基于域自适应的复合材料结构损伤识别方法

doi:10.7527/S1000-6893.2022.26752

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基于域自适应的复合材料结构损伤识别方法

王育鹏¹, 吕帅帅², 杨宇², 李嘉欣², 王叶子²

1. 西北工业大学航空学院, 西安 710072;
2. 中国飞机强度研究所, 西安 710065

收稿日期:2021-12-06 修回日期:2022-03-08 出版日期:2022-06-15 发布日期:2022-03-04
通讯作者: 吕帅帅,E-mail:647817545@qq.com E-mail:647817545@qq.com
基金资助:
航空科学基金（2020Z061023001）

Damage recognition of composite structures based on domain adaptive model

WANG Yupeng¹, LYU Shuaishuai², YANG Yu², LI Jiaxin², WANG Yezi²

1. School of Aeronautics, Northwestern Polytechnical University, Xi'an 710072, China;
2. Aircraft Strength Research Institute of China, Xi'an 710065, China

Received:2021-12-06 Revised:2022-03-08 Online:2022-06-15 Published:2022-03-04
Supported by:
Aeronautical Science Foundation of China(2020Z061023001)

摘要/Abstract

摘要： 深度学习模型能够辅助提高基于导波的复合材料结构损伤监测的可靠性，但需要大量的损伤样本。以大量的模拟损伤样本和少量的真实损伤样本为基础，设计一种基于域自适应的损伤识别模型，实现从模拟损伤识别向真实损伤识别能力的迁移。首先，通过粘贴质量块收集大量模拟损伤数据，设计卷积-时序混合神经网络，实现对模拟损伤的高准确率识别；然后，在模型中加入域自适应模块，使模拟损伤和真实损伤数据在特征空间内分布规律近似，进而在无需对真实损伤进行标注的情况下，实现准确识别。实验结果表明，该方法对真实损伤的检出准确率为85.7%，优于传统深度学习模型。

关键词: 域自适应, 导波, 结构健康监测, 复合材料, 迁移学习

Abstract: Deep learning can help to improve the guided-wave-based damage detection of composite structures; however, it needs a large number of damage samples. Based on a large number of simulated damage samples and a small number of real ones, a domain adaptive damage identification model is designed to realize the migration from simulated damage detection to real damage detection. Firstly, guided-wave signals of faked damage are collected extensively in the form of mass attachment on to the structure surface, and corresponding deep learning model based on convolutional-timing-sequential hybrid neural network is designed to achieve a high accuracy of damage detection. Secondly, a certain amount of guided-wave signals of real damage are collected, and a domain adaptive module is adopted by the model, which approximates the data distribution law of simulated damage and real damage in the feature space. With this framework, the model could detect the real damage without the labelling process in advance. The experimental results demonstrate the detection accuracy of 85.7%, which is ahead of other traditional deep learning models.

Key words: domain adaptive, guided-wave, structural health monitoring, composite material, transfer learning

中图分类号:

V219

王育鹏, 吕帅帅, 杨宇, 李嘉欣, 王叶子. 基于域自适应的复合材料结构损伤识别方法[J]. 航空学报, 2022, 43(6): 526752-526752.

WANG Yupeng, LYU Shuaishuai, YANG Yu, LI Jiaxin, WANG Yezi. Damage recognition of composite structures based on domain adaptive model[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2022, 43(6): 526752-526752.

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E-mail：hkxb@buaa.edu.cn

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主管单位：中国科学技术协会主办单位：中国航空学会北京航空航天大学

基于域自适应的复合材料结构损伤识别方法

Damage recognition of composite structures based on domain adaptive model

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