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.

A nonlinear Kernel-based adaptive learning-by-examples method for robust NDT/NDE of conductive tubes / Salucci, Marco; Anselmi, Nicola; Oliveri, Giacomo; Rocca, Paolo; Ahmed, Shamim; Calmon, Pierre; Miorelli, Roberto; Reboud, Christophe; Massa, Andrea. - In: JOURNAL OF ELECTROMAGNETIC WAVES AND APPLICATIONS. - ISSN 0920-5071. - STAMPA. - 33, 2019:6(2019), pp. 669-696. [10.1080/09205071.2019.1572546]

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

Salucci, Marco;Anselmi, Nicola;Oliveri, Giacomo;Rocca, Paolo;Massa, Andrea
2019

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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Salucci, Marco; Anselmi, Nicola; Oliveri, Giacomo; Rocca, Paolo; Ahmed, Shamim; Calmon, Pierre; Miorelli, Roberto; Reboud, Christophe; Massa, Andrea
A nonlinear Kernel-based adaptive learning-by-examples method for robust NDT/NDE of conductive tubes / Salucci, Marco; Anselmi, Nicola; Oliveri, Giacomo; Rocca, Paolo; Ahmed, Shamim; Calmon, Pierre; Miorelli, Roberto; Reboud, Christophe; Massa, Andrea. - In: JOURNAL OF ELECTROMAGNETIC WAVES AND APPLICATIONS. - ISSN 0920-5071. - STAMPA. - 33, 2019:6(2019), pp. 669-696. [10.1080/09205071.2019.1572546]
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11572/231970
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