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Apprentissage statistique pour l'évaluation et le contrôle non destructifs : application à l'estimation de la durée de vie restante des matériaux par émission acoustique sous fluage

Abstract : The composite materials are characterized by a high dispersion of their lifetime, which may extend from several minutes to several weeks in a creep test. When tested in creep of these materials we distinguish three phases, each characterized by its own acoustic activity. In the first phase, the occurrence rate of the AE signals is important, and then the rate drops to a relatively low constant value during the second phase, then this occurrence rate accelerate announcing the third phase which ends by a rupture. The characteristics of the acoustic emission (AE) signals in the phase preceding the rupture are different from those of other phases.The first part of this study is to use learning methods from artificial intelligence (neural networks, support vector machines and Bayesian classifier) to predict if the signals collected from the material under test in the pre-rupture or not. These are methods which, when applied to acoustic emission, identify among a large number of signals, characterized by key parameters, classes of signals having similar parameters and thus probably from the same phase. These methods have proved highly effective in classification; we reach the SVM with a sensitivity of 82 % and a specificity of 84 % for cross-validation results, and a sensitivity of 90 % and a specificity of 94 % for test results, with an acceptable calculation time.The second part of the study in the framework of this thesis concerns the estimation of the remaining life of composites. Standardization of signals accumulated acoustic emission curves as a function proves that the responses of the creep test pieces are set perfectly similar. A model was developed to characterize the behavior of this material during this test. Two approaches are used to determine the time of rupture. Compared to the literature, the first proposed approach improves the detection time of transition phases. This approach also provides a better correlation with the rupture time. The second approach is based on the correlation of rupture time with the reference time corresponding to the decrease of the speed by a percentage. The results of this latter approach is very interesting : the estimation of the rupture time for a test piece having a life of one hour may be possible from the first 15 seconds, with an error of about 4 %.
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Mohamad Darwiche. Apprentissage statistique pour l'évaluation et le contrôle non destructifs : application à l'estimation de la durée de vie restante des matériaux par émission acoustique sous fluage. Autre [cond-mat.other]. Université du Maine, 2013. Français. ⟨NNT : 2013LEMA1013⟩. ⟨tel-01019999⟩

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