首页|基于WOA-ELM的空间分层结构FBG三维振动加速度传感器非线性解耦

基于WOA-ELM的空间分层结构FBG三维振动加速度传感器非线性解耦

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针对三维振动加速度传感器存在的维间耦合干扰问题,以空间分层结构光纤布拉格光栅(FBG)三维振动加速度传感器为研究对象,阐述了三维振动加速度传感的基本原理.其次,构建了振动加速度动态标定实验平台,并分析了传感器的结构耦合特性.最后,提出一种基于鲸鱼算法优化极限学习机(WOA-ELM)的神经网络模型并进行了非线性解耦实验,其结果显示,在x、y、z三轴的平均测量误差分别降至1.58%、1.17%、0.95%,平均Ⅰ类和Ⅱ类误差最大值分别降至0.73%和0.37%.为验证解耦效果,将WOA-ELM与其他算法等进行解耦效果对比.结果表明,WOA-ELM更有效地降低三维振动加速度传感器的维间耦合干扰,提高测量精度.
Nonlinear decoupling of spatially hierarchically structured FBG 3D vibration acceleration sensor based on WOA-ELM
To address the issue of interdimensional coupling interference in three-dimensional vibration acceleration sensors,this paper focuses on the spatial layered structure fiber bragg grating(FBG)three-dimensional vibration acceleration sensor.It outlines the basic principle of three-dimensional vibration acceleration sensing.An experimental platform for dynamic calibration of the vibration acceleration is constructed,and the structural coupling characteristics of the sensor are analyzed.A neural network model based on the whale optimization algorithm and extreme learning machine(WOA-ELM)is proposed for non-linear decoupling experiments.The results show that the average measurement errors in the x,y,and z axes are reduced to 1.58%,1.17%,and 0.17%,respectively.Additionally,the maximum values of the average class Ⅰ and class Ⅱ errors are reduced to 0.73%and 0.37%,respectively.The decoupling effect of the WOA-ELM is compared with other algorithms,and the results demonstrate that WOA-ELM is more effective in reducing inter-dimensional coupling interference in the three-dimensional vibration accelerometer sensor,thereby improving measurement accuracy.

fiber Bragg gratingthree-dimensional vibration acceleration sensorinterdimensional couplingwhale optimization algorithmextreme learning machine

孙世政、武宇峰、何江、徐向阳、陈仁祥

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重庆交通大学机电与车辆工程学院 重庆 400074

光纤布拉格光栅 三维振动加速度传感器 维间耦合 鲸鱼优化算法 极限学习机

国家自然科学基金青年科学基金项目重庆市自然科学基金创新发展联合基金项目重庆市教育委员会科学技术研究项目重庆市技术创新与应用发展专项重大项目

52105542CSTB2023NSCQ-LZX0081KJZD-K202200705CSTB2023TIAD-STX0016

2024

仪器仪表学报
中国仪器仪表学会

仪器仪表学报

CSTPCD北大核心
影响因子:2.372
ISSN:0254-3087
年,卷(期):2024.45(7)
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