A Noise-Robust Deep-Learning Framework for Weld-Defect Detection in Magnetic Flux Leakage Systems

Magnetic flux leakage (MFL) inspection systems are widely used for detecting pipeline defects in industrial sites. However, the acquired MFL signals are affected by field noise, such as electromagnetic interference and mechanical vibrations, which degrade the performance of the developed models. In addition, the noise type or intensity is unknown or changes dynamically during the test phase in contrast to the training phase. To address the above challenges, this paper introduces a novel noise-robust deep-learning framework to remove the noise component in the original signal and learn its noise-invariant feature representation. This can handle the unseen noise pattern and mitigate the impact of dynamic noises on MFL inspection systems. Specifically, we propose a transformer-based architecture for denoising, which encodes noisy input signals into a latent space and reconstructs them into clean signals. We also devise an up–down sampling denoising block to better filter the noise component and generate a noise-invariant representation for weld-defect detection. Finally, extensive experiments demonstrate that the proposed approach effectively improves detection accuracy under both static and dynamic noise conditions, highlighting its value in real-world industrial applications.