## 4 Case Study
### Fault Prediction and Analysis
Figure 10: _Cont._  
Figure 10: **(a)** Prediction results for the PSD fault rate in the dataset containing all lines when using SSA-CNN-LSTM; **(b)** prediction results for the PSD fault rate in the dataset containing all lines when using PSO-CNN-LSTM; **(c)** prediction results for the PSD fault rate in the Line 1 dataset when using SSA-CNN-LSTM; **(d)** prediction results for the PSD fault rate in the Line 1 dataset when using PSO-CNN-LSTM; **(e)** prediction results for the PSD fault rate in Line 5 dataset when using SSA-CNN-LSTM; **(f)** prediction results for the PSD fault rate in the Line 5 dataset when using PSO-CNN-LSTM; **(g)** prediction results for the PSD fault rate in the Line 9 dataset when using SSA-CNN-LSTM; **(h)** prediction results for the PSD fault rate in the Line 9 dataset when using PSO-CNN-LSTM; **(i)** prediction results for the PSD fault rate in the Line 10 dataset when using SSA-CNN-LSTM; **(j)** prediction results for the PSD fault rate in the Line 10 dataset when using SSA-CNN-LSTM; **(j)** prediction results for the PSD fault rate in the Line 10 dataset when using PSO-CNN-LSTM. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Criterion**} & \multirow{2}{*}{**Algorithm**} & \multicolumn{5}{c}{**Optimal Value**} \\ \cline{3-8}  & & **All Lines** & **Line 1** & **Line 5** & **Line 9** & **Line 10** \\ \hline \multirow{2}{*}{RMSE} & SSA & 0.001445 & 0.002493 & 0.002232 & 0.003375 & 0.002724 \\  & PSO & 0.002636 & 0.003204 & 0.002564 & 0.003669 & 0.00322 \\ \hline \multirow{2}{*}{MAE} & SSA & 0.000778 & 0.001771 & 0.00154 & 0.001917 & 0.002161 \\  & PSO & 0.001478 & 0.002487 & 0.001672 & 0.001991 & 0.00255 \\ \hline \multirow{2}{*}{R2} & SSA & 0.92696 & 0.914977 & 0.882115 & 0.743619 & 0.866218 \\  & PSO & 0.756944 & 0.859563 & 0.844404 & 0.692574 & 0.804918 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Performance comparison of the CNN-LSTM model when using different optimization algorithms. * Comparison of different prediction models  
To verify the performance of the SSA-CNN-LSTM model, this study compared the prediction performance of SSA-CNN-LSTM and a gated recurrent unit (GRU) on the augmented datasets with all lines and with the four individual lines. Table 8 shows a comparison of the performance between the two. It can be seen in the table that the SSA-CNN-LSTM prediction model proposed in this study achieved lower RMSE and MAE values than those of the GRU on all datasets, and it had higher R2 scores. This showed that the SSA-CNN-LSTM model proposed in this study had better performance than that of the GRU in the face of long-range time-series prediction problems. Figure 11 shows the prediction results of SSA-CNN-LSTM and the GRU for the different datasets. It can be seen that the fitting degree of the SSA-CNN-LSTM prediction results was higher. Figure 11: (**a**) Prediction results for the PSD fault rate in the dataset containing all lines when using SSA-CNN-LSTM; (**b**) prediction results for the PSD fault rate in the dataset containing all lines when using the GRU; (**c**) prediction results for the PSD fault rate in the Line 1 dataset when using SSA-CNN-LSTM; (**d**) prediction results for the PSD fault rate in the Line 1 dataset when using the GRU; (**e**) prediction results for the PSD fault rate in the Line 5 dataset when using SSA-CNN-LSTM; (**f**) prediction results for the PSD fault rate in the Line 5 dataset when using the GRU; (**g**) prediction results for the PSD fault rate in the Line 9 dataset when using SSA-CNN-LSTM; (**h**) prediction results for the PSD fault rate in the Line 9 dataset when using the GRU; (**i**) prediction results for the PSD fault rate in the Line 10 dataset when using SSA-CNN-LSTM; (**j**) prediction results for the PSD fault rate in the Line 10 dataset when using the GRU. * Ablation experiment In order to verify the effectiveness of each component in the SSA-CNN-LSTM model, ablation experiments were designed and conducted.