A nonlinear Kernel-based adaptive learning-by-examples method for robust NDT/NDE of conductive tubes - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue Journal of Electromagnetic Waves and Applications Année : 2019

A nonlinear Kernel-based adaptive learning-by-examples method for robust NDT/NDE of conductive tubes

Résumé

In this work, the real-time non-destructive testing and evaluation (NDT/NDE) of faulty conductive tubes from eddy current (EC) measurements is addressed and solved in a computationally efficient way by means of an innovative learning-by-examples (LBE) methodology. More specifically, the estimation of the descriptors of a defect embedded within the cylindrical structure under test (SUT) is yielded by combining a non-linear feature extraction technique with an adaptive sampling strategy able to uniformly explore the arising feature space. Predictions are then performed during the on-line testing phase by means of a support vector regression (SVR). Representative results from a numerical/experimental validation are reported to assess the effectiveness of the proposed approach also in comparison with competitive state-of-the-art approaches.
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Dates et versions

hal-02082846 , version 1 (28-03-2019)

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Marco Salucci, Nicola Anselmi, Giacomo Oliveri, Paolo Rocca, Shamim Ahmed, et al.. A nonlinear Kernel-based adaptive learning-by-examples method for robust NDT/NDE of conductive tubes. Journal of Electromagnetic Waves and Applications, 2019, 33 (6), pp.669-696. ⟨10.1080/09205071.2019.1572546⟩. ⟨hal-02082846⟩
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