Robust Estimation of Hidden Corrosion Parameters Using an Eddy Current Technique

Abstract

An eddy current technique is used to inspect the interface between air and a conductive material such as aluminum, which can be covered with a non-conductive material. Hidden corrosion may appear inside the conductive material. This corrosion leads to flaws whose shape varies greatly depending of the flaw. The proposed methodology addresses this problem by considering the potential shapes as realizations of a random process. The goal of the proposed approach is not to find the exact shape of the corrosion flaw but to estimate some of its dimensional parameters. The area and the dimension ratio of the shape have been chosen because they depict the importance of the corrosion damage.The estimation of the area and the dimension ratio is achieved in a Nondestructive Evaluation context: An alternating magnetic field is created in the air above the inspected material and the magnetic field near the air-aluminum interface is measured. It is a typical inverse measurement problem. Due to the complexity of the shape and of the physical phenomena, no algebraic model exists to solve this inverse problem. That is why a machine learning approach has been carried out: A database of observed signals for reference flaws is created (by using FEM tool) and used to calibrate a relationship giving the estimated area and the estimated dimension ratio from the observed signal. As the number of flaws in the database cannot be very large, the proposed approach overcomes the overfitting risk by performing a reduction of the data dimension.