基于导波和轻量化卷积神经网络的复合材料结构损伤识别方法

包文强; 马济通; 赵森; 杨正岩

doi:10.21656/1000-0887.450162

基于导波和轻量化卷积神经网络的复合材料结构损伤识别方法

doi: 10.21656/1000-0887.450162

1. 大连海事大学信息与科学技术学院，辽宁大连 116026;
2. 江南大学纤维工程与装备技术学院，江苏无锡 214122

基金项目:

国家自然科学基金（12102075；62101090）；中国科协青年人才托举工程(2022QNRC001)；中央高校基本科研业务费（3132024236）

详细信息

作者简介:
包文强（1999—），男，硕士(E-mail: baowenqiang2022@163.com)；杨正岩（1993—），女，教授，博士(通讯作者. E-mail: zyyang1993@jiangnan.edu.cn).

通讯作者:
杨正岩（1993—），女，教授，博士(通讯作者. E-mail: zyyang1993@jiangnan.edu.cn).

中图分类号: V11
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出版历程
- 收稿日期: 2024-05-31
- 修回日期: 2024-09-18
- 网络出版日期: 2025-09-10

A Lightweight Convolutional Neural Network for Guided Wave Based Damage Detection in Composite Structures

1. School of Information Science and Technology, Dalian Maritime University, Dalian, Liaoning 116026, P.R.China;
2. School of Fiber Engineering and Equipment Technology, Jiangnan University, Wuxi, Jiangsu 214122, P.R.China

Funds:

The National Science Foundation of China(12102075；62101090)

摘要

摘要: 针对有限计算资源下复合材料的实时监测问题，该文提出了基于超声导波和轻量化卷积神经网络（one-dimension convolutional neural network-deformable convolution attention，CDCA）的损伤实时识别方法.在该方法中，为了压缩多个路径的导波信号，首先提出了改进差分驱动的平均聚合（improved differential-driven piecewise aggregate approximation， IDPAA）算法，利用该方法可以显著减少计算量；其次，提出了轻量化可变形卷积注意力(deformable convolution attention，DCA）机制，让模型聚焦在与损伤相关的像素级特征，从而实现更高效、准确的结构损伤识别；最后，通过结合一维卷积神经网络（one-dimension convolutional neural network，1D-CNN）和DCA机制，构建了CDCA模型.该模型不仅可以在有限资源环境下运行，还能实现含噪声工况下的损伤实时识别.在真实数据集上验证了所提出方法的有效性.试验结果表明，所提出的损伤识别方法有较高的损伤识别准确性，准确率可达98%，并且大幅提高了模型计算效率，相较于其他先进深度学习模型具有显著的优势.
- 结构健康监测 /
- 超声导波 /
- 复合材料 /
- 深度学习 /
- 轻量化卷积神经网络
Abstract: For real-time monitoring composite structures under limited resource conditions, a machine learning-assisted method based on ultrasonic guided waves was proposed for real-time damage detection. In the proposed method, firstly, an improved differential-driven piecewise aggregate approximation (IDPAA) algorithm was designed to compress multi-path guided wave signals, thereby greatly reducing computational requirements. Secondly, a novel lightweight deformable convolution attention (DCA) mechanism was developed, which can help model focus on pixel-level feature information related to damage, enabling more efficient and accurate structural damage detection. Finally, by integrating a one-dimension CNN (1D CNN) with the designed DCA mechanism, a lightweight 1D-CNN-DCA (CDCA）model was proposed, which not only can work under the limited resource conditions but also can effectively real-time monitor structural damages under noise environments. The experiment of the proposed damage detection method on real-world datasets demonstrates its effectiveness, where the results reveal that it can reach 98% accuracy and significantly improves computation efficiency, outperforming other advanced monitoring approaches.
- structural health monitoring /
- ultrasonic guided wave /
- composite material /
- deep learning /
- lightweight convolutional neural network

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参考文献(20)

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基于导波和轻量化卷积神经网络的复合材料结构损伤识别方法

doi: 10.21656/1000-0887.450162

作者简介:
包文强（1999—），男，硕士(E-mail: baowenqiang2022@163.com)；杨正岩（1993—），女，教授，博士(通讯作者. E-mail: zyyang1993@jiangnan.edu.cn).

通讯作者:
杨正岩（1993—），女，教授，博士(通讯作者. E-mail: zyyang1993@jiangnan.edu.cn).

计量

A Lightweight Convolutional Neural Network for Guided Wave Based Damage Detection in Composite Structures

计量

目录

留言板

基于导波和轻量化卷积神经网络的复合材料结构损伤识别方法

doi: 10.21656/1000-0887.450162

作者简介: 包文强（1999—），男，硕士(E-mail: baowenqiang2022@163.com)； 杨正岩（1993—），女，教授，博士(通讯作者. E-mail: zyyang1993@jiangnan.edu.cn).

通讯作者: 杨正岩（1993—），女，教授，博士(通讯作者. E-mail: zyyang1993@jiangnan.edu.cn).

计量

出版历程

A Lightweight Convolutional Neural Network for Guided Wave Based Damage Detection in Composite Structures

计量

出版历程

目录

作者简介:
包文强（1999—），男，硕士(E-mail: baowenqiang2022@163.com)；杨正岩（1993—），女，教授，博士(通讯作者. E-mail: zyyang1993@jiangnan.edu.cn).

通讯作者:
杨正岩（1993—），女，教授，博士(通讯作者. E-mail: zyyang1993@jiangnan.edu.cn).