Intelligent acoustic emission-based pipeline leak detection technology plays a critical role in Internet of Things structural health monitoring for offshore platforms. However, traditional deep networks possess large parameter counts and high computational complexity, making them infeasible for deployment on resource-constrained edge nodes, while lightweight methods universally adopt single-scale feature extraction and cannot simultaneously capture short-duration burst and long-range attenuation characteristics of acoustic emission signals, resulting in insufficient discriminative capability for adjacent valves. To address this, this article proposes Tiny Leak Sense Net (TinyLSN), a novel, lightweight leak localization framework specifically designed for Internet of Things edge nodes. TinyLSN achieves an optimal balance between computational efficiency and detection performance through three innovative components we designed, including the inverted residual block (IRB), multiscale dilated perception module (MSDPM), and large kernel feed-forward network (LK-FFN), which, respectively, enhance cross-channel interactions, capture multiscale temporal features, and extract global attenuation patterns. On our self-constructed experimental dataset simulating real offshore platform operational pipeline leakage, TinyLSN achieved detection accuracy of 97.11%–97.45% and an extremely low false positive rate of 0.27%–0.32%, significantly outperforming lightweight baseline methods. Validation on publicly available benchmark datasets further confirmed its generalization capability. When deployed on the STM32H7B3I-DK microcontroller, TinyLSN requires only 267.16-KiB flash memory and achieves 3.417-ms inference latency. Furthermore, TinyLSN maintains over 90% accuracy under strong noise and achieves 94.83% accuracy with only 10% training samples, fully validating its reliability in harsh industrial environments and providing an efficient and feasible solution for offshore platform Internet of Things edge intelligence.

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.