Research on Intelligent Optical-Fiber Pre-Warning System for Long-Distance Pipeline Safety

Abstract

Long-distance oil and gas pipelines are widely distributed and have complex background environments.Therefore,their optical-fiber pre-warning system experiences a high false-alarm rate in identifying destructive events that threaten pipeline safety in a real-world environment.This makes it challenging for the system to achieve accurate pre-warning results and ensure pipeline safety.This study applies deep learning to a long-distance fiber pre-warning system.Through deep learning,a vehicle-passing signal that mainly affects the pre-warning effect is identified,which effectively reduces the false-alarm rate of the pre-warning system.The intelligent fiber pre-warning system is mainly divided into two parts:the distributed optical-fiber sensing system and the signal-recognition system.In a real-world environment,an intrusion signal around the pipeline is collected by aΦ-OTDR(phase-sensitive optical time domain reflectometry)distributed optical-fiber sensing system.Additionally,a recognition model is established by convolutional long short-term memory and fully connected deep neural networks to detect the vehicle-passing signal.After training and blind testing,the vehicle-passing event recognition model demonstrated a good recognition and positioning effect in a real-world long-distance fiber-monitoring environment and effectively reduced the false positives of the pre-warning system.