Heavy-haul railways are critical for transporting freight. However, prolonged wheel–rail interactions cause frequent rail defects, particularly in small-radius curve sections. Ultrasonic A-scan signals are essential for the non-destructive evaluation of internal rail defects. In real heavy-haul environments, these signals suffer from strong non-Gaussian coupled noise. Such noise includes structural noise, low-frequency irrelevant components, and high-frequency electrical noise. Noise aliasing obscures defect echoes and increases the risk of missed detections. Conventional denoising methods are limited by poor noise–signal separability, mode mixing, and inadequate adaptability to complex non-Gaussian signals. To address these challenges, an A-scan signal model under noise-coupled conditions is constructed by analyzing the statistical and time–frequency characteristics of different noise components. Based on this model, a multi-feature fusion filtering framework is developed within the ideal binary mask (IBM) paradigm. This framework is designed to enhance defect echo extraction from ultrasonic A-scan signals under strong non-Gaussian interference. Tests on field inspection data show that the proposed method effectively suppresses coupled noise and achieves accurate extraction of defect echoes.
Zhang et al. (2026) studied this question.