基于粒子群优化Elman神经网络的流量温度复合测量

Flow and temperature composite measurement based on particle swarm optimization Elman neural network

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 m^(3)/h范围内,流量最大误差、均方误差分别为0.086 m^(3)/h和0.0027 m^(3)/h,温度最大误差、均方误差分别为0.084℃和0.0017℃。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 m^(3)h and 0.0027 m^(3)/h,in the flow range of 2 m^(3)/h—30 m^(3)/h after FBG sensor is compensated,the maximum error and mean square error of temperature are 0.084℃and 0.0017℃,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.