基于数据融合理论的多通道光纤位移传感器

肖韶荣1; 张周财2; 黄新2

doi:10.3788/OPE.20132111.2764

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基于数据融合理论的多通道光纤位移传感器

现代应用光学 | 更新时间：2020-08-12

- 基于数据融合理论的多通道光纤位移传感器
- Multi-channel fiber-optic displacement sensor based on data fusion
- 光学精密工程 2013年21卷第11期页码：2764-2770
- 作者机构：
  
  1. 南京信息工程大学物理与光电工程学院,江苏南京,210044
  2. 南京信息工程大学电子与信息工程学院,江苏南京,210044
- 作者简介：
- 基金信息：
  
  科技部公益性（气象）行业专项();江苏省科技支撑计划()
- DOI：10.3788/OPE.20132111.2764
  中图分类号： TN253
- 收稿日期：2013-05-07，
  
  修回日期：2013-06-12，
  
  网络出版日期：2013-11-22，
  
  纸质出版日期：2013-11-15
- 稿件说明：
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肖韶荣, 张周财, 黄新. 基于数据融合理论的多通道光纤位移传感器[J]. 光学精密工程, 2013,21(11): 2764-2770

XIAO Shao-Rong, ZHANG Zhou-Cai, HUANG Xin. Multi-channel fiber-optic displacement sensor based on data fusion[J]. Editorial Office of Optics and Precision Engineering, 2013,21(11): 2764-2770
肖韶荣, 张周财, 黄新. 基于数据融合理论的多通道光纤位移传感器[J]. 光学精密工程, 2013,21(11): 2764-2770 DOI： 10.3788/OPE.20132111.2764.

XIAO Shao-Rong, ZHANG Zhou-Cai, HUANG Xin. Multi-channel fiber-optic displacement sensor based on data fusion[J]. Editorial Office of Optics and Precision Engineering, 2013,21(11): 2764-2770 DOI： 10.3788/OPE.20132111.2764.

摘要

基于多传感器数据融合理论，构建了一种多通道光纤位移传感器。用一根光纤作为输入通道，三根光纤作为输出通道，每两通道构建一个双通道光纤位移传感器。对不同双通道传感器输出进行数据融合处理，拓宽了传感器的测量范围。对传感器测量结果进行归一化处理，得到传感器的多条输出特性曲线；然后从获得的多条输出特性曲线中选取合适的区域作为传感器的工作区间；最后，分别采用回归分析和神经网络算法对选取的工作区间进行数据融合处理，并讨论了它们的适用范围和优缺点。实验结果表明，多通道光纤位移传感器结合适当的数据融合方法，提高了系统的测量精度和稳定性，采用径向基函数（RBF）神经网络法得到的最大相对误差小于1.0%。三个通道融合后，光纤位移传感器的动态范围扩展为双通道的1.5倍。

Abstract

A multi-channel fiber-optic displacement sensor was proposed based on the multi-sensor data fusion theory. By using a fiber as the input channel

and three fibers as the output one

every two channels constructed a two-channel fiber-optic displacement sensor. Then

the data from different two-channel fiber-optic sensors were fused to extend the measurement range of the sensor. Furthermore

the sensor measurement results were normalized to obtain the plurality of output characteristic curves of the sensor. By selecting the appropriate area from the plurality of output characteristic curves

the operation range of the sensor was determined. Finally

the data were processed by regression analysis and neural network algorithm respectively and the range of application

advantages and disadvantages of the methods were discussed. The results show that the measurement accuracy and stability have been improved. The greatest relative error by the Radial Basis Function(RBF) neural network is less than 1.0%

and the dynamic range of three channel fiber optic displacement sensor is extended to 1.5 times that of dual channel sensors with appropriate data fusion.

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references

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