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Advanced statistical learning method for multi-physics NDT-NDE

Abstract : This work presents an innovative multi-physics (MP) Learning-by-Examples (LBE) inversion methodology for real-time non-destructive testing (NDT). Eddy Current Testing (ECT) and Ultrasonic Testing (UT) data are effectively combined to deal with the localization and characterization of a crack inside a conductive structure. An adaptive sampling strategy is applied on ECT-UT data in order to build an optimal (i.e., having minimum cardinality and highly informative) training set. Support vector regression (SVR) is exploited to obtain a computationally-efficient and accurate surrogate model of the inverse operator and, subsequently, to perform real-time inversions on previously-unseen measurements provided by simulations. The robustness of the proposed MP-LBE approach is numerically assessed in presence of synthetic noisy test set and compared to single-physic (i.e., ECT or UT) inversion.
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Contributor : Andrea Massa <>
Submitted on : Thursday, December 20, 2018 - 5:22:37 PM
Last modification on : Wednesday, April 8, 2020 - 4:01:48 PM

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Shamim Ahmed, Pier Calmon, Roberto Miorelli, Christophe Reboud, Andrea Massa. Advanced statistical learning method for multi-physics NDT-NDE. Journal of Physics: Conference Series, IOP Publishing, 2018, 1131, pp.1-7. ⟨10.1088/1742-6596/1131/1/012012⟩. ⟨hal-01962666⟩



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