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

Abstract

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

CLC Number:

V219

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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Damage recognition of composite structures based on domain adaptive model

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