Crack depth estimation of non-magnetic material by convolutional neural network analysis of eddy current testing signal

When a heat transfer tube of the steam generator of a pressurized water reactor fails, the primary cooling water leaks quickly into the secondary system. Moreover, if this leakage is large, the nuclear reactor emergency core cooling system (ECCS) may be activated. In Japan, to prevent such situation to take place, periodic inspections are performed in order to check whether heat transfer tubes are cracked. Eddy Current Testing (ECT) is a type of non-destructive inspection method used to detect cracks in a conductive material. ECT can estimate the shape of a crack by inverse problem analysis, but it is computationally expensive. Therefore, in this study, we aimed to develop a method to estimate crack depth by Convolutional Neural Network (CNN). The method was shown to be less computationally expensive during estimation and was robust against lift-off fluctuation during measurements.