【摘要】 针对光纤光栅自身对温度和应变的交叉敏感性,以及光纤光栅压力传感器的输出受环境温度影响很大且不易消除的问题,以聚合物封装的光纤光栅传感器为例,提出了用BP神经网络实现光纤光栅压力传感器温度补偿的方法,解决了传感器输出特性的非线性校正的问题.通过Matlab仿真结果显示,系统最大测量误差由1915%降低到4.26%;实验证明该方法可以有效地减少温度对光纤光栅压力传感器测量精度的影响.更多还原

【Abstract】 Fiber Bragg Grating (FBG)itself has cross-sensitivity to temperature and stress, and the output of an FBG pressure sensor is seriously influenced by environmental temperature, which is hard to be got rid of. Take the FBG pressure sensor with polymer package as an example, this paper introduces the way to realize temperature compensation for the FBG pressure sensor by creating a BP neural network, and thus solve the problem of nonlinear adjustment to the output characteristic of the sensor. The simulation by Matlab reveals that the influence of environmental temperature fluctuation can be eliminated effectively. The maximum measurement error of the system has decreased from 1915% to 4.26%. The experiment proves that the method proposed can effectively reduce the influence of temperature on the measuring precision of the FBG pressure sensor.更多还原