## 4 Case Study
### Fault Prediction and Analysis
The performance of the model in removing the CNN layer, the performance when not using the SSA algorithm to optimize the parameters, and the performance when simultaneously removing the CNN layer and the SSA optimization algorithm were tested. The experimental results are shown in Tables 9-11. It can be seen that the prediction accuracy of the model with the CNN layer was greater than that of the model without the CNN layer, so the CNN layer had an important influence on the prediction accuracy of the model. In addition, the model using the SSA for intelligent optimization had higher prediction accuracy in each dataset than the traditional model that was based on experience for determining the parameters. The SSA-CNN-LSTM model with both the CNN layer and SSA for optimization achieved the best prediction accuracy in each dataset. Therefore, the results of the ablation experiments showed that both the CNN layer and the SSA for optimization in the SSA-CNN-LSTM model played an important role in the prediction accuracy of the model. Figures 12-16 show the prediction results of different methods in the ablation experiment on the fault rate of platform screen doors for each dataset, which were consistent with the conclusion of performance analysis. The poor performance of LSTM may have been due to the unclear distribution characteristics of the monthly fault rate data for certain lines, such as Line 5 and Line 10. The lack of a CNN layer to assist in feature extraction may have weakened the learning ability of LSTM for these lines' monthly fault rate data, resulting in a decrease in prediction accuracy. Figure 12: (**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 CNN-LSTM; (**c**) prediction results for the PSD fault rate in the dataset containing all lines when using SSA-LSTM; (**d**) prediction results for the PSD fault rate in the dataset containing all lines when using LSTM. Figure 13: _Cont._  
Figure 14: (**a**) Prediction results for the PSD fault rate in the Line 5 dataset when using SSA-CNN-LSTM; (**b**) prediction results for the PSD fault rate in the Line 5 dataset when using CNN-LSTM; (**c**) prediction results for the PSD fault rate in the Line 5 dataset when using SSA-LSTM; (**d**) prediction results for the PSD fault rate in the Line 5 dataset when using LSTM. Figure 13: (**a**) Prediction results for the PSD fault rate in the Line 1 dataset when using SSA-CNN-LSTM; (**b**) prediction results for the PSD fault rate in the Line 1 dataset when using CNN-LSTM; (**c**) prediction results for the PSD fault rate in the Line 1 dataset when using SSA-LSTM; (**d**) prediction results for the PSD fault rate in the Line 1 dataset when using LSTM. Figure 16: _Cont._  
Figure 15: **(a)** Prediction results for the PSD fault rate in the Line 9 dataset when using SSA-CNN-LSTM; **(b)** prediction results for the PSD fault rate in the Line 9 dataset when using CNN-LSTM; **(c)** prediction results for the PSD fault rate in the Line 9 dataset when using SSA-LSTM; **(d)** prediction results for the PSD fault rate in the Line 9 dataset when using LSTM.