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题名

光纤曲率传感器关键技术研究

其他题名
Research on key technologies of optical fiber curvature sensor
姓名
学号
11849302
学位类型
硕士
学位专业
电子与通信工程领域工程
导师
宋章启
论文答辩日期
2020-05-27
论文提交日期
2020-07-20
学位授予单位
哈尔滨工业大学
学位授予地点
深圳
摘要
结构的弯曲曲率是衡量物体形变程度的重要参数之一。实际应用中,曲率很难被直接测量。目前大多数检测结构变形程度的传感器都是通过检测弯曲引起的应变,进而计算出形变的。薄结构在大曲率半径弯曲时产生的应变很小,采用应变的方法测量比较困难。因此,开发一种高精度的可以直接测量曲率的传感器对于薄结构在大曲率半径弯曲时的形变测量具有重要意义。本文介绍一种基于强度调制型的光纤曲率传感技术,通过在光纤表面引入敏感区对光纤中传输光的模场进行扰动。当光纤向不同方向弯曲时,其模场分布会发生变化,则敏感区对模场的扰动程度就会发生变化,光纤敏感区的透射系数和背向散射系数也会随之变化。本文采用脉冲自参考解调技术从理论上抑制光源强度波动、光路传输损耗变化、系统噪声等因素的影响。并基于该解调技术研制了高精度强度调制型光纤曲率传感系统。采用有限差分光束传播法对侧边抛磨光纤传输特性进行数值仿真研究。利用MATLAB软件对基于曲率的曲线重建算法进行仿真验证。通过悬臂梁曲率测量实验对光纤曲率传感器增敏区的透射系数和背向散射系数进行高精度测量。实验测定传感器透射系数线性区灵敏度为0.47/m^(-1) ,背向散射系数灵敏度为6.73/m^(-1),系统测量噪声幅度为2.25×〖10〗^(-4),经过悬臂梁位移与曲率换算,可知基于透射光的光纤曲率传感器曲率分辨率是4.75×〖10〗^(-4) (m^(-1) )。在曲率区间0~1(m^(-1) )内响应的线性度较好。基于背向散射光的光纤曲率传感器分辨率是3.35×〖10〗^(-5) (m^(-1) ),在曲率范围-1~1(m^(-1) )内线性响应度较好。基于背向散射光的光纤曲率传感器可区分弯曲方向。该系统具有结构简单、响应带宽宽和可实现时分复用等优点。在研究光纤曲率传感器在空间形状感知领域的应用时,开发了一套基于单点曲率测量的悬臂梁空间二维曲线恢复系统。将两根光纤曲率传感器进行推挽式结构封装构成x方向的传感臂,将另一套推挽式结构与之正交封装构成y方向传感臂。再对解调系统和软件程序进行相应修改。利用悬臂梁曲率测量结构进行实验。实验测得该系统最大测量相对误差是0.18。研究表明基于单模光纤的强度型曲率传感器具有结构简单、易解调和成本低廉等优点。具有较好的应用前景。
其他摘要
The curvature of a structure is one of the important parameters to measure. However, in practical applications, curvature is difficult to be measured directly. Most deformation sensor measure curvature through detecting strain and calculating strain as curvature . The deformation of thin structure with large bending radius is small. It is difficult to measure strain in this situation. So, developing a high accurate curvature sensor is of great significance for the deformation measurement of thin structures. In this paper, a fiber curvature sensing technique based on intensity modulation is introduced. The field mode of optical fiber is disturbed by introducing rough region on one side of optical fiber. When the fiber is bended in different directions, the distribution of the mode field in the sensing zone will change, and the transmission coefficient and backscatter coefficient of the sensitive area of the fiber will also change.In this paper, the pulse self-reference demodulation technique is used to theoretically suppress the influence of the fluctuation of light source intensity, t optical transmission loss and the system noise. Based on the demodulation technique, a high precision intensity modulated fiber curvature sensing system is developed. We use finite difference beam propagation method to simulate the transmission characteristics of side-polished optical fiber. We use MATLAB to simulate and verify the curve reconstruction algorithm based on curvature. Experiment results based on the cantilever beam show that the sensitivity of the light transmission of the sensor is 0.47/m^(-1)in the linear region. The backscattering coefficient sensitivity is 6.73/m^(-1), and the system measurement noise amplitude is2.25×〖10〗^(-4). After converting the cantilever displacement into curvature , the curvature resolution of the fiber-optic curvature sensor based on transmitting light can reach 4.75×〖10〗^(-4) (m^(-1) ). The linearity of the response is good in the curvature interval 0~1(m^(-1) ). Optical fiber curvature sensors based on backscattering light have a resolution of 3.35×〖10〗^(-5) (m^(-1) ), and linear response is better in the curvature range-1~1(m^(-1) ), which meaning optical fiber curvature sensors based on backscattered light can distinguish the bending direction. The system has the advantages of simple structure, wide response bandwidth and time division multiplexing. At the same time, a two-dimensional cantilever spatial positioning system based on single-point curvature measurement was developed. The maximum relative error of this system was 0.18.The research shows that the intensity-type curvature sensor based on single-mode fiber has the advantages of simple structure, easy demodulation and low cost. It has a good application prospect
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语种
中文
培养类别
联合培养
成果类型学位论文
条目标识符http://sustech.caswiz.com/handle/2SGJ60CL/142725
专题创新创业学院
作者单位
南方科技大学
推荐引用方式
GB/T 7714
杨洋. 光纤曲率传感器关键技术研究[D]. 深圳. 哈尔滨工业大学,2020.
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