## 1 Introduction
Our current research mainly focuses on the corresponding signal acquisition and subsequent identification processing for several types of common CFRP damage, such as holes, notches, fatigue cracks, etc. This study is based on the health monitoring principle of Lamb wave. A Lamb wave in composite laminate is an elastic wave generated and propagated between the upper and lower surface boundaries of the plate by particle movement, as shown in Figure 2. When the wave packet encounters a defect, reflection and scattering occur. Then, by analyzing the difference in waveform signals between healthy samples and damaged samples, the defects in the medium can be easily detected. Figure 1: Hydrogen storage composite wound cylinder. Compared with broadband excitation signal, narrowband excitation signal is more suitable for structural health monitoring technology [7]. The active Lamb wave is excited by the driving element to the monitored structure. Lamb wave has been widely used in the detection of materials and plates due to its flexible excitation and detection methods, its interaction with plate defects and carrying a large amount of information required for detection. Additionally, it has become an important and effective means and method for evaluating the performance of plate-like structures. In 2011, [PERSON] et al. [8] effectively improved the time resolution and signal-to-noise ratio of Lamb wave S0 mode detection by using signal processing methods such as Wiener filtering. In 2021, [PERSON] from East China University of Science and Technology proposed a crack localization method based on group velocity matching, which can well locate breathing cracks on the guided wave propagation path [9]. Recently, intelligent manufacturing has been thriving, and various types of production data can be collected faster and more extensively than ever before. This provides new opportunities for data-driven fault diagnosis methods to fully utilize massive data of various types [10], which has attracted increasing attention from researchers and engineers. In the rapidly developing world of technology, artificial neural networks are one of the most mature data-driven fault diagnosis methods. With the rapid development of machine learning, deep learning (DL) has become an effective method for structural health monitoring. DL can automatically learn abstract representations of raw data [11], which can better extract features and improve analysis effectiveness. In certain scenarios, data types for fault diagnosis can be presented in two-dimensional formats (such as time-frequency spectra), and then combined with image processing methods for classification. Through the processing of these images, analysis of various time-series signals can be facilitated, providing better ideas and directions for structural health monitoring today [12]. [PERSON] et al. [13] applied one-dimensional CNN to real-time motor fault diagnosis in their research. [PERSON] et al. [14] conducted experiments on real-time damage detection using one-dimensional CNN. [PERSON] [15] proposed a method for extracting induction motor fault features, in which he converted one-dimensional vibration signals into two-dimensional grayscale images. [PERSON] et al. [16] applied a CNN model to wave-based crack damage detection, training a deep CNN model based on Lamb waves data to avoid the calculation and extraction of damage features. [PERSON] et al. [17] used continuous wavelet transform to convert Lamb wave time series signals into two-dimensional images, and then used a CNN model to classify the images, achieving accurate localization of layered damage in composite structures. Through these methods, researchers can simplify the process of waveguide signal processing, and to a certain extent, simplify the analysis of waveguide's multimode and dispersion problems, while also improving the ability to extract signal features. At the same time, these methods can be combined with image processing methods for signal classification, improving the classification effect of temporal signals.