DiffUT: Diffusion-based augmentation for limited ultrasonic testing defects in high-speed rail

Abstract

Ultrasonic testing is a widely used nondestructive testing (NDT) method for detecting defects in critical industrial components. However, ultrasonic defect detection in high-speed rail (HSR) systems faces significant challenges due to limited sample availability and complex working conditions. These limitations often lead to subjective judgments by inspectors, increasing the risk of false positives and missed detections. To mitigate data scarcity, this study introduces a diffusion model for data augmentation, applied to real ultrasonic B-scan wheel defect data. By learning the probability and noise distribution through diffusion and reverse diffusion processes, the model generates synthetic data to improve detection accuracy. Experimental results show notable improvements in average precision and recall, increasing from 78.0 % to 66.0 %–93.3 % and 91.5 %, respectively. This method has been successfully deployed in practical applications, with plans for continuous updates as new data becomes available. The study addresses the challenge of limited defect data in industrial NDT and highlights the potential for broader applications in automated defect detection systems.