## 4 Case Study
### Data Augmentation for Shanghai Rail Transit Lines  
According to the data enhancement method proposed in Section 3.3, first, the parameters of TimeGAN were debugged, a GRU was used as the generator, the hidden layers were set to 24, there were 3 layers, the batch size was set to 20, and the number of iterations was set to 10,000, with 24 units of data making up a group; then, the preprocessed data were input into the TimeGAN model to generate 20 groups of 480 units of data, including the monthly fault rate and monthly MTBF. This data augmentation operation was applied to the monthly fault data of all of the lines and the monthly fault data of each of the four lines, resulting in five datasets. The generated data were evaluated using t-SNE analysis, the cumulative probability distribution, and the discrimination score. The results of the t-SNE analysis are shown in Figure 8. It can be seen that the generated data in each dataset basically overlapped with the original data distribution area and had similar distribution characteristics, indicating that TimeGAN effectively learned and generated data that confirmed to the original data distribution pattern. The cumulative probability distribution results are shown in Figure 9. It can be seen that the cumulative probability distribution curve of the generated data was able to basically fit the probability distribution curve of the original data, indicating that the generated data were able to better restore the probability distribution characteristics of the original data. The discrimination scores of the generated data are shown in Table 5, and the scores were all below 0.2, indicating that the classification error rate was low in each dataset, which further proved that the similarity between the generated data and the original data was close.  
\begin{table}
\begin{tabular}{c c c} \hline
**Year and Month** & **Monthly Fault Rate** & **Monthly MTBF** \\ \hline January 2020 & 0.016203 & 1913.135 \\ February 2020 & 0.005787 & 5011.192 \\ March 2020 & 0.010912 & 2840.721 \\... &... &... \\ August 2023 & 0.011739 & 2640.666 \\ \hline \end{tabular}
\end{table}
Table 4: Sample data from the preprocessed dataset (data set of all lines).  
\begin{table}
\begin{tabular}{c c} \hline
**Features** & **Value** \\ \hline Maintenance Content & Replaced the drive power board, fixed \\ Response Time (min) & 0.027 \\ Handling Time (min) & 2.45 \\ Processing Time (min) & 0.473 \\ Delay Time (min) & 21.869 \\ Fault Cause & Drive power board \\ Fault Mode & Door machine system fault \\ Door Function Status & Cannot be operated \\ \hline \end{tabular}
\end{table}
Table 3: _Cont._Figure 8: **(a)** The results of the t-SNE analysis of the dataset containing all lines; **(b)** the results of the t-SNE analysis of the Line 1 dataset; **(c)** the results of the t-SNE analysis of the Line 5 dataset; **(d)** the results of the t-SNE analysis of the Line 9 dataset; **(e)** the results of the t-SNE analysis of the Line 10 dataset.  
Figure 9: (**a**) Results of the cumulative probability analysis of the dataset containing all lines; (**b**) results of the cumulative probability analysis of the Line 1 dataset; (**c**) results of the cumulative probability analysis of the Line 5 dataset; (**d**) results of the cumulative probability analysis of the Line 9 dataset; (**e**) results of the cumulative probability analysis of the Line 10 dataset.