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

BAO Wenqiang; MA Jitong; ZHAO Sen; YANG Zhengyan

doi:10.21656/1000-0887.450162

Volume 46 Issue 8

Aug. 2025

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Article Navigation > Applied Mathematics and Mechanics > 2025 > 46(8): 1027-1036

BAO Wenqiang, MA Jitong, ZHAO Sen, YANG Zhengyan. A Lightweight Convolutional Neural Network for Guided Wave Based Damage Detection in Composite Structures[J]. Applied Mathematics and Mechanics, 2025, 46(8): 1027-1036. doi: 10.21656/1000-0887.450162

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

doi: 10.21656/1000-0887.450162

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)

Received Date: 2024-05-31
Rev Recd Date: 2024-09-18

Available Online: 2025-09-10

Abstract

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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References(20)

References

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