Inspection of Defects Depth for Stainless-Steel Sheets Using 4-coil Excitation Sensor and Deep Learning

The detection of lift-off variation presents a significant challenge in many eddy current testing (ECT) applications, where it is often considered an undesirable signal. To address this issue, the paper proposes a novel 4-coil excitation sensor, which can effectively suppress the lift-off effect when detecting defects of varying depths in planar structures. The proposed sensor design leverages opposite excitation directions between any two arbitrary adjacent excitation coils, which can effectively cancel out the lift-off signal due to a defect-free plate. However, this 4-coil excitation design has an angular sensitivity – i.e., different signal response for different angles between the same defect and the sensor. This angular sensitivity will cause trouble to identify the depth of defects without knowing the angle in prior. To overcome this challenge, the current study introduces deep learning (DL) models as a potential solution. Signals representing different defect depths and angles between the sensor and the defect are acquired using an experimental platform in our lab. The real and imaginary parts of the induced voltage on the receiver coil labelled with depths and angles are utilized to train a 1-D convolutional neural network (1D CNN) model, in which different convolution kernel sizes are used to extract more information and speed up convergence. The high performance of the proposed structure of 1D CNN were compared with convolutional long-short term memory (LSTM) model, recurrent neural network (RNN) model, LSTM+RNN model and ResNet18 model. The proposed 1D CNN model not only achieves good performances with a higher accuracy of 99.8% but also requires a smaller model size and less computational time.