| Paper ID | MLSP-48.5 |
| Paper Title |
PIPELINE SAFETY EARLY WARNING METHOD FOR DISTRIBUTED SIGNAL USING BILINEAR CNN AND LIGHTGBM |
| Authors |
Yiyuan Yang, Yi Li, Haifeng Zhang, Tsinghua University, China |
| Session | MLSP-48: Neural Network Applications |
| Location | Gather.Town |
| Session Time: | Friday, 11 June, 14:00 - 14:45 |
| Presentation Time: | Friday, 11 June, 14:00 - 14:45 |
| Presentation |
Poster
|
| Topic |
Machine Learning for Signal Processing: [MLR-APPL] Applications of machine learning |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| 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. |
