Pipeline Safety Early Warning by Multifeature-Fusion CNN and LightGBM Analysis of Signals From Distributed Optical Fiber Sensors

• Published in: IEEE transactions on instrumentation and measurement Vol. 70; pp. 1 - 13
• Main Authors: Yang, Yiyuan, Zhang, Haifeng, Li, Yi
• Format: Journal Article
• Language: English
• Published: New York IEEE 2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial neural networks; Damage detection; Distributed optical fiber sensor; Energy; Gas pipelines; industrial signal processing and monitoring; Inspection; lightGBM; Machine learning; Monitoring; multifeature fusion convolutional neural network (MFCNN); Natural gas; Optical communication; Optical fibers; Optical network units; Optical sensors; Optical signal processing; Petroleum pipelines; Pipeline safety; pipeline safety early warning (PSEW); Pipelines; Real time; Real-time systems; Reflectometers; Safety; Signal processing algorithms

Energy pipelines are the backbones of global energy systems. Monitoring their safety and automatically identifying and locating third-party damage events are crucial to energy supply. However, most traditional methods lack in-depth consideration of distributed fiber signals and have not been tested...