## 4 Case Study
### Fault Prediction and Analysis  
Prediction Results and Discussion  
In accordance with the fault rate data prediction methodology and the augmented dataset delineated in Section 3.3, 80% of the augmented dataset was designated as the training set for the model, while the remaining 20% was allocated as the validation set [46]. The sliding window technique was employed to generate a fixed-step fault time series, which served as the input for the CNN-LSTM model. Subsequently, the parameters of the model were optimized using the shuffled shrinkage algorithm (SSA) to ascertain the optimal learning rate, the number of hidden neurons, the number of epochs, and the batch size for the LSTM model. The ultimate prediction results were derived through an iterative training process of the model, as illustrated in Table 6.  
* Comparison of different optimization algorithms  
To improve the efficiency and prediction accuracy of neural networks, the particle swarm optimization (PSO) algorithm is used to effectively search for problems with a large solution space and find candidate solutions by simulating the motion of a bird swarm. It is an optimization algorithm that is commonly used in CNN-LSTM models [47]. To evaluate the prediction performance of the SSA-optimized CNN-LSTM model, the root-mean-square error (RMSE), mean absolute error (MAE), and determination coefficient (R2) were used. In this study, experiments were carried out on the augmented datasets containing all lines, Line 1, Line 5, Line 9, and Line 10. The performance of the SSA-CNN-LSTM and PSO-CNN-LSTM models is compared while using the same time window sequence and prediction step size as input, and the structure of the CNN-LSTM model was consistent. Figure 10 shows the prediction results of the SSA-CNN-LSTM and PSO-CNN-LSTM models for different datasets. It can be seen that the fitting degree of the SSA-CNN-LSTM prediction results was higher. Table 7 shows a performance comparison between the two, where the CNN-LSTM prediction model based on SSA optimization proposed in this study achieved a lower RMSE and MAE than those of traditional particle swarm optimization (PSO) on all datasets, and it had higher R2 scores. The RMSE is the square root of the average square of the difference between the predicted and actual values; the MAE is the average of the absolute sum of all prediction errors. From this, it can be seen that the smaller the values of the RMSE and MAE, the better the predictive performance of the model. This shows that the parameters of the CNN-LSTM model optimized using SSA were more reasonable, and the network scale was better, thus improving the prediction accuracy for the fault rate of platform screen doors. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Parameters**} & \multicolumn{5}{c}{**Optimal Value**} \\ \cline{2-6}  & **All Lines** & **Line 1** & **Line 5** & **Line 9** & **Line 10** \\ \hline Learning rate & 0.009555 & 0.007819 & 0.001427 & 0.007261 & 0.006122 \\ Epoch & 93 & 171 & 151 & 250 & 357 \\ Hidden & 32 & 27 & 44 & 39 & 38 \\ neurons & 36 & 30 & 38 & 22 & 24 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Parameters of LSTM optimized using the SSA. \begin{table}
\begin{tabular}{c c} \hline \hline
**Data Sets** & **Discrimination Scores** \\ \hline All lines & 0.155 \\ Line 1 & 0.168 \\ Line 5 & 0.162 \\ Line 9 & 0.169 \\ Line 10 & 0.181 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Discrimination scores of different datasets.