Geometry Parameters Estimation of Defects in Multi-layered Structures Based on Eddy Current Nondestructive Testing Technique with Bayesian Networks

To determine the geometry parameters of defects in multi-layered structures is one of the principal challenges in the research field of eddy current nondestructive testing. For buried defects the direct observation of the values of these geometry parameters is practically impossible. So it is necessary to estimate such values. Bayesian networks (BNs) have been proved to be a potentially useful alternative in defect geometry parameters estimation. These geometry parameters are derived from the conditional probability distributions (CPDs) estimation in BNs with experimental data. This paper describes how a novel algorithm based on BNs can be applied to successfully estimate CPDs of defect geometry parameters. In scanning inspection, the eddy current signals were preprocessed for noise elimination using wavelet packet analysis method. Then, BNs were applied to a realistic multidimensional parameters estimation problem of defect dimensioning. Finally, the estimation results were analyzed. Measurement uncertainty was generally characterized from CPDs of defect geometry parameters. The feasibility of the presented BNs has been validated.