改进光纤光栅应变分布解调算法中优化目标函数的理论与方法
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摘要
分析了光纤布拉格光栅(FBG)反射光谱的获取方式,根据反射光谱的特征,提出了利用相关系数改进FBG应变分布解调算法中优化目标函数的理论方法。结合差分进化算法,对改进算法与传统算法的性能进行了对比仿真。仿真结果表明,传统算法仅适用于能获知FBG真实反射率的情况,而改进算法还适用于无法获知FBG真实反射率的情况,所提方法提升了FBG应变分布解调算法的实际应用能力。
The obtainment method of fiber Bragg grating(FBG) reflection spectra is analyzed, and according to the characteristics of reflection spectra, a theoretical method is proposed for improving the optimization objective functions in the demodulation algorithm of FBG strain distributions using correlation coefficients. The performances of improved and traditional algorithms are compared by simulation combined with the differential evolution algorithm. The simulation results show that the traditional algorithm is only suitable for the situation where the true reflectivity of FBG is known, in contrast the improved algorithm can be applied to the situation where the true reflectivity of FBG is unknown. The proposed method can be used to improve the practicality of demodulation algorithms of FBG strain distribution.
The obtainment method of fiber Bragg grating(FBG) reflection spectra is analyzed, and according to the characteristics of reflection spectra, a theoretical method is proposed for improving the optimization objective functions in the demodulation algorithm of FBG strain distributions using correlation coefficients. The performances of improved and traditional algorithms are compared by simulation combined with the differential evolution algorithm. The simulation results show that the traditional algorithm is only suitable for the situation where the true reflectivity of FBG is known, in contrast the improved algorithm can be applied to the situation where the true reflectivity of FBG is unknown. The proposed method can be used to improve the practicality of demodulation algorithms of FBG strain distribution.
引文
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[10] Wang J, Wang Z F, Sui Q M, et al. Study of FBG strain distribution reconstruction based on improved genetic algorithm dual constraint[J]. Chinese Journal of Lasers, 2012, 39(3): 0305004. 王静, 王正方, 隋青美, 等. 基于改进遗传算法双重约束的FBG应变分布重构研究[J]. 中国激光, 2012, 39(3): 0305004.
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[12] Huang G J, Wei C B, Chen S Y, et al. Reconstruction of structural damage based on reflection intensity spectra of fiber Bragg gratings[J]. Measurement Science and Technology, 2014, 25(12): 125109.
[13] Shi C Z, Zeng N, Zhang M, et al. Adaptive simulated annealing algorithm for the fiber Bragg grating distributed strain sensing[J]. Optics Communications, 2003, 226(1/2/3/4/5/6): 167-173.
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[15] Rodriguez-Cobo L, Cobo A, Lopez-Higuera J M. Recovering a fiber Bragg grating axial strain distribution from its reflection spectrum[J]. Optics Letters, 2013, 38(13): 2327-2329.
[16] Wang H T, Zhang G Z, Zheng S J. Reconstruction of the nonuniform strain profile for fiber Bragg grating using dynamic particle swarm optimization algorithm and its experimental verification[J]. Optical Engineering, 2013, 52(10): 107103.
[17] Zou H B, Liang D K, Zeng J, et al. Quantum-behaved particle swarm optimization algorithm for the reconstruction of fiber Bragg grating sensor strain profiles[J]. Optics Communications, 2012, 285(5): 539-545.
[18] Wen X Y, Yu Q. Reconstruction of strain distribution in fiber Bragg gratings with differential evolution algorithm[J]. Optoelectronics Letters, 2008, 4(6): 403-406.
[19] Guo H Y, Zheng Y, Tang J G, et al. Reflectivity measurement of weak fiber Bragg grating (FBG)[J]. Journal of Wuhan University of Technology-Materials Science Edition, 2012, 27(6): 1177-1179.
[20] Vesterstrom J, Thomsen R. A comparative study of differential evolution, particle swarm optimization, and evolutionary algorithms on numerical benchmark problems[J]. Proceedings of the 2004 Congress on Evolutionary Computation, 2004: 1980-1987.
[21] Ali T A, Shehata M I, Mohamed N A. Design and performance investigation of a highly accuratea-podized fiber Bragg grating-based strain sensor in single and quasi-distributed systems[J]. Applied Optics, 2015, 54(16): 5243-5251.
[2] Liao Y B, Yuan L B, Tian Q. The 40 years of optical fiber sensors in China[J]. Acta Optica Sinica, 2018, 38(3): 0328001. 廖延彪, 苑立波, 田芊. 中国光纤传感40年[J]. 光学学报, 2018, 38(3): 0328001.
[3] Qian M Y, Yu Y L. Tactile sensing of fiber Bragg grating based on back propagation neural network[J]. Chinese Journal of Lasers, 2017, 44 (8): 0806001. 钱牧云, 余有龙. 基于逆传播神经网络的光纤布拉格光栅触觉传感[J]. 中国激光, 2017, 44(8): 0806001.
[4] Sun S Q, Chu F H. Temperature compensation of fiber Bragg grating current sensor based on optimized neural network algorithm[J]. Acta Optica Sinica, 2017, 37(10): 1006001. 孙诗晴, 初凤红. 基于优化神经网络算法的光纤布拉格光栅电流传感器的温度补偿[J]. 光学学报, 2017, 37(10): 1006001.
[5] Zhang W. Key technology for reliability of fiber Bragg grating strain sensing system[D]. Chongqing: Chongqing University, 2016: 1-5. 张伟. 光纤布拉格光栅应变传感系统可靠性的关键技术研究[D]. 重庆: 重庆大学, 2016: 1-5.
[6] Guo Y X, Kong J Y, Xiong H G, et al. Advances in robot force/torque tactile sensing technology based on fiber Bragg grating[J]. Laser & Optoelectronics Progress, 2016, 53(5): 050006. 郭永兴, 孔建益, 熊禾根, 等. 基于光纤Bragg光栅的机器人力/力矩触觉传感技术研究进展[J]. 激光与光电子学进展, 2016, 53(5): 050006.
[7] Gill A, Peters K, Studer M. Genetic algorithm for the reconstruction of Bragg grating sensor strain profiles[J]. Measurement Science and Technology, 2004, 15(9): 1877-1884.
[8] Zheng S J, Zhang N, Xia Y J, et al. Research on non-uniform strain profile reconstruction along fiber Bragg grating via genetic programming algorithm and interrelated experimental verification[J]. Optics Communications, 2014, 315: 338-346.
[9] Zhang L Y, Shen X Y, Han Y, et al. A new population initialization method of genetic algorithm applied in FBG inhomogeneous strain demodulation[J]. Proceedings of SPIE, 2015, 9620: 96200Z.
[10] Wang J, Wang Z F, Sui Q M, et al. Study of FBG strain distribution reconstruction based on improved genetic algorithm dual constraint[J]. Chinese Journal of Lasers, 2012, 39(3): 0305004. 王静, 王正方, 隋青美, 等. 基于改进遗传算法双重约束的FBG应变分布重构研究[J]. 中国激光, 2012, 39(3): 0305004.
[11] Xia Y J, Zheng S J. Non-uniform strain profile reconstruction of FBG via genetic programming[J]. Journal of Optoelectronics·Laser, 2010, 21(8): 1141-1145. 夏彦君, 郑世杰. FBG轴向非均匀应变分布的遗传规划重构方法[J]. 光电子·激光, 2010, 21(8): 1141-1145.
[12] Huang G J, Wei C B, Chen S Y, et al. Reconstruction of structural damage based on reflection intensity spectra of fiber Bragg gratings[J]. Measurement Science and Technology, 2014, 25(12): 125109.
[13] Shi C Z, Zeng N, Zhang M, et al. Adaptive simulated annealing algorithm for the fiber Bragg grating distributed strain sensing[J]. Optics Communications, 2003, 226(1/2/3/4/5/6): 167-173.
[14] Detka M, Kaczmarek Z. Distributed strain reconstruction based on a fiber Bragg grating reflection spectrum[J]. Metrology and Measurement Systems, 2013, 20(1): 53-64.
[15] Rodriguez-Cobo L, Cobo A, Lopez-Higuera J M. Recovering a fiber Bragg grating axial strain distribution from its reflection spectrum[J]. Optics Letters, 2013, 38(13): 2327-2329.
[16] Wang H T, Zhang G Z, Zheng S J. Reconstruction of the nonuniform strain profile for fiber Bragg grating using dynamic particle swarm optimization algorithm and its experimental verification[J]. Optical Engineering, 2013, 52(10): 107103.
[17] Zou H B, Liang D K, Zeng J, et al. Quantum-behaved particle swarm optimization algorithm for the reconstruction of fiber Bragg grating sensor strain profiles[J]. Optics Communications, 2012, 285(5): 539-545.
[18] Wen X Y, Yu Q. Reconstruction of strain distribution in fiber Bragg gratings with differential evolution algorithm[J]. Optoelectronics Letters, 2008, 4(6): 403-406.
[19] Guo H Y, Zheng Y, Tang J G, et al. Reflectivity measurement of weak fiber Bragg grating (FBG)[J]. Journal of Wuhan University of Technology-Materials Science Edition, 2012, 27(6): 1177-1179.
[20] Vesterstrom J, Thomsen R. A comparative study of differential evolution, particle swarm optimization, and evolutionary algorithms on numerical benchmark problems[J]. Proceedings of the 2004 Congress on Evolutionary Computation, 2004: 1980-1987.
[21] Ali T A, Shehata M I, Mohamed N A. Design and performance investigation of a highly accuratea-podized fiber Bragg grating-based strain sensor in single and quasi-distributed systems[J]. Applied Optics, 2015, 54(16): 5243-5251.