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

Abstract

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 datasets. Statistical tests confirmed the method significantly outperformed existing approaches. With only 30% training data, accuracy exceeded 88%. Ablation studies validated component effectiveness. STAG required only three layers for high-precision detection and localization. This research provides an effective solution for intelligent offshore pipeline monitoring with significant engineering value.