摘要
相位敏感光时域反射计(Φ-OTDR)系统在对振动信号位置判断的过程中,采用幅度差分累加法存在对振动峰位置计算准确率低、不同频率的振动信号需随时修改N值的问题。提出了一种经验模态分解(EMD)与神经网络结合的算法,该算法将振动点的时域信号分解后作为特征值输入到神经网络中训练,再对信号是否为目标振动信号进行识别,实验表明该方法的振动信号识别率达到96.49%。
The phase-sensitive optical time domain reflectometer(Ф-OTDR)system uses the amplitude differential accumulation method to count the position of the vibration signal.There exists the problem of low recognition rate and need to be modified the N for different frequencies.An algorithm combining empirical mode decomposition(EMD)and neural network is proposed.The EMD is used to denoise and analyze the time domain signal at the vibration point,the signal characteristics are extracted by EMD and input to an improved neural network as features for training.The trained neural network is then employed to identify different vibration signals at different frequencies.The experiment shows that the recognition rate of vibration signal is 96.49%.
作者
欧阳竑
刘承达
秦祖军
王侠
OUYANG Hong;LIU Chengda;QIN Zujun;WANG Xia(The 34th Research Institute of CETC,Guilin Guangxi 541004,China;School of Electronic Engineering and Automation,Guilin University of Electronic Technology,Guilin Guangxi 541004,China)
出处
《光通信技术》
北大核心
2020年第3期37-40,共4页
Optical Communication Technology
关键词
相位敏感光时域反射计
神经网络
经验模态分解
Ф-optical time domain reflectometer
neural network
empirical mode decomposition
