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

Author

Listed:
  • Marco Salucci
  • Nicola Anselmi
  • Giacomo Oliveri
  • Paolo Rocca
  • Shamim Ahmed
  • Pierre Calmon
  • Roberto Miorelli
  • Christophe Reboud
  • Andrea Massa

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.

Suggested Citation

  • Marco Salucci & Nicola Anselmi & Giacomo Oliveri & Paolo Rocca & Shamim Ahmed & Pierre Calmon & Roberto Miorelli & Christophe Reboud & Andrea Massa, 2019. "A nonlinear Kernel-based adaptive learning-by-examples method for robust NDT/NDE of conductive tubes," Journal of Electromagnetic Waves and Applications, Taylor & Francis Journals, vol. 33(6), pages 669-696, April.
    • Handle: RePEc:taf:tewaxx:v:33:y:2019:i:6:p:669-696
    DOI: 10.1080/09205071.2019.1572546
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