基于混合输入神经网络的Φ-OTDR系统模式识别方法  

Mode Recognition Method ofΦ⁃OTDR System Based on Mixed Input Neural Network

作　　者：李笑 高毅[1] 吴昊[2] 王道宇 Li Xiao;Gao Yi;Wu Hao;Wang Daoyu(Department of Information and Electronics,Wuhan Digital Engineering Institute,Wuhan 430202,Hubei,China;National Engineering Laboratory for Next Generation Internet Access System,Huazhong University of Science and Technology,Wuhan 430074,Hubei,China)

机构地区：[1]武汉数字工程研究所信息电子部,湖北武汉430202 [2]华中科技大学下一代互联网接入系统国家工程实验室,湖北武汉430074

出　　处：《中国激光》2023年第11期265-271,共7页Chinese Journal of Lasers

摘　　要：相位敏感光时域反射仪(Φ-OTDR)已被广泛应用于周界安防以及轨道交通和管道监测等动态传感领域,进一步提升振动信号识别准确率对异常事件及时报警具有重要意义。针对长距离相干探测相位解调Φ-OTDR易受干涉衰落影响而导致误报率较高的问题,笔者提出了基于强度和相位信号混合输入的模式识别方法。所提方法使用多层感知模块提取强度信号中的衰落噪声特征,采用常规一维卷积神经网络作为对照模型。实验结果表明:使用强度和相位作为混合输入的模型对人工敲击、机械挖掘、人为行走和跳跃等4种事件的平均识别准确率可以达到98.8%,优于仅使用相位信号作为输入的一维卷积神经网络模型的平均识别准确率96.1%。采用强度信号辅助相位信号检测的模式识别方法可进一步提高Φ-OTDR的模式识别准确率。Objective Phase-sensitive optical time-domain reflection(Φ-OTDR)has the advantages of high accuracy,fast response speed,long monitoring distance,and anti-electromagnetic interference and has been widely used in dynamic sensing fields such as perimeter security and railway and pipeline monitoring.For direct detection intensity-demodulationΦ-OTDR,the pulse power is limited by the nonlinear effect,which causes a weak signal-to-noise ratio of the end signal,and its sensing distance is usually less than 25 km.Because the optical phase signal is linearly related to the vibration signal imposed on the fiber and coherent detection can significantly improve the detection sensitivity,the long-distanceΦ-OTDR system mainly uses coherent detection and phase demodulation technology.Most coherent detection phase-demodulationΦ-OTDR system model recognition algorithms use phase signal as the input,combined with time-frequency feature extraction methods,such as Fourier transform and wavelet transform.However,interference fading occurs in the coherent detection system,which causes serious deterioration of the intensity signal,resulting in phase demodulation errors and false alarms.Common methods to eliminate interference fading are the frequency diversity,chirped pulses,and other frequency domain regulation technologies,which lead to complex system hardware.Moreover,owing to the variety of the disturbance signals and long sensing distance that results in a low signal-to-noise ratio of the end signal,Φ-OTDR systems suffer from false alarms in practical applications.It is of great significance to further improve the accuracy of the vibration signal identification for the timely detection of abnormal events.Methods A pattern recognition method based on a coherent detectionΦ-OTDR system with mixed intensity and phase signal inputs is proposed,which can effectively reduce the impact of interference fading on the accuracy of event alarms without increasing the hardware complexity.The proposed method uses a hybrid deep neural networ

关 键 词：光纤光学 光纤传感 相位敏感光时域反射仪 混合神经网络 模式识别 深度学习 

分 类 号：TN247[电子电信—物理电子学]