Reconstruction Technology of Flexible Structure Shape Based on FBG Sensor Array and Deep Learning Algorithm

Kelong Huang,Faye Zhang,Jie Yan,Lei Zhang,Mingshun Jiang,Qingmei Sui

doi:10.32604/sdhm.2022.018202

Abstract

A structural displacement field reconstruction method is proposed to aim at the problems of deformation monitoring and displacement field reconstruction of flexible plate-like structures in the aerospace field. This method combines the deep neural network model of the cross-layer connection structure with the fiber grating sensor network. This paper first introduces the principle of strain detection of fiber grating sensor, studies the mapping relationship between strain and displacement, and proposes a strain-displacement conversion model based on an improved neural network. Then the intelligent structure deformation monitoring system is built. By controlling the stepping distance of the motor to produce different deformations of the plate structure, the strain information and real displacement information are obtained based on the high-density fiber grating sensor network and the dial indicator array. Finally, based on the deformation prediction model obtained by training, the displacement field reconstruction of the structure under different deformation states is realized. Experimental results show that the mean absolute error of the deformation of the measuring points obtained by this method is less than 0.032 mm. This method is feasible in theory and practice and can be applied to the deformation monitoring of aerospace vehicle structures.

Highlights

The flexible plate structure is widely used in the aerospace field
Compared with the other traditional structural reconstruction models introduced in the introduction, the reconstruction model proposed in this paper establishes the mapping relationship between structural strain and displacement, and obtains displacement information directly from the collected strain information, without the need to establish complex conversion equations
Based on the conventional neural network, this paper proposes a cross-layer connection neural network reconstruction model based on the idea of residual learning

Summary

Introduction

The flexible plate structure is widely used in the aerospace field. This structure has the characteristics of large span, lightweight, and low rigidity [1]. The camera method is a traditional deformation monitoring technology, which mainly obtains the deformation parameters of the structure by taking an image of the measured structure and processing the image [6,7]. This method has disadvantages such as susceptibility to environmental influences and poor anti-vibration interference. Measuring with resistance strain gauge is to measure the structural strain based on the principle that the resistance value changes with strain by pasting the resistance strain gauge on the structure’s surface This method has the disadvantages of being susceptible to noise interference and complicated configuration system.

Methods

Findings

Discussion

Conclusion