Optimal database combining with Multi Output Support Vector Machine for Eddy Current Testing

This paper provides a new methodology for the characterization of defect size in a conductive nonmagnetic plate from the measurement of the impedance variations. The methodology is based on Finite Element Method (FEM) combined with the Multi Output Support Vector Machines (MO-SVM). The MO-SVM is a statistical learning method that has good generalization capability and learning performance. FEM is used to create the adaptive database required to train the MO-SVM and the Cross Validation (CV) is used to find the parameters of MO-SVM model. The results show the applicability of MO-SVM to solve eddy current inverse problems instead of using traditional iterative inversion methods which can be very time-consuming. With the experimental results we demonstrate the accuracy which can be provided by the MO-SVM technique.