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.