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

Marco Salucci, Nicola Anselmi, Giacomo Oliveri, Paolo Rocca, Shamim Ahmed, Pierre Calmon, Roberto Miorelli, Christophe Reboud and Andrea Massa

Journal of Electromagnetic Waves and Applications, 2019, vol. 33, issue 6, 669-696

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.

Date: 2019
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DOI: 10.1080/09205071.2019.1572546

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