Complex-Valued CNN-Based Defect Reconstruction of Carbon Steel from Eddy Current Signals

Abstract

Eddy current testing (ECT) has become a widely adopted technique for non-destructive testing (NDT) due to its effectiveness in detecting surface and near-surface defects in conductive materials. However, traditional methods mainly focus on defect detection and face significant challenges in extracting geometric information such as defect size and shape, which is crucial for structural health monitoring (SHM) and remaining useful life (RUL) assessment. To address these challenges, this study proposes a defect reconstruction approach based on a complex-valued convolutional neural network (CV-CNN), which directly leverages both amplitude and phase information inherent in complex-valued impedance signals. The proposed framework employs convolution, pooling, and activation operations specifically designed within the complex-valued domain to facilitate the high-fidelity reconstruction of defect morphology as well as precise multi-class defect classification. Notably, this approach processes the complete complex-valued signal without relying on prior structural parameters or baseline data, thereby achieving substantial improvements in both defect visualization and classification performance. Moreover, when compared to a complex-valued fully convolutional neural network (CV-FCNN), CV-CNN demonstrates a superior average classification accuracy of 85%, significantly outperforming the CV-FCNN model. Experimental results on carbon steel specimens with standard electrical discharge machining (EDM) notches under multi-frequency excitation confirm these advantages. This contribution provides a promising solution in the field of NDT for intelligent and precise defect detection.