## 5 Conclusions  
In this study, a prediction algorithm based on SSA-CNN-LSTM was proposed to predict the fault rate of platform screen door systems. Based on TimeGAN, augmented data that conformed to the temporal characteristics of the original dataset were generated, which solved the problem of sparse data in the original rail transit platform screen door fault dataset. The established SSA-CNN-LSTM fault rate prediction model extracted effective data features from low-dimensional data with insufficient feature depth through the convolutional layer, pooling layer, and other structures of a CNN, determined the optimal hyperparameters, automatically optimized the model network scale, solved the problem of the scale of the neural network model being difficult to determine, and realized the accurate prediction of the fault rate of rail transit platform screen door systems.  
The results of this study show the following:  
1. The fault rate prediction model for rail transit platform screen doors based on SSA-CNN-LSTM was able to better fit nonlinear time-series data, such as the fault rate data of platform screen doors. Compared with the GRU model, the model performed better in prediction performance.
2. The TimeGAN algorithm was used for data enhancement, the spatial and temporal distribution patterns of the data were effectively retained, and the data capacity was increased. TimeGAN learned the temporal characteristics of the fault rate data of platform screen door systems through the joint training of an automatic coding network and adversarial network, and it effectively solved the problem of data sparseness.
3. Compared with the PSO-optimized CNN-LSTM model, when the SSA-optimized CNN-LSTM model was used to predict the PSD fault rate of all lines after enhancement, the RMSE was reduced by 0.001191 (improved by 45.2%), the MAE was reduced by 0.0007 (improved by 47.3%), and the R2 index was increased by 0.170016 (improved by 22.5%). When predicting the PSD fault rate of each line after enhancement, the performance of the CNN-LSTM model optimized with the SSA algorithm was better than that of the CNN-LSTM model optimized with the PSO algorithm. For example, in the fault rate prediction for Line 1, the RMSE was reduced by 0.00071 (improved by 22.2%), the MAE was reduced by 0.00072 (improved by 28.9%), and in the fault rate prediction for Line 10, R2 was increased by 0.0613 (improved by 7.6%).  
Figure 16: (**a**) Prediction results for the PSD fault rate in the Line 10 dataset when using SSA-CNN-LSTM; (**b**) prediction results for the PSD fault rate in the Line 10 dataset when using CNN-LSTM; (**c**) prediction results for the PSD fault rate in the Line 10 dataset when using SSA-LSTM; (**d**) prediction results for the PSD fault rate in the Line 10 dataset when using LSTM.  
In the future, further optimization of the SSA will be considered so that the results of multiple intelligent optimization parameters are close to the same. In addition, maintenance decisions for platform screen door systems based on fault rate prediction data will be considered.  
Conceptualization, [PERSON] and [PERSON]; methodology, [PERSON] and J.X.; formal analysis, [PERSON] and J.X.; investigation, [PERSON] and Y.W.; resources, Y.W.; data curation, Y.W. and J.S.; verification, [PERSON] and J.X.; writing--original draft preparation, J.S., G.Y. and J.X.; writing--review and editing, [PERSON], G.Y. and J.S.; visualization, G.Y. and J.S.; supervision, J.X.; project administration, [PERSON] and J.X. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Ministry of Industry and Information Technology of China (No. 2022-A04R-1-1) and the Science and Technology Program of Shanghai, China (No. 22511104300).  
Not applicable.  
The data are confidential and were obtained from Shanghai Shentong Metro Group Co., Ltd.  
The authors declare no conflicts of interest.