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

Abstract : 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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https://hal.archives-ouvertes.fr/hal-02082846
Contributor : Andrea Massa <>
Submitted on : Thursday, March 28, 2019 - 2:59:00 PM
Last modification on : Saturday, March 30, 2019 - 1:58:50 AM

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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, Taylor & Francis, 2019, 33 (6), pp.669-696. ⟨10.1080/09205071.2019.1572546⟩. ⟨hal-02082846⟩

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