针对光纤布拉格光栅(fiber Bragg grating,FBG)传感器应变温度交叉敏感问题,提出了基于粒子群优化(particle swarm optimization,PSO)Elman神经网络的温度补偿算法。首先,基于流体力学和FBG传感原理,设计了探针式FBG流量温度复合测量传感器,分析了流量温度复合传感机理；然后,搭建了流量温度复合测量实验平台获取测量数据,进行了误差分析；最后,利用PSO优化Elman神经网络获取最优隐含层数和最优函数组合,构建PSO-Elman算法模型对测量数据 进行温度补偿,补偿后FBG传感器在流量2—30 m³/h范围内,流量最大误差、均方误差分别为0.086 m³/h和0.002 7 m³/h,温度最大误差、均方误差分别为0.084 ℃和0.001 7 ℃。实验结果表明:该传感器可实现管道内流体流量温度复合测量,结合PSO-Elman算法可以有效降低应变温度交叉敏感引起的误差,显著提升传感器测量性能。

For the strain-temperature cross-sensitivity problem of fiber Bragg grating (FBG) sensor,a temperature compensation algorithm based on Elman neural network with particle swarm optimization (PSO) is proposed.Firstly,based on the principles of fluid mechanics and FBG sensing,a probe-type FBG flow-temperature composite measurement sensor is designed and the flow-temperature composite sensing mechanism is analyzed;then,a flow-temperature composite measurement experimental platform is built,measurement data are obtained,and error analysis is performed;finally,the optimal number of implied layers and the optimal combination of functions are obtained using the PSO-optimized Elman neural network,the flow maximum error and the mean error of the FBG sensor are 0.086 m³h and 0.002 7 m³/h, in the flow range of 2 m³/h—30 m³/h after FBG sensor is compensated,the maximum error and mean square error of temperature are 0.084 ℃ and 0.001 7 ℃,respectively.The experimental results show that the sensor can realize the composite measurement of fluid flow and temperature in the pipeline,and the combination of the PSO-Elman algorithm can effectively reduce the error caused by strain-temperature cross-sensitivity and significantly improve the measurement performance of the sensor.

TN253TH814

国家自然科学基金青年科学基金(52105542)、 “成渝地区双城经济圈建设” 科技创新项目(KJCX2020032) 和重庆市教委科学技术研究项目(KJZD-K202200705) 资助项目