Pipeline Safety Early Warning Method for Distributed Signal using Bilinear CNN and LightGBM
Yiyuan Yang, Yi Li, Haifeng Zhang
Abstract
Oil and gas pipelines are known as the backbone of global energy, and securing their safety is crucial for energy supply. In this study, we utilized a novel machine learning method based on the spatiotemporal features of distributed optical fiber sensor signals to monitor the safety of oil and gas pipelines in real time. Encouraging empirical results on a large amount of data collected from real sites confirmed that our model could accurately locate and identify the damage events of a pipeline in real time under strong noise and various hardware conditions, and could effectively handle the signal drift problem. Furthermore, as a generalized tool, the proposed solution could be applied to other industrial inspection fields. Our codes and video demos are available at https://github.com/yyysjz1997/B-CNN_LGBM-PSEW.