基于深度学习的Φ-OTDR输油管道入侵监测研究  

Oil Pipeline Intrusion Monitoring Based on Deep Learning ofΦ-OTDR

作　　者：杨震 封皓[1] Yang Zhen;Feng Hao(State Key Laboratory of Precision Measuring Technology and Instruments,Tianjin University,Tianjin 300072,China)

机构地区：[1]天津大学精密测试技术及仪器国家重点实验室,天津300072

出　　处：《激光与光电子学进展》2022年第8期41-49,共9页Laser & Optoelectronics Progress

摘　　要：相位敏感光时域反射(Φ-OTDR)技术在管道入侵预警领域有着突出表现,其中对于入侵事件的定位和识别是该领域研究的热点。近年来,基于神经网络的信号识别方法不断被提出,但这些方法大多忽视了事件的定位,在实际工程应用中需要人工的持续介入。基于输油管道入侵事件的安全预警问题,提出了一种基于深度学习的入侵事件自动识别和定位的方法。所提方法以图像目标检测思想为基础,以1 s时间、4 km空间距离的时空图作为目标检测网络的输入,并使用最大最小归一化、带通滤波和图像移位数据增强3种预处理方法,同时实现对入侵事件的定位和识别。实验结果表明,所提方法对地表捶打、地表挖掘和人为跳跃3种事件单次发生的平均召回率达到82.9%,精确率达到70.4%,基本能够满足工程上的需求。Phasesensitive optical timedomain reflection(ΦOTDR)technique has played a critical role in the field of pipeline intrusion monitoring.Identifying and locating intrusion events is a key topic in this field.While neural networkbased solutions have been proposed frequently in recent years,a majority of them neglect the location of the events,resulting in ongoing manual labor in practical engineering applications.Based on the investigation of pipeline intrusion event identification,an automatic event recognition and location method is proposed.The proposed method is based on the concept of target detection,and the spatiotemporal diagram of 1 s time and 4 km spatial distance is used as the input of the target detection network.As such,maxmin normalization,bandpass filtering,and data augmentation are employed as preprocessing methods to realize the location and identification of intrusion events at the same time.The experiment demonstrates that the proposed method can achieve an average recall of 82.9%and a precision of 70.4%in three types of events,including surface beating,surface digging,and human jumping,which can basically meet most industrial requirements.

关 键 词：光纤光学 光纤传感 图像处理 相位敏感光时域反射计 安全预警 目标检测 

分 类 号：TN247[电子电信—物理电子学]