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Development of a robust inversion methodology in nondestructive eddy current testing

Abstract : The research activity of the PhD thesis focuses on the study and development of innovative strategies for the solution of inverse problems arising in the field of Non-Destructive Testing and Evaluation (NDT-NDE), based on the use of statistical learning theory. Generally speaking, the objective of the optimization stage is the retrieval of the unknown parameters within the studied electromagnetic scenario. In the case of NDT-NDE, the optimization problem, in terms of parameters to estimate, is divided into three stages, namely detection, localization and characterization. This work mainly addresses localization and characterization of crack(s) and/or estimation of probe(s) parameters. Unknown parameters, constituting a subset of the parameters set describing the electromagnetic scenario, are robustly estimated using several approaches. Standard optimization approaches are based on the minimization, by means of iterative approaches like stochastic and/or deterministic algorithms, of a cost function describing the discrepancy between measurements and prediction. This thesis considers the estimation problem in a machine learning perspective, adopting well known Learning-By-Example (LBE) paradigm. In a so-called offline phase, a surrogate inverse model is first fitted on a set of known input/output couples, generated through numerical simulations. Then, in a so-called online phase, the model predicts unknown outputs (the parameters of interest) based on new inputs (measured NDT signals) in quasi-real time. When considering practical inspection situations, due to the large number of variables involved (known as curse of dimensionality), obtaining an accurate and robust model is not a trivial task. This thesis carries out a deep and systematic study of different strategies and solutions to achieve simultaneously good accuracy and computational time efficiency in the parameters estimation. Moreover, a particular emphasis is put on the different approaches adopted for mitigating the curse of dimensionality issue. The proposed LBE schema has been tested with success on a wide set of practical problems, using both synthetic noisy data and experimental measurements.
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Shamim Ahmed. Development of a robust inversion methodology in nondestructive eddy current testing. Electromagnetism. Université Paris Saclay (COmUE), 2018. English. ⟨NNT : 2018SACLS043⟩. ⟨tel-02426006⟩

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