摘要
针对大型结构健康监测对于光纤光栅传感网络的复用容量和维护成本的较高要求,设计了一种蛛网形拓扑结构的传感网络。该结构网络利用波分复用来增加网络的复用容量,并优化了基于门控循环单元的模型来对重叠波长进行解调。设计的新型传感网络具有较高的网络可靠性和网络复用容量,截取蛛网形网络的部分结构进行实验,设计了四种故障情况进行对比,证明了蛛网形网络具有较高的可靠性。通过改进解调模型的网络结构增加模型识别精度,采用训练良好的模型对不同重叠程度光谱解调,在89.9%情况下其均方根小于1 pm,证明改进模型可有效地对重叠光谱进行解调,大大增加了网络的复用容量。设计的新型传感网络可有效地增加网络的可靠性和复用容量。
A cobweb topology sensor network was designed to meet the high requirements of large structure health monitoring on the reuse capacity and maintenance cost of fiber Bragg grating sensor network.In this structured network,Wavelength Division Multiplex(WDM)is used to increase the multiplexing capacity of the network,and the model based on gated cyclic unit is optimized to demodulate overlapping wavelengths.The new sensor network designed has high network reliability and network reuse capacity.Part of the structure of cobweb network is selected for experiment,and four kinds of fault conditions are designed for comparison.Through the four kinds of fault conditions,the signal can be effectively transmitted,which proves that the cobweb network has high reliability.In this paper,the four cases are summarized and summarized in the form of a table.The table shows that in these four cases,the network can still be used normally and has a high reliability.By improving the network structure of the demodulation model,the recognition accuracy of the model is increased,and the well-trained model is used to demodulate the spectra with different overlapping degrees.In 89.9%cases,the root mean square of the model is less than 1 PM,which proves that the improved model can effectively demodulate the overlapping spectra,and greatly increases the network reuse capacity.The experimental results of demodulation are presented in the form of tables and pictures.It can be seen that the central wavelength of each sensor can be well identified and the physical variables can be obtained under the condition of different degrees of spectral overlap.The new sensor network can increase the reliability and reuse capacity effectively.
作者
邵向鑫
马子筱
路天麒
李冬
江虹
SHAO Xiangxin;MA Zixiao;LU Tianqi;LI Dong;JIANG Hong(School of Electrical and Electronic Engineering,Changchun University of Technology,Changchun130012,China)
出处
《光子学报》
EI
CAS
CSCD
北大核心
2022年第3期206-214,共9页
Acta Photonica Sinica
基金
吉林省自然科学基金(No.20210101479JC)。
关键词
光纤布拉格光栅
拓扑结构
门控循环单元
波分复用
深度学习
可靠性
Fiber Bragg grating
Topological structure
Gated circulation unit
Wavelength division multiplex
Deep learning
Reliability