Unsupervised and few-shot based temperature compensation of pipeline ultrasonic-guided waves via a rolling framework

Abstract

In structural health monitoring, guided wave-based techniques are highly sensitive to environmental temperature, leading to spurious signal variations that obscure damage features and compromise localization accuracy. This article introduces a robust unsupervised and few-shot temperature compensation framework that achieves accurate signal reconstruction and reliable damage localization under diverse thermal conditions. The proposed approach requires only a limited number of baseline measurements, and avoids reliance on labeled damage data. First, a rolling pairing scheme is developed to expand training diversity from small datasets, enabling the model to learn cross-temperature relationships in an unsupervised manner. Second, four temperature-embedding operators are formulated to explicitly incorporate thermal information into the latent space. Finally, a channel-weighting mechanism adaptively emphasizes statistically reliable sensing paths while suppressing unstable ones. The framework is tested on a 9.2-m carbon steel pipe, achieving accurate signal reconstruction and stable damage localization under different temperatures and noise levels.