基于分布式光纤声波传感技术的管道泄漏探测应用
Application of Pipeline Leakage Detection Based on Distributed Fiber Optic Acoustic Sensing Technology
冼峰1
作者信息
- 1. 上海城投水务<集团>有限公司,上海 200082
- 折叠
摘要
文章介绍了分布式光纤声波传感(distributed fiber optic acoustic sensing,DAS)技术的工作原理及其在管道泄漏检测中的应用.DAS系统通过光纤中背向瑞利散射光的变化来检测光纤上沿途感知到的声波信息,再结合光时域反射技术获取外部扰动事件所处的位置,从而通过检测和分析背向散射的光信号,提取外界扰动对应的位置和相位.文章搭建了一套试验系统,通过试验检测了 DAS运用于管道泄漏探测的可靠性.试验表明,DAS技术可以检测并识别不同位置和类型的泄漏事件.为了进一步提高泄漏检测效果,文章设计了一种分析方法,通过数据预处理、特征提取、事件检测和泄漏识别与分类,实现了对泄漏事件的精准识别和定位,并提高了其在泄漏检测中的灵敏度和实时性.该方法结合DAS的技术特点,可以在长距离和大范围内提供高效、可靠的泄漏监测.
Abstract
This paper introduced the working principle of distributed fiber optic acoustic sensing(DAS)technology and its application in pipeline leakage detection.The DAS system detected the acoustic information perceived along the fiber by the change of backward Rayleigh scattering light in the fiber,and combined the optical time domain reflectometry technique to obtain the location of the external disturbance event.Thus,by detecting and analyzing the backward scattered optical signal,the position and phase corresponding to the external disturbance were extracted.In this paper,a set of experimental system was set up to test the reliability of DAS used in pipeline leakage detection through experiments.The experiments showed that DAS technology could detect and identify different locations and types of leakage events.In order to further improve the leakage detection effect,this paper designed an analytical method.Through data preprocessing,feature extraction,event detection,leakage identification and classification,the precise identification and location of leakage events were achieved,and the sensitivity and real-time performance in leakage detection were improved.This method combined with the technical characteristics of DAS,can provide efficient and reliable leakage monitoring in long distances and large ranges.
关键词
分布式光纤声波传感(DAS)/泄漏检测/瑞利散射/频谱分析/光纤光学Key words
distributed fiber optic acoustic sensing(DAS)/leakage detection/Rayleigh scattering/spectrum analysis/fiber optics引用本文复制引用
基金项目
国家重点研发计划(2022YFC3801000)
出版年
2024
国家科技期刊平台
NSTL
万方数据