基于神经网络的光纤光栅应变传感器重构

Reconstruction of Fiber Bragg Grating Strain Sensor Based on Neural Networks

提出一种基于径向基函数神经网络模型来重构蜕化的光纤光栅应变传感器的方法.以结构健康监测系统中应变测量系统的光纤光栅应变传感器之间关联分析为基础,依据径向基函数神经网络强大的函数逼近能力,利用关联度高、运行良好的应变传感器去重构蜕化的应变传感器,保证重构的应变传感器与蜕化传感器有较高的数据准确度.实验结果证实了该方法在理论和实践上的精确性和可行性.

A novel reconstruction method based on RBF neural networks is proposed for restoring the degeneration Fiber Bragg Grating（FBG） strain sensor. According the correlation analysis of bridgers FBG strain monitoring sites and none-linear approximation of RBF neural networks, the degeneration FBG strain is reconstructed by the regular and the most correlative strain sensor, which assures more accurate and accords with practice. Simulation results verify the effectiveness of the designed method and theoretical discussions.