Fu Zhen, Zhao Yong, Bao Hong, Zhao Feifei
Key Laboratory of Electronic Equipment Structure Design of Ministry of Education, Xidian University, Xi'an 710071, China.
School of New Energy Vehicles, Henan Mechanical and Electrical Vocational College, Zhengzhou 451191, China.
Sensors (Basel). 2019 Jul 30;19(15):3350. doi: 10.3390/s19153350.
In order to monitor the variable-section wing deformation in real-time, this paper proposes a dynamic reconstruction algorithm based on the inverse finite element method and fuzzy network to sense the deformation of the variable-section beam structure. Firstly, based on Timoshenko beam theory and inverse finite element framework, a deformation reconstruction model of variable-section beam element was established. Then, considering the installation error of the fiber Bragg grating (FBG) sensor and the dynamic un-modeled error caused by the difference between the static model and dynamic model, the real-time measured strain was corrected using a solidified fuzzy network. The parameters of the fuzzy network were learned using support vector machines to enhance the generalization ability of the fuzzy network. The loading deformation experiment shows that the deformation of the variable section wing can be reconstructed with the proposed algorithm in high precision.
为了实时监测变截面机翼变形，本文提出了一种基于逆有限元法和模糊网络的动态重建算法，以感知变截面梁结构的变形。首先，基于铁木辛柯梁理论和逆有限元框架，建立了变截面梁单元的变形重建模型。然后，考虑光纤布拉格光栅（FBG）传感器的安装误差以及静态模型与动态模型差异引起的动态未建模误差，利用固化模糊网络对实时测量应变进行校正。采用支持向量机学习模糊网络的参数，以增强模糊网络的泛化能力。加载变形实验表明，所提算法能够高精度地重建变截面机翼的变形。
