基于分布式光纤传感与U-Net网络的复合材料分层损伤定量识别方法  

Quantitative Identification Method of Composite Material Delamination Damage Based on Distributed Optical Fiber Sensing and U-Net Network

作　　者：武湛君 董珊珊 李建乐 朱明睿 张仕承 刘海涛[2] 孙亮[2] 李汉克 董孜劢 徐浩 WU Zhanjun;DONG Shanshan;LI Jianle;ZHU Mingrui;ZHANG Shicheng;LIU Haitao;SUN Liang;LI Hanke;DONG Zimai;XU Hao(College of Materials Science and Engineering,Dalian University of Technology,Dalian 116024,China;College of Mechanics and Aerospace Engineering,Dalian University of Technology,Dalian 116024,China)

机构地区：[1]大连理工大学材料科学与工程学院,大连116024 [2]大连理工大学力学与航空航天学院,大连116024

出　　处：《航空制造技术》2024年第13期20-27,共8页Aeronautical Manufacturing Technology

基　　金：国家自然科学基金(12072056)。

摘　　要：结构健康监测(SHM)是确保飞行器复合材料结构安全性和完整性的重要手段。基于背向瑞利散射的分布式光纤传感器可以通过测量高密度的应变分布为复合材料损伤监测提供数据支持。然而,结构应变分布特征和损伤的映射关系较为复杂,无法直接根据应变分布准确判定损伤的定量信息。另外,分布式光纤传感器数据量大,通过人为分析应变数据识别损伤较为耗时且准确性偏低。为了应对这一挑战,提出了一种基于分布式光纤传感数据与U-Net神经网络的智能损伤识别方法,旨在自动精确识别复合材料中常见的分层损伤。首先,通过有限元仿真构建U-Net神经网络的训练集与验证集;随后进行含分层损伤复合材料板的悬臂加载试验,通过分布式光纤传感器采集结构应变分布数据作为测试集。损伤识别结果表明,U-Net神经网络可以对分层损伤的位置、尺寸与形状进行较为精确的定量识别。Structural health monitoring is a crucial approach for ensuring the safety and integrity of composite material structures in aircraft.Distributed fiber optic sensors based on backscattered Rayleigh scattering provide data support for composite material damage monitoring by measuring high-density strain distributions.However,the mapping relationship between structural strain distribution characteristics and damage is complex,making it challenging to accurately determine the quantitative information of damage based solely on strain distribution.Additionally,the large volume of data from distributed fiber optic sensors makes manual analysis of strain data time-consuming and less accurate.To address this challenge,an intelligent damage identification method based on distributed fiber optic sensing data and the U-Net neural network is proposed.It aims to automate the precise identification of common delamination damage in composite materials.Initially,training and validation sets for the U-Net neural network are constructed through finite element simulations.Subsequently,cantilever loading tests of composite material plates with delamination damage are conducted,and structural strain distribution data are collected as a test set using distributed fiber optic sensors.The damage identification results demonstrate that the U-Net neural network can accurately quantify the position,size,and shape of delamination damage.

关 键 词：结构健康监测(SHM) 复合材料结构 分布式光纤传感器 深度学习 U-Net神经网络 

分 类 号：V214.8[航空宇航科学与技术—航空宇航推进理论与工程] V414.8