Real-Time NDT-NDE Through an Innovative Adaptive Partial Least Squares SVR Inversion Approach

Abstract

The real-time retrieval of the characteristics of a defect with eddy current testing in a nondestructive testing and evaluation framework is addressed. An innovative statistical learning approach is developed to deal with the inversion problem at hand in a computationally efficient way. More in detail, a feature extraction technique based on partial least squares (PLS) is profitably combined with a customized output space filling (OSF) adaptive sampling scheme for generating optimal training databases, while accurate and robust reconstructions are performed with a support vector regression (SVR) algorithm. A selected set of numerical and experimental results is reported to assess the effectiveness as well as the efficiency of the proposed PLS-OSF/SVR approach.