Demodulation of Temperature Stabilized Fiber Bragg Grating Sensor Based on Optimized Least Square Support Vector Machine

Wenjuan Sheng; Zhengbin Hu; Ning Yang; Gangding Peng

doi:10.3788/LOP202259.0305002

Laser & Optoelectronics Progress, Volume. 59, Issue 3, 0305002(2022)

Demodulation of Temperature Stabilized Fiber Bragg Grating Sensor Based on Optimized Least Square Support Vector Machine

Wenjuan Sheng^1、*, Zhengbin Hu¹, Ning Yang¹, and Gangding Peng²

Author Affiliations

¹School of Automation Engineering, Shanghai University of Electric Power, Shanghai 200090, China

²School of Electrical Engineering and Telecommunications, The University of New South Wales, Sydney , New South Wales 2052, Australia

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Figures & Tables(10)

Fig. 1. Flow chart of the improved BAS-PSO

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Fig. 2. Principle of the FBG sensor measurement system

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Fig. 3. Position of the reference grating and sensor grating. (a) No strain condition; (b) strain condition

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Fig. 4. Change curves of temperature and strain during the cooling process. (a) Temperature change; (b) spectral position; (c) G1; (d) G2

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Fig. 5. Change curves of strain and temperature after smoothing. (a) G1; (b) G2; (c) temperature

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Fig. 6. Change curve of fitness function value with the number of iterations (G1)

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Fig. 7. Change curve of fitness function value with the number of iterations (G2)

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Table 1. Modeling compensation results of different optimization algorithms for G1unit: pm
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Table 1. Modeling compensation results of different optimization algorithms for G1unit: pm
Algorithm Grid search PSO BAS BAS-PSO Improved BAS-PSO
Maximum residual error ±6.07 ±2.99 ±6.17 ±2.87 ±2.20
Standard deviation 2.07 1.41 2.83 1.41 1.07

Table 2. Modeling compensation results of different optimization algorithms for G2unit: pm
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Table 2. Modeling compensation results of different optimization algorithms for G2unit: pm
Algorithm Grid search PSO BAS BAS-PSO Improved BAS-PSO
Maximum residual error ±8.65 ±3.90 ±12.77 ±3.30 ±3.03
Standard deviation 2.82 1.67 4.66 1.51 1.32

Table 3. Temperature compensation error of different algorithmsunit: pm
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Table 3. Temperature compensation error of different algorithmsunit: pm
Algorithm Maximum residual error
Ref.［16］ ±3.50
Ref.［7］ ±22.00
Ref.［17］ ±18.22
Optimized LSSVM ±3.03

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Wenjuan Sheng, Zhengbin Hu, Ning Yang, Gangding Peng. Demodulation of Temperature Stabilized Fiber Bragg Grating Sensor Based on Optimized Least Square Support Vector Machine[J]. Laser & Optoelectronics Progress, 2022, 59(3): 0305002

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Paper Information

Category: Diffraction and Gratings

Received: May. 7, 2021

Accepted: Jun. 11, 2021

Published Online: Jan. 24, 2022

The Author Email: Wenjuan Sheng (wenjuansheng@shiep.edu.cn)

DOI:10.3788/LOP202259.0305002

Topics

laser devices and laser physics

Lasers and Laser Optics

Laser physics

laser manufacturing

Instrumentation, Measurement and Metrology

Table 1. Modeling compensation results of different optimization algorithms for G1unit: pm

Table 1. Modeling compensation results of different optimization algorithms for G1unit: pm

Table 2. Modeling compensation results of different optimization algorithms for G2unit: pm

Table 2. Modeling compensation results of different optimization algorithms for G2unit: pm

Table 3. Temperature compensation error of different algorithmsunit: pm

Table 3. Temperature compensation error of different algorithmsunit: pm