A FSTCN-Based Leak Detection Method for Large-Scale Pipeline Transportation Systems

Abstract

As one of the five major transportation systems, pipeline plays an important role in the energy transportation systems. In large-scale pipeline transportation systems, security issues such as leaks and explosions are prevalent, thus early detection of leaks is important to reduce security hazards in pipeline systems. Time series-based studies are widely used for leak detection in large-scale pipeline transportation systems, but single time-domain information, which ignores the spatial distribution of pressure sensors and does not consider periodic features, may not be sufficient for the detection accuracy of complex systems. To address it, a leak detection method based on frequency spatial-temporal convolution network (FSTCN) is proposed in this paper. Next, a spatial-encoder module for leak detection is proposed, which considers the spatial correlation of pressure sensors in pipeline systems. Second, a frequency-enhanced attention layer is proposed, which enables the feature extraction module to capture the periodic features of the pressure data. Meanwhile, a network self-updating mechanism is proposed which considers the changes in detection accuracy and data distribution to adapt to the continuously changing conditions of the pipeline systems. Finally, experiments are used to validate the proposed method, and nine time series classification models are chosen for comparison. The comprehensive results demonstrate that the effectiveness and superiority of the proposed leak detection method for large-scale pipeline systems.

Keywords

• Pipeline transportation systems
• leak detection
• data driven
• spatial-temporal feature
• frequency enhanced attention