Research Progress in the Key Device and Technology for Fiber Optic Sensor Network

The recent research progress in the key device and technology of the fiber optic sensor network （FOSN） is introduced in this paper. An architecture of the sensor optical passive network （SPON）, by employing hybrid wavelength division multiplexing/time division multiplexing （WDM/TDM） techniques similar to the fiber communication passive optical network （PON）, is proposed. The network topology scheme of a hybrid TDM/WDM/FDM （frequency division multiplexing） three-dimension fiber optic sensing system for achieving ultra-large capacity, long distance, and high resolution sensing performance is performed and analyzed. As the most important device of the FOSN, several kinds of light source are developed, including the wideband multi-wavelength fiber laser operating at C band, switchable and tunable 2 μm multi-wavelength fiber lasers, ultra-fast mode-locked fiber laser, as well as the optical wideband chaos source, which have very good application prospects in the FOSN. Meanwhile, intelligent management techniques for the FOSN including wideband spectrum demodulation of the sensing signals and real-time fault monitoring of fiber links are presented. Moreover, several typical applications of the FOSN are also discussed, such as the fiber optic gas sensing network, fiber optic acoustic sensing network, and strain/dynamic strain sensing network.

Application of the Spectrum Peak Positioning Technology Based on BP Neural Network in Demodulation of Cavity Length of EFPI Fiber Optical Sensor

An Extrinsic Fabry-Perot Interferometric (EFPI) fiber optical sensor system is an online testing system for the gas density. The system achieves the measurement of gas density information mainly by demodulating the cavity length of EF- PI fiber optical sensor. There are many ways to achieve the demodulation of the cavity length. For shortcomings of the big intensity demodulation error and complex structure of phase demodulation, this paper proposes that BP neural net-work is used to locate the special peak points in normalized interference spectrum and combining the advantages of the unimodal and bimodal measurement achieves the demodulation of the cavity length. Through online simulation and actual measurement, the results show that the peak positioning technology based on BP neural network can not only achieve high-precision demodulation of the cavity length, but also achieve an absolute measurement of cavity length in large dynamic range.

Loading Localization by Small-Diameter Optical Fiber Sensors

Structural health monitoring(SHM)in service has attracted increasing attention for years.Load localization on a structure is studied hereby.Two algorithms,i.e.,support vector machine(SVM)method and back propagation neural network(BPNN)algorithm,are proposed to identify the loading positions individually.The feasibility of the suggested methods is evaluated through an experimental program on a carbon fiber reinforced plastic laminate.The experimental tests involve in application of four optical fiber-based sensors for strain measurement at discrete points.The sensors are specially designed fiber Bragg grating(FBG)in small diameter.The small-diameter FBG sensors are arrayed in 2-D on the laminate surface.The testing results indicate that the loading position could be detected by the proposed method.Using SVM method,the 2-D FBG sensors can approximate the loading location with maximum error less than 14 mm.However,the maximum localization error could be limited to about 1 mm by applying the BPNN algorithm.It is mainly because the convergence conditions(mean square error)can be set in advance,while SVM cannot.

Reflective Ladder Topology Network Based on White Light Fiber-Optic Mach-Zehnder Interferometer

In order to improve the multiplexing capability of the optical sensors based on the lower interferential optic fiber sensing technology and the white light fiber-optic Mach-Zehnder interferometer,reflective ladder topology network ( RLT) with tailored formula was proposed. The topology network consists of 6 rungs sensing elements linked by 5 couplers. Two cases with different choices of couplers were contrasted: one is equal coupling ratio,and the other is tailored coupling ratio. Through the simulation of these two cases,the detailed multiplexing capability was analyzed,and accordingly the experiments were also carried out. The simulation results showed that,the tailored formula enhances the multiplexing capability of the structure. In the first case, the maximum number of sensors which can be multiplexed is 8,and in the other case is 12 fiber optic sensors. The experimental results have a good agreement with numerical simulation results. Thus,it is considered expedient to incorporate RLT into large-scale building,grounds,bridges,dams,tunnels,highways and perimeter security.

Use Single－chip Computer to Realize Automatic Compensation for Optical Fiber Sensors

Being directed against two kinds of noise in optical fiber sensors,a simple and effective method of automatic compensation for optical fiber sensors is presented.Not only the unstability effect of light source,but also zero drift of photoelectronic devices,can be eliminated or enormously restrained with the aid of this method.In another way,by using single-chip microcomputer,the optical fiber sensor system fabricated is connected to a computer network to realize an automatic compensation.

基于分布式光纤传感与U-Net网络的复合材料分层损伤定量识别方法

结构健康监测(SHM)是确保飞行器复合材料结构安全性和完整性的重要手段。基于背向瑞利散射的分布式光纤传感器可以通过测量高密度的应变分布为复合材料损伤监测提供数据支持。然而,结构应变分布特征和损伤的映射关系较为复杂,无法直接根据应变分布准确判定损伤的定量信息。另外,分布式光纤传感器数据量大,通过人为分析应变数据识别损伤较为耗时且准确性偏低。为了应对这一挑战,提出了一种基于分布式光纤传感数据与U-Net神经网络的智能损伤识别方法,旨在自动精确识别复合材料中常见的分层损伤。首先,通过有限元仿真构建U-Net神经网络的训练集与验证集;随后进行含分层损伤复合材料板的悬臂加载试验,通过分布式光纤传感器采集结构应变分布数据作为测试集。损伤识别结果表明,U-Net神经网络可以对分层损伤的位置、尺寸与形状进行较为精确的定量识别。

基于光纤的传感物联网吞吐量最大化中继节点选择

中继节点是传感物联网通信的中转中心,其选择的合理性对保证物联网通信稳定和信息吞吐量具有重要意义。提出基于光纤的传感物联网吞吐量最大化中继节点选择方法。构建传感物联网模型,在平面中任意布局节点,利用所在点数据整合全部节点,在目标光纤传感器基站中找出主瓣,识别光源节点传输的信号。计算节点能量平均数,在自适应状态中将能量的分母最小化。在候选节点选取可将已知的光信道状态完整传输给其他节点的中继节点,基于统计独立分布获得系统平均接收信噪比,选择光纤传感器天线完成吞吐量最大化中继节点选择。仿真结果表明,所提方法在节点数量为22时的中断概率趋近于0;在光源节点功率30 dBm时,网络吞吐率达到98%。

Review of Optical Fiber Sensor Network Technology Based on White Light Interferometry

Optical fiber sensor networks(OFSNs)provide powerful tools for large-scale buildings or long-distance sensing,and they can realize distributed or quasi-distributed measurement of temperature,strain,and other physical quantities.This article provides some optical fiber sensor network technologies based on the white light interference technology.We discuss the key issues in the fiber white light interference network,including the topology structure of white light interferometric fiber sensor network,the node connection components,and evaluation of the maximum number of sensors in the network.A final comment about further development prospects of fiber sensor network is presented.

光纤传感网络空间中攻击节点捕获方法研究

为了更准确地捕获网络空间中的攻击节点,提出一种光纤传感网络空间中攻击节点捕获方法。首先利用传感电路以及信号采集模块实时采集扰动信号,采用级联离散小波变换方法对信号实施去噪处理。然后通过基站向网内节点发送测试数据包,要求将数据包转发至邻居节点,并监视处理转发过程。节点对比基站和邻居节点发送的数据包,对邻居节点的可疑性进行投票。基站根据节点投票结果判断攻击节点,完成对空间中攻击节点的捕获。测试结果表明:该方法能够更准确地捕获光纤传感网络空间中的攻击节点,且能量消耗较低。

光纤传感网络小样本入侵信号边际谱标记检测

光纤传感网络中,小样本人工入侵信号由于没有入侵经验数据做识别背景,其首次入侵特征标记一直是一个难点。设计了一种光纤传感网络小样本人工入侵信号动态提取方法。首先采用高斯滤波器对光纤传感网络信号做去噪处理,消除光纤传感网络信号中存在的噪声信号对入侵信号提取产生的干扰。然后结合希尔伯特变换方法和经验模态分解方法,构建光纤传感网络信号的Hilbert边际谱。将光纤传感网络信号的Hilbert边际谱作为信号的标记特征,输入SVM分类器中,完成光纤传感网络小样本人工入侵信号的动态提取。仿真结果表明,所提方法的噪声信号、敲击信号和晃动信号事件识别率平均值分别为95.5%、91.5%及89.5%,当时域信号采样点数量为5 000个时,所提方法的信号提取时间为0.245 s。

Huge Capacity Fiber-Optic Sensing Network Based on Ultra-Weak Draw Tower Gratings

This paper reviews the work on huge capacity fiber-optic sensing network based on ultra-weak draw tower gratings developed at the National Engineering Laboratory for Fiber Optic Sensing Technology （NEL-FOST）, Wuhan University of Technology, China. A versatile drawing tower grating sensor network based on ultra-weak fiber Bragg gratings （FBGs） is firstly proposed and demonstrated. The sensing network is interrogated with time- and wavelength-division multiplexing method, which is very promising for the large-scale sensing network.

基于ABC-BP神经网络的光纤传感器光强补偿

为了减少光纤传感器测量过程中接收光强受到非线性因素的影响,提出利用人工蜂群算法(artificial bee colony, ABC)优化反向传播神经网络(BPNN)进行光强补偿的方法。通过人工蜂群算法局部搜索最优的能力优化传统反向传播神经网络的权值与阈值,达到减少局部样本陷入极值的目的。将内圈与外圈2组接收光功率以及位移作为训练数据,优化神经网络各参数值,从而建立最优ABC-BP神经网络补偿模型。结果表明人工蜂群算法优化后平均绝对误差减少了0.001 114,均方根误差减少了0.001 182,参数值均小于传统反向传播神经网络和支持向量机补偿模型。对比实验证明该混合算法预测误差更小,能够更高精度完成光强补偿过程。

仿生算法优化神经网络的光纤传感器光强补偿方法

为了减少非线性因素对反射式光纤传感器测量结果的影响,提出了一种基于麻雀搜索算法(SSA)优化反向传播神经网络(BPNN)的光强补偿模型。利用麻雀搜索算法优化反向传播神经网络的权值与阈值,达到避免传统反向传播神经网络陷入局部样本极值的目的。比较麻雀搜索算法和粒子群算法的收敛曲线,可知麻雀搜索算法具有迭代步骤少和收敛速度快的特性。通过传感器内圈和外圈两组接收光功率值训练SSA-BP神经网络,结果表明该混合算法平均绝对误差为0.002,均方根误差为0.003,平均绝对百分比误差为0.011%。将SSA-BP神经网络、PSO-BP神经网络和传统BP神经网络的补偿效果进行对比,结果证明提出的光强补偿模型预测误差更小且稳定,能够更高精度完成位移测量过程。

面向过程控制的光纤传感网络多状态监测系统

为了实现对工业过程控制的实时监测与在线控制,设计了基于光纤传感网络的多状态监测与反馈控制系统。系统由扫频激光器、光纤解调模块、PC处理模块、光纤传感网络及状态控制单元组成。提出了一种多状态监控算法,将温度与应变的各点位FBG波长响应值进行权重组合,然后与4种常见的状态异常情况进行映射建模,再通过状态分析,给出反馈调整参数。实验采用罐内温度场与罐外应力场采集的方式,对应变0~5000με与温度30~150℃的在线检测进行标定。结果显示,校正后的应变灵敏度为0.49 pm/με,温度灵敏度为7.46 pm/℃。反馈控制对状态波动具有在线调整能力,不同异常类型的反馈时间不同。4种情况校正后的温度与应变波长偏差均小于±1℃和±50με,验证了系统的可行性。

基于光频域反射技术的光纤传感网络性能分析方法

现有方法无法实现温度与应变的精准检测,为了提升光纤传感网络的传感性能,扩展光纤传感网络应用范围,设计了基于光频域反射技术的光纤传感网络性能分析。可调谐激光器将扫描激光传送至耦合器中,耦合器分散激光后分别传送至反射镜以及光纤光栅阵列中,激光传送至光纤光栅阵列后利用耦合器生成主干涉仪,主干涉仪将光传送至光电探测器中形成拍频信号;传送至反射镜的激光由马赫-曾德尔干涉仪接收后形成辅助反射仪,辅助反射仪将光传送至光电探测器形成另一个拍频信号。通过两个拍频信号间的差异完成光纤传感网络复用;依据两个拍频信号频率呈现光纤光栅位置信息,实现光纤传感网络的光栅位置信息解调,通过复用与光栅位置信息解调完成光纤传感网络的传感。实验结果表明,本方法线性拟合度的结果高达0.998 6,光纤传感网络可实现温度与应变的精准检测,应用性能广泛。

以光纤技术助力广播电视安全优质播出

光纤技术作为现代通信技术的后起之秀,以其宽频带、低损耗、抗干扰能力强等优势备受人们的青睐,也给广播电视领域带来深远的影响。通过运用好光纤技术,促进广播电视业务朝着更加优质、高效、安全的方向不断发展。

分立式与分布式光纤传感关键技术研究进展

光纤传感技术已广泛应用于航空航天、石油化工、电子电力、土木工程、生物医药等领域,其技术形式主要体现为分立式和分布式.分立式光纤传感技术利用光纤敏感器件作为传感器来感知被测参量的变化,光纤作为光信号的传输通道连接光纤传感器及后端的解调装置;分布式光纤传感系统基于光纤瑞利散射、拉曼散射或布里渊散射等光学效应,利用光纤本身作为传感器,可对沿途的光信号进行大范围、长距离传感.本文介绍了分立式与分布式光纤传感中主要关键技术的研究进展,并对未来的研究和发展方向进行了探讨.

光纤传感器的神经网络光强补偿及非线性校正

为实现光纤位移传感器的光强补偿及非线性校正,确立并构造了两输入单输出的BP神经网络。讨论了基于L-M优化算法的BP网络的优点,说明了神经网络的权值修正方法及网络的具体训练步骤,阐明了用神经网络实现光纤位移传感器光强波动补偿及传感器非线性校正的原理。最后使用Matlab实现了神经网络,并将该网络应用于实际测量,结果表明该网络很好地实现了传感器的光强补偿及非线性校正。

管道泄漏监测与控制技术的研究

管道泄漏监测与控制技术已经成为管道安全运行的关键问题。本文通过对泄漏信号识别、泄漏点精确定位以及管线运行数据的实时传输和科学管理等关键技术的分析, 提出了基于分布式光纤传感器的管道泄漏实时监测系统设计方案。对泄漏点的定位技术提出了用神经网络实现泄漏点定位的策略。对监测信号的数据采集, 设计了以FPGA为核心的数据采集系统, 通过实验数据采集系统工作稳定, 达到了设计要求。

基于红外光谱吸收的煤矿瓦斯光纤传感网络

基于气体在其特征吸收波长下光的吸收随浓度变化的原理,通过对甲烷气体红外吸收光谱的分析,设计了一套检测甲烷气体浓度的多点光纤传感网络。该系统采用分布反馈式半导体激光器(DFB LD)作为光源,高灵敏度、低噪声的InGaAs PIN作为光电探测器,结合空分复用技术复用了16个甲烷气体传感器,采用滤波、前置放大等电路对微弱信号进行处理,经PCI数据采集卡采集各路信号后,再用软件编写程序对采集信号进行快速傅里叶变换等分析。研究表明,该网络各传感器的灵敏度均达到200 ppm(μg.mL-1),长时间的精确度和稳定性均可满足实际要求,单个传感器的响应时间小于2 s,系统巡检一周的时间小于32 s。通过理论分析得出,该系统各传感器可放置于离地面20公里以上的矿井中,且该系统可在多场合进行多点的实时检测。