A load-enhanced methodology has been proposed to enable the in situ detection, localization, and characterization of damage in metallic plate-like structures using Lamb waves. A baseline-free load-differential method using the delay-and-sum imaging algorithm is proposed for defect detection and localization. The term “load-differential” refers to the comparison of recorded ultrasonic signals at various levels of stress. Defect characterization is achieved by incorporating expected scattering information of guided waves interacting with defects into the minimum variance imaging algorithm, and a method for estimating such scattering patterns from the measurements of a sparse transducer array is developed. The estimation method includes signal preprocessing, extracting initial scattering values from baseline subtraction results, and obtaining the complete scattering matrix by applying radial basis function interpolation. The factors that cause estimation errors, such as the shape parameter used to form the basis function and the filling distance used in the interpolation, are discussed. The estimated scattering patterns from sparse array measurements agree reasonably well with laser wavefield data and are further used in the load-enhanced method. The results from fatigue tests show that the load-enhanced method is capable of detecting cracks, providing reasonable estimates of their localizations and orientations, and discriminating them from drilled holes, disbonds, and fastener tightness variations.

Cette thèse est consacré aux tests d'adéquation basés sur la fonction caractéristique. Nous débutons en présentant et en complétant les résultats probabilistes nécessaires à la construction de statistiques de test prenant la fonction caractéristique et son pendant la fonction caractéristique empirique comme représentations respectives des lois de référence et de la loi inconnue de l'échantillon de vecteurs aléatoires à tester. Nous poursuivons le travail en faisant la revue et en classant les tests basés sur la fonction caractéristique existants. Nous élaborons ensuite une classe de statistiques de test s'appuyant sur le calcul d'une distance intégrale. Le cas de la distance L2 est étudié plus à fond, car nous avons pu établir des résultats asymptotiques dans ce dernier cas. Ceux-ci font intervenir les éléments propres inconnus d'un opérateur intégral. Nous présentons, puis utilisons, une méthode d'approximation spectrale basée sur une projection de l'opérateur sur une base orthonormée.Finalement, nous construisons une nouvelle classe de tests appartenant au paradigme des tests lisses de Neyman. L'étude précédente nous permet de simplifier considérablement la construction de ces tests, dont différentes versions sont proposées tant pour le test d'une hypothèse simple que pour le test d'une hypothèse composite. / This PhD thesis consists in building goodness-of-fit tests using the characteristic function (CF) as a prefered representation for the probability laws involved.We start with listing and improving results in probability theory necessary to build test statistics using the characteristic function and its conterpart the empirical characteristic function.We list and classify existing characteristic function based goodness-of-fit tests published by varions authors since 1977.Then, we build a class of tests based on integral metrics. We take particular attention to the case where the statistics are build using a L2 distance. More specifically, we give asymptotic results in this case. However, these results reveal the need for information on the unknown eigenelements of an integral operator. Thus, we present and implement an approximation method using a sequence of projections on orthonormal bases ofan hilbertian functional space.Finally, we will build another class of tests using the Neyman smooth test paradigm. This study is based on our previous results, that fit well into the construction of characteristic function based smooth tests. We give various applications, presenting tests for both a simple hypothesis and a composite hypothesis.