predictors). Partial least square (PLS) regression is a mean to reduce the dimensionality of the subsequent inverse problem by projecting the predictors in a latent subspace of reduced dimension maximizing the covariance between the projection and the responses which have to be estimated. In a second step, a regression model is elaborated linking the responses to the latent variables. PLS was originally developed in the field of chemical analysis. In this paper, the PLS method is applied in the field of eddy current testing for the characterization of minute cracks. It is tested firstly on simulated data and then on experimental data. It is found that the reconstruction of the area of minute cracks is made possible by PLS.
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Article dans une revue
European Physical Journal: Applied Physics, EDP Sciences, 2014, 67 (3), pp.30901. 〈10.1051/epjap/2014130487〉
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Contributeur : Thierry Leblanc <>
Soumis le : vendredi 2 janvier 2015 - 20:12:03
Dernière modification le : jeudi 29 mars 2018 - 11:06:02

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Yann Le Bihan, József Pávó, Claude Marchand. Partial least square regression: an analysis tool for quantitative non-destructive testing. European Physical Journal: Applied Physics, EDP Sciences, 2014, 67 (3), pp.30901. 〈10.1051/epjap/2014130487〉. 〈hal-01099333〉

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