基于粒子群优化Elman神经网络的流量温度复合测量
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重庆交通大学

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中图分类号:

TN253、TH814

基金项目:

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


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

Chongqing Jiaotong University

Fund Project:

Supported by the Science and Technology Research Program of Chongqing Municipal Education Commission (Grant No. KJZD-K202200705)

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    摘要:

    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(Particle Swarm Optimization, PSO)优化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 m3/h~30 m3/h范围内,流量最大误差、均方误差分别为0.086 m3/h和0.0027 m3/h,温度最大误差为、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。

    Abstract:

    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 maximum error and the mean error of the FBG sensor in the range of 2 m3/h~30 m3/h range of the FBG sensor is 0.086 m3/h and 0.0027 m3/h, 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.

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  • 收稿日期:2023-03-30
  • 最后修改日期:2023-07-04
  • 录用日期:2023-07-12
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