A Kernel Based Learning by Sample Technique for Defect Identification Through the Inversion of a Typical Electric Problem

The main purpose of a Non Destructive Evaluation technique is to provide information about the presence/absence, Within this framework, it is very important to automatically detect and characterize defect minimizing the indecision about measurements. This paper just treats an inverse electrostatic problem, with the aim of detecting and characterizing semi-spherical defects (i.e. superficial defects) on metallic plates. Its originality consists on the proposed electromagnetic way exploited to a non destructive inspection of specimens as well as on the use of a Support Vector Regression Machine based approach in order to characterize the detected defect. The experimental results show the validity of the proposed processing.