Structural health monitoring of offshore pipelines via a novel spatial-topological adaptive graph neural network

作者：Lu Yin, Yuxuan Zhang, Xiaolong Qiu, Weizhe Ren, Chuanyang Zhao, Menghan Chen, Yifei Li, Sebastian Bader, Hongbing Liu · 发表于：Structural Health Monitoring · 年份：2026 · DOI：10.1177/14759217261418056 · 被引用次数：14 · 研究领域：Water Systems and Optimization、Structural Integrity and Reliability Analysis、Offshore Engineering and Technologies

Structural health monitoring of offshore oil and gas pipelines is critical for energy security and environmental protection. Acoustic emission technology has been widely adopted as a non-destructive approach for pipeline valve leakage detection. However, it faces severe challenges in real marine environments. Offshore platform pipelines exhibit strong background noise interference that significantly undermines leakage signal identifiability. This requires distributed sensor deployment to expand monitoring coverage. But installation constraints cause spatially uneven distributions that limit information propagation and create monitoring blind spots. Consequently, collaborative response patterns among multiple sensors are difficult to extract effectively. Traditional fusion methods fail to exploit spatial dependencies between sensors. To address these challenges, this paper proposes a novel graph learning-based end-to-end intelligent monitoring method. The method employs a time–frequency domain graph to suppress noise interference and encode spatial relationships between sensors. Building upon this, a spatial-topological adaptive graph neural network (STAG) captures global collaborative patterns and balances information propagation in non-uniform networks. On datasets simulating real offshore platform pipeline leakage, the proposed method achieved 94.64%–97.74% accuracy and maintained 91.56% under −15 dB noise. Generalization and superiority were validated on public benchmark d...