## 5 Conclusions  
This study proposed a novel method to generate the fault machine vibration signal data, thus enhancing the model performance in the case of a limited fault dataset for training. In this study, our generated fault data improve the accuracy of the model to 93.1% with 0% original fault machine data set (Full signal analysis) and 99.41% with 20% original fault machine data set (statistical analysis). After testing, we conclude that the generated data has high similarity to the original data and significantly improves the accuracy of the model with limited real fault machine data in the training dataset.  
However, the data augmentation method using GAN still has a limitation, since the high variety can reduce the output signal. Another drawback of GAN is unstable during the training process, in which the balance between the discriminator and the generator needs to be maintained. Therefore, the architectures of both the generator and discriminator should be considered carefully, and the output of GAN has to be carefully evaluated.  
With the disadvantages of GAN, we consider to provide other generative AI models for the data augmentation and compare with the current scheme. Another option is to provide a model to generate the fault data for different machines, only using the fault data of one machine and normal data of other machines.  
Conceptualization, V.B. and T.L.P.; methodology, V.B.; software, V.B. and H.N.; validation, V.B., T.L.P., and H.N.; formal analysis, V.B.; resources, V.B.; writing--original draft preparation, V.B.; writing--review and editing, T.L.P. and Y.M.J.; supervision, Y.M.J.; project administration, V.B.; funding acquisition, Y.M.J. All authors have read and agreed to the published version of the manuscript.  
This research was financially supported by the Ministry of Trade, Industry and Energy (MOTIE) and Korea Institute for Advancement of Technology (KIAT) through the International Cooperative R&D program (Project ID: P0011880).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**100\% Original**} & \multicolumn{1}{c}{**80\% Original**} & \multicolumn{1}{c}{**60\% Original**} & \multicolumn{1}{c}{**40\% Original**} & \multicolumn{1}{c}{**20\% Original**} & \multicolumn{1}{c}{**0\% Original**} \\  & \multicolumn{1}{c}{**Data**} & \multicolumn{1}{c}{**Data**} & \multicolumn{1}{c}{**Data**} & \multicolumn{1}{c}{**Data**} & \multicolumn{1}{c}{**Data**} & \multicolumn{1}{c}{**Data**} & \multicolumn{1}{c}{**Data**} \\ \hline \multirow{3}{*}{Full FFT of the signal} & ANN & 100\% & 100\% & 100\% & 100\% & 95.1\% & 89.4\% \\ \cline{2-8}  & K-mean & \multirow{2}{*}{100\%} & \multirow{2}{*}{95.7\%} & \multirow{2}{*}{81.3\%} & \multirow{2}{*}{78.1\%} & \multirow{2}{*}{61.3\%} & \multirow{2}{*}{60.2\%} \\  & clustering & & & & & & \\ \cline{2-8}  & SVM & 100\% & 100\% & 98.7\% & 96.4\% & 95.3\% & 93.1\% \\ \hline \multirow{3}{*}{PCA transform of the signal} & ANN & 100\% & 100\% & 99.2\% & 98.1\% & 93.1\% & 87.2\% \\ \cline{2-8}  & K-mean & \multirow{2}{*}{100\%} & \multirow{2}{*}{96.2\%} & \multirow{2}{*}{85.4\%} & \multirow{2}{*}{76.6\%} & \multirow{2}{*}{65.2\%} & \multirow{2}{*}{55.3\%} \\  & clustering & & & & & & \\ \cline{2-8}  & SVM & 100\% & 100\% & 97.2\% & 95.4\% & 94.7\% & 91.0\% \\ \hline \multirow{3}{*}{SA of the signal} & ANN & 100\% & 99.41\% & 100\% & 99.41\% & 99.41\% & 79.41\% \\ \cline{2-8}  & K-mean & \multirow{2}{*}{62.94\%} & \multirow{2}{*}{67.37\%} & \multirow{2}{*}{64.21\%} & \multirow{2}{*}{71.05\%} & \multirow{2}{*}{68.42\%} & \multirow{2}{*}{57.32\%} \\  & clustering & & & & & & \\ \cline{2-8}  & SVM & 91.17\% & 76.84\% & 80.52\% & 73.68\% & 81.05\% & 84.74\% \\ \hline \hline \end{tabular}
\end{table}
Table 8: Performance of ANN, K-mean clustering, and SVM with generated training data using different approaches.