## 5 Conclusions  
This study addresses the time-series data augmentation technique using the WGAN model for the solution of the data imbalance problem and fault diagnosis using the 1D CNN model. For time-series data augmentation, vibration signals were directly used as input data for the WGAN model. To verify the performance increase in fault diagnosis by applying the WGAN model, experiments using laminated composite beam specimens to obtain vibration signals were conducted. For imitation of the data imbalance dataset, 1000 signals for the healthy state and 60 signals for faulty states were obtained through the experiments, and a total of 9000 synthetic signals were generated by using the WGAN model. To verify the enhanced fault diagnosis performance, the results from a 1D CNN model using both experimental and synthetic datasets were compared. The results demonstrated a significant improvement in fault diagnosis. Furthermore, a comparative study revealed that data augmentation with the WGAN model yielded the best diagnostic performance. In contrast to previous works, this research has simplified the data augmentation and fault diagnosis process by directly using time-series data as input. The simplification of the process saves computational resources because both the WGAN and 1D CNN models use time-series data, which has a smaller data size than the image data. Thus, training time, as well as predicting time, are also much shorter, which means the enhanced probability of applications in real-time fault diagnosis. For the last contribution, fault diagnosis could be performed by inexpensive and simple experiments. The accelerometer is a cheap sensor among various sensors, which means that data can be obtained efficiently. Notwithstanding these contributions to the solution of the data imbalance problem and the fault diagnosis process, some limitations still exist. After the advent of the GAN model, much research related to GAN has been conducted, but a generalization of the GAN model for various engineering problems is still one of the critical issues [61]. Thus, the trial and error process is generally employed to optimize the parameters of GAN models; however, it is a time-consuming and laborious method. A reasonable technique or specific standard for optimizing the parameters of the GAN model is essential. For the next limitation, the 1D CNN model can extract features and diagnose the health state of the composite system, as mentioned above. However, because the 1D CNN model is a black box model, it is unclear which part of the signal was considered, especially for fault diagnosis. To improve this hardship in explainability, the eXplainable Artificial Intelligence (XAI) algorithm will be used in future work. Through the XAI algorithm, it is anticipated that the features that are considered more important in the fault diagnosis process will be identified. Also, this research focused on evaluating data augmentation performance. For future research, a generalization of the fault diagnosis model will be conducted. For this purpose, a large number of specimens with various conditions will be tested. Then, data augmentation will be implemented for the training dataset to prove that the fault diagnosis model can accurately classify the data that are not trained for the model. Conceptualization, S.K., M.M.A., J.S. and H.K. ; methodology, S.K., M.M.A. and J.S. ; software, S.K. ; validation, S.K., M.M.A. and J.S. ; formal analysis, S.K. and J.S. ; investigation, S.K. and M.M.A. ; resources, S.K. and J.S. ; data curation, S.K. and M.M.A. ; writing--original draft preparation, S.K. and M.M.A. ; writing--review and editing, J.S. and H.K. ; visualization, S.K. ; supervision, J.S. and H.K. ; project administration, H.K. ; funding acquisition, H.K. All authors have read and agreed to the published version of the manuscript. This research was funded by a National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (No.