[38] Learning multi-dimensional sensor relationships for robust marine pipeline leakage non-destructive detection via adaptive graph networks

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Marine pipeline leakages pose severe environmental and economic threats, demanding accurate and robust detection systems. Sensor network-based detection faces three key challenges: highly dynamic network structures, weak leak signals with complex sensor correlations, and strong marine environmental noise. Existing methods have struggled significantly with these challenges. To address these limitations, this paper proposes a novel Multi-dimensional Adaptive Graph Integration Framework (MAGI). Within MAGI, an innovative Multi-dimensional Sensor Edge Interaction (MSEI) layer is introduced. MSEI tackles time-varying network modeling by dynamically integrating local interactions and global context, thereby generating rich multi-dimensional edge features that overcome the constraints of traditional single-weight edges. Furthermore, the framework incorporates a Feature Aggregation Network (FAN). FAN employs a parallel graph convolutional architecture and adaptive gating mechanisms, achieving noise robustness and efficient heterogeneous data integration. Experimental results validate MAGI's superior performance.