###### Abstract
The ViT model can extract features from the local part of an image and classify the health state of the system. Transfer learning models that are pretrained on a large amount of image datasets are widely applied for fault diagnosis. Among the various models, Residual Network-50 (Resnet-50) is generally used [32]. Long Short-Term Memory (LSTM) and Bidirectional LSTM (BiLSTM) are other deep learning-based methods for fault diagnosis. They have a unique ability to extract features from time series data; they have remarkable performance in predicting Remaining Useful Life (RUL), as well as fault diagnosis. The models use extracted features from the raw time-series signal itself or preprocessed signals to perform fault diagnosis [33; 34; 35; 36]. Although DL-based fault diagnosis has been explored by many researchers, numerous limitations still remain. For general applications of fault diagnosis using DL models, further research is required to address challenges, such as noise mitigation, data imbalance, and various uncertainties. In particular, data imbalance, where certain classes in the dataset have fewer instances, can significantly reduce the performance of the DL model. The model may struggle to learn features of the minority class, resulting in lower accuracy for that class [37]. In addition, since there is a myriad of unpredictable damage cases with a diverse range of operating and environmental conditions, it is impractical to comprehensively detect and anticipate all potential damages in advance. To cope with these challenges, it is essential to generate synthetic data through experimentation, simulation, or data-driven approaches. However, the process of data acquisition through experiments or simulations can be both time-consuming and financially burdensome. Additionally, these methods require an in-depth understanding of the physics of the composite structure [38]. To address these challenges, a focus has been placed on developing data-driven approaches to generate synthetic data. These methods require less in-depth understanding of the physics involved and are less labor-intensive by leveraging advanced algorithms and DL techniques. Above all, there are some basic data augmentation techniques that use simple mathematical calculations. For example, images from impact response data can be manipulated by adding and multiplying by random numbers [39]. Similarly, adding Gaussian noise to the images obtained from acoustic emission and guided wave signals was also performed [40; 41]. The advantage of this type of data augmentation method is the low level of difficulty. They are easy to implement, and the corresponding algorithms are not complex. Other simple methods--such as shifting, scaling, rotating, and random grid shuffling of the images--are also performed [42]. These are also basic techniques for data augmentation for images. However, in most engineering problems, images contain some dynamic characteristics of the system, including time, amplitude, and frequency of the signal. Therefore, this kind of augmentation technique should be applied with caution regarding the engineering problem. Recently, data augmentation using the GAN model for image data has been widely used [43; 44; 45]. By using the GAN model, it is possible to duplicate the dynamic characteristics of the original data and thus generate synthetic image data properly. Although these approaches can generate sufficient data for robust fault diagnosis in composite structures, most of them rely on image-based data augmentation. Thus, this approach necessitates proper preprocessing of raw data for their transformation into 2D images, making the process tedious and time-consuming. Therefore, data augmentation of the raw time-series signal, not imagery, can benefit by eliminating the need for excessive preprocessing, making the PHM process for composite structures simple and computationally efficient.