A Spatiotemporal Data-Driven Framework for Acoustic Signal-Based Natural Gas Pipeline Leak Detection

Existing methods for natural gas pipeline leak detection primarily rely on feature extraction and fault classification based on Euclidean data, which often overlook the potential spatiotemporal correlations present in leak signals. This limitation restricts the development of high-precision fault identification models. To address this issue, a spatiotemporal data-driven framework (STDDF) for acoustic signal-based natural gas pipeline leak detection is proposed. The framework begins with the application of a percentile-based cosine similarity measure to the acoustic sensor data, transforming it into a graph structure that captures both topological and temporal correlations. Subsequently, a full-graph mean aggregation (FGMA) method is introduced, utilizing a graph sample and aggregation network to enhance the representation of leak signal features. This approach overcomes the limitations of single-node features in capturing the spatial complexity of pipeline leaks. Furthermore, a gated recurrent unit (GRU) is employed to process the time-series data, effectively capturing the temporal dynamics of leak signals. Validation experiments conducted on a pipeline leak detection platform demonstrate that the STDDF model significantly outperforms traditional deep learning methods, achieving a fault identification accuracy of up to 99%.