Ultrasonic Diagnosis for Internal Defects in Vehicle Cable Terminals Using Improved Gramian Angular Field and CBAM-EfficientNetB0

Vehicle cable terminals (VCTs) are crucial for ensuring a continuous and reliable power supply to high-speed trains (HSTs). Ultrasonic testing is a promising technique for diagnosing internal defects in VCTs. However, the non-intuitive features and limited distinguishability of time-domain ultrasonic signals (USs) hinder the accurate identification of defect types. To address this issue, we first proposed an improved Gramian angle field (IGAF) method to convert US data into 2-D images. This approach integrates the effective features of the traditional Gramian angle summation field (GASF) and Gramian angle difference field (GADF) into a single image, thereby enhancing data representation and eliminating redundant information caused by pattern symmetry in conventional methods. We then developed a model named CBAM-EfficientNetB0, which integrates convolutional block attention modules (CBAMs) to enhance feature extraction and representation capabilities. Finally, we proposed a combined method that utilizes IGAF images generated from US data alongside the CBAM-EfficientNetB0 model for the accurate diagnosis of internal defects in VCTs. The results indicate that the proposed combined method achieves a defect diagnosis accuracy of 95.8%, which is 5.6% and 4.3% higher than the GASF and GADF methods, respectively, and surpasses the original EfficientNetB0 by 1.3%. Furthermore, we developed a portable US measurement device, facilitating the application of the proposed technique for intelligent detection in practical scenarios.