PSO-GPR for Linear Fit of Fiber Grating Sensing

QIAN Min; GUI Lin; LIAN Xiaoxuan; DING Meiqi; WANG Liandong

doi:10.13756/j.gtxyj.2024.230142

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PSO-GPR for Linear Fit of Fiber Grating Sensing

更新时间：2024-08-05

- PSO-GPR for Linear Fit of Fiber Grating Sensing
- Study on Optical Communications Issue 4, Pages: 23014201-23014206(2024)
- 作者机构：
  
  a.上海第二工业大学　智能制造与控制工程学院，上海　201209
  b.上海第二工业大学　计算机与信息工程学院，上海　201209
- 作者简介：
- 基金信息：
- DOI：10.13756/j.gtxyj.2024.230142
  CLC： TN929
- Published：10 August 2024，
  
  Received：08 September 2023，
  
  Revised：28 September 2023，
- 稿件说明：
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钱敏，桂林，连枭轩，等. 面向光纤光栅传感线性拟合度的PSO-GPR算法[J]. 光通信研究，2024(4)：230142.

Qian M, Gui L, Lian X X, et al. PSO-GPR for Linear Fit of Fiber Grating Sensing [J]. Study on Optical Communications, 2024(4)：230142.
钱敏，桂林，连枭轩，等. 面向光纤光栅传感线性拟合度的PSO-GPR算法[J]. 光通信研究，2024(4)：230142. DOI： 10.13756/j.gtxyj.2024.230142.

Qian M, Gui L, Lian X X, et al. PSO-GPR for Linear Fit of Fiber Grating Sensing [J]. Study on Optical Communications, 2024(4)：230142. DOI： 10.13756/j.gtxyj.2024.230142.

摘要

【目的】

为了提高光纤布拉格光栅（FBG）传感系统中反射光谱中心波长与外部环境变量之间的线性拟合度，文章提出了使用粒子群优化的高斯过程回归模型应用于FBG应力传感领域。

【方法】

针对FBG的反射光谱特性，文章研究了对于FBG传感系统在光谱拟合中线性拟合度的影响，通过粒子群算法去寻找高斯过程回归模型中的最优超参数以提升对反射光谱中心波长的预测性能。文章搭建了FBG应力传感实验平台，将FBG铺设在强度梁上，在等强度梁一端施加不同重量的砝码对FBG产生轴向应变，通过光谱仪采集反射光谱数据并使用文章所提模型进行线性拟合分析处理，将未优化的高斯过程回归模型、最大值法、高斯拟合法和质心法得到的结果作为对照组。

【结果】

结果表明，在掺铒光纤放大器输出功率为10 dBm、传输光纤距离为50 m、光谱仪采样点个数为501的条件下，反射光谱中心波长与砝码重量之间的线性拟合度均优于对照组，文章所提模型的线性拟合度最高能达到0.951 9，相较于对照组均有所提升。在501、251、167和126点的光谱采样点条件下，文章所提模型能将系统的线性拟合度提升到0.990 0，相较于最大值法最大提升了0.258 7。

【结论】

分析结果表明，使用粒子群优化的高斯过程回归模型能够有效提高FBG应力传感系统的线性拟合度。

Abstract

【Objective】

To improve the linear fit between the reflected spectral center wavelength and external environmental variables in Fiber Bragg Grating (FBG) sensing system

this paper proposes to use particle swarm optimization of Gaussian process regression model to the field of FBG stress sensing.

【Methods】

For the reflectance spectral characteristics of the FBG

the paper studies the impact of the linear fit in the spectral fitting of FBG sensing system. The particle swarm algorithm is used to search for the optimal hyperparameters in the Gaussian process regression model in order to enhance the predictive performance of the reflectance spectral wavelength of the center. A FBG stress sensing experimental platform was built

and the FBG was laid on the strength beam. Different weights were applied to one end of the equal strength beam to produce axial strain on the FBG

and the reflectance spectral data were collected by the spectrometer and analyzed by linear fitting with the studied model. The results obtained by the unoptimized Gaussian process regression model

the maximum value method

the Gaussian fitting method

and the center of mass method were used as the control group.

【Results】

The results show that under the conditions of erbium-doped fiber amplifier output power of 10 dBm

transmission fiber distance of 50 m

and the number of sampling points of the spectrometer of 501

the linear fit between the reflected spectral center wavelength and the mass of the weights is better than that of the control group. The linear fit of the studied model can reach up to 0.951 9

which is improved compared with that of the control group. Under the conditions of 501

251

167 and 126 spectral sampling points

the studied model can improve the linear fit of the system to 0.990 0

which is a maximum improvement of 0.258 7 compared with the maximum value method.

【Conclusion】

The analysis results show that the Gaussian process regression model optimized by the particle swarm is able to effectively improve the linear fit of the FBG stress sensing system.

关键词

光纤布拉格光栅高斯过程回归粒子群算法线性拟合度

Keywords

FBGGaussian process regressionparticle swarm algorithmlinearity of fit

references

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Zhang W, Chen W M, Shu Y J, et al. Effects of Bonding Layer on the Available Strain Measuring Range of Fiber Bragg Gratings[J]. Applied Optics, 2014, 53(5):885-891.

朱梅, 张淼, 胡立章, 等. 光纤布拉格光栅中心波长检测中的寻峰算法[J]. 光通信研究, 2011(5):60-63.

Zhu M, Zhang M, Hu L Z, et al. Peak-search Algorithm for Center Wavelength Detection in Fiber Bragg Gratings[J]. Study on Optical Communications, 2011(5):60-63.

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Manie Y C, Li J W, Peng P C, et al. Using a Machine Learning Algorithm Integrated with Data De-noising Techniques to Optimize the Multipoint Sensor Network[J]. Sensors, 2020, 20(4):s20041070.

江俊峰, 何盼, 刘铁根, 等. 基于复合波长参考的温度稳定光纤光栅传感解调研究[J]. 光学学报, 2015, 35(10):1006005.

Jiang J F, He P, Liu T G, et al. Research of Temperature-stable Fiber Bragg Grating Sensing Demodulation based on Composite Wavelength References[J]. Acta Optica Sinica, 2015, 35(10):1006005.

Williams C K I, Rasmussen C E. Gaussian Processes for Machine Learning[M]. Cambridge, MA: MIT Press, 2006.

尹晓叶. 基于粒子群算法的函数优化求解方法研究[J]. 集成电路应用, 2022, 39(9):210-211.

Yin X Y. Study on Function Optimization based on Particle Swarm Optimization[J]. Application of IC, 2022, 39(9):210-211.

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Related Institution

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Beijing Laboratory of Optical Fiber Sensing and System, Beijing Information Science & Technology University

Key Laboratory of the Ministry of Education for Optoelectronic Measurement Technology and Instrument, Beijing Information Science & Technology University

Institute of Oceanographic Instrumentation, Qilu University of Technology (Shandong Academy of Sciences)

School of Ocean Technology Sciences, Qilu University of Technology (Shandong Academy of Sciences)

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