Purpose:This paper presents a data processing methodology combining kernel change detection (KCD) and efficient global optimization (EGO) algorithms for solving inverse problem in eddy current non-destructive testing (EC-NDT). The main purpose is to reduce the computation cost of EC data inversion that is essentially due to the heavy forward modelling with finite element method (FEM) and the non-linearity of the parameter estimation problem.Design/methodology/approachThe KCD algorithm is adapted and applied to detect damaged parts in an inspected conductive tube using probe impedance signal. The localization step allows to reduce the number of measurement data that will be processed for estimating the flaw characteristics using a global optimization algorithm (EGO). Actually, the minimized objective function is calculated from data related to defect detection indexes provided by KCD.FindingsSimulation results show the efficiency of the proposed methodology in terms of defect detection and localization, a significant reduction of computing time is obtained in the step of defect characterization.Originality/valueThe combination of a change detection method (KCD) with a global optimization algorithm (EGO) for defect detection and characterization. To show that such approach allows to reduce the numerical cost of ECT data processing.