基于PSO-SWELM的应变传感器在线温度补偿方法

On-line Temperature Compensation Method for Strain Transducers Based on PSO-SWELM

摘要:

针对光纤布拉格光栅(Fiber Bragg Grating,FBG)应变传感器受环境温度影响而造成的波长漂移问题,提出粒子群优化(Particle Swarm Optimization,PSO)结合滑动窗口极限学习机(Sliding Window Extreme Learning Machine,SWELM)的在线预测算法对其进行温度补偿。利用PSO算法优化SWELM网络滑动窗口和隐含层神经元数目,提升了模型的预测精度,模型预测均方根误差最小能达到0.06pm。PSO-SWELM实现了对应变传感器数据的在线更新及波长漂移预测,对实时测量数据和预测数据进行差分运算完成温度补偿。与SWELM的对比分析结果表明,PSO-SWELM算法的预测精度平均提升了11.04%,并具有良好的温度补偿效果。

Abstract:

Aiming at the wavelength drift problem caused by the influence of ambient temperature on fiber Bragg grating (FBG) strain sensor, the online prediction algorithm that combined particle swarm optimization (PSO) with sliding window extreme learning machine (SWELM) is proposed for temperature compensation. The PSO algorithm was used to optimize the sliding window and the number of neurons in the hidden layer of the SWELM network, which improved the prediction accuracy of the model, and the minimum root mean square error of the model prediction could reach 0.06pm. PSO-SWELM realized online update and wavelength drift prediction of strain sensor data, and differential calculation of real-time measurement data and prediction data completed temperature compensation. PSO-SWELM was compared with SWELM, and the results show that the accuracy of the proposed algorithm is improved by an average of 11.04%, and has good temperature compensation effect.