###### Abstract  
Woven fabric composite structures are applied in a wide range of industrial applications. Composite structures are vulnerable to damage from working in complex conditions and environments, which threatens the safety of the in-service structure. Damage detection based on Lamb waves is one of the most promising structural health monitoring (SHM) techniques for composite materials. In this paper, based on guided Lamb waves, a lightweight deep-learning approach is proposed for identifying damaged regions in woven fabric composite structures. The designed deep neural networks are built using group convolution and depthwise separated convolution, which can reduce the parameters considerably. The input of this model is a multi-channel matrix transformed by a one-dimensional guided wave signal. In addition, channel shuffling is introduced to increase the interaction between features, and a multi-head self-attention module is designed to increase the model's global modeling capabilities. The relevant experimental results show that the proposed SHM approach can achieve a recognition accuracy of 100% after only eight epochs of training, and the proposed LCANet has only 4.10% of the parameters of contrastive SHM methods, which further validates the effectiveness and reliability of the proposed method. composite structure; structural health monitoring; damage detection; deep learning; Lamb wave; convolutional neural network +
Footnote †: journal: Medical Imaging  
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methods, and machine-learning (ML) methods. PB methods usually use prior knowledge of guided wave propagation to accurately extract useful information about damage from mixed wave signals [16; 17; 18]. However, it is difficult to quantitatively analyze the physical behavior of guided waves in complex material structures and environments. Therefore, PB methods can only be used for damage detection when the structure is well characterized and the environment is well controlled [19]. ML methods do not require prior knowledge of physical parameters and the mode of the Lamb waves [20; 21]. In ML methods, by analyzing the guided wave signals before and after passing through a damage region, the damage index can be calculated and then the damage can be further detected according to the dense sensor path [22]. However, as the traditional ML method usually has a limited learning capability, it would lead to unstable results for damage detection. With breakthroughs in the field of deep-learning techniques, it has emerged as a solution with great potential for lots of intelligent applications [23]. Deep-learning networks can automatically extract high-level features from signals and capture differences in waveforms without the requirement of any prior knowledge. Hence, deep-learning networks have been commonly used in combination with guided waves for intelligent damage detection, including artificial neural networks [22; 24], deep neural networks [25], recurrent neural networks [26], autoencoder networks [27] and convolutional neural networks (CNN). The aforementioned networks have achieved a great deal in the field of SHM. Among them, CNN is the most widely used due to its advantages. For instance, [PERSON] et al. [28] used CNN to automatically extract the DI in the guided wave signal and output the damage-probability images to achieve damage detection of aluminum and composite plates. [PERSON] et al. [29] used delay and sum (DAS) to transform the guided wave signal into an image, and then CNN was adopted to predict the region of aluminum hole damage. [PERSON] et al. [30] used CNN to establish a mapping relationship between guided wave signals and aluminum corrosion imaging that uses dispersion curves to describe the extent of the damage. Although this method has the advantage of fast imaging, it requires the usage of many sensors. [PERSON] et al. [31] proposed an efficient Lamb wave-based damage-detection method for composite structures by integrating convolutional neural networks with continuous wavelet transform, and it has outstanding performance.