Dual-Modal Fusion PRI-SWT Model for Eddy Current Detection of Cracks, Delamination, and Impact Damage in Carbon Fiber-Reinforced Plastic Materials

Abstract

Carbon fiber-reinforced plastic (CFRP) composites are prone to damage during both manufacturing and operational phases, making the classification and identification of defects critical for maintaining structural integrity. This paper presents a novel dual-modal feature classification approach for the eddy current detection of CFRP defects, utilizing a Parallel Real–Imaginary/Swin Transformer (PRI-SWT) model. Built using the Transformer architecture, the PRI-SWT model effectively integrates the real and imaginary components of sinusoidal voltage signals, demonstrating a significant performance improvement over traditional classification methods such as Support Vector Machine (SVM) and Vision Transformer (ViT). The proposed model achieved a classification accuracy exceeding 95%, highlighting its superior capability in terms of addressing the complexities of defect detection. Furthermore, the influence of key factors—including the real–imaginary fusion layer, the number of layers, the window shift size, and the model’s scale—on the classification performance of the PRI-SWT model was systematically evaluated.