# Cumulative Failure Rate Prediction of EDCU in Subway Vehicles Based on RF-CNN-LSTM Model
## 4 Failure Rate Prediction  
The RF-CNN-LSTM model is applied to predict the failure rate of the gate controller for the next five years with the parameters set in Table 6.  
Taking 12 months of data as the initial sequence in the already scaled failure rate series, we set up 60 cycles. For each cycle, a point in time prediction is made, and the prediction results are added to the dataset to form a new series; the model is used to make predictions on the new dataset to obtain new results, and the above steps are repeated to generate predictions of the cumulative failure rate for the next five years. The door controller failure rate is predicted for vehicle type A and vehicle type B. The basic information of vehicle type B is shown in Table 7, and the prediction results are shown in Figures 11 and 12. In Figures 11 and 12, the solid blue line represents the actual data, and the dashed orange line represents the projected value of the data five years into the future. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Numerical Value** \\ \hline Initial sequence & 12 months \\ Number of cycles & 60 times \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Parameterization of future projections. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Training Set** & **ARIMA** & **MLP** & **LSTM** & **RF–CNN–LSTM** \\ \hline R\({}^{2}\) & 0.5465 & 0.5965 & 0.9740 & 0.9978 \\ Adjusted R\({}^{2}\) & 0.5420 & 0.5879 & 0.9710 & 0.9965 \\ Test set & ARIMA & MLP & LSTM & RF–CNN–LSTM \\ R\({}^{2}\) & 0.0390 & 0.9311 & 0.8997 & 0.9788 \\ Adjusted R\({}^{2}\) & 0.0161 & 0.9291 & 0.8244 & 0.9610 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Calculation of training set and test set accuracy. The following information can be obtained from Figures 11 and 12:  
1. According to Figure 11, during the period 2011-2023, the door controller failure rate of vehicle type A reaches a maximum value of 4.2% in 2016-2017; the EDCU controller failure rate for the period 2024-2029 is the predicted data output from the RF-CNN-LSTM model, and it can be seen that the cumulative failure rate of the train's EDCU reaches a maximum value of 4.5% in 2027. From Table 1, it can be seen that the vehicle type A was put into service in 2009 and underwent a major overhaul during the period 2017-2020, after which its cumulative failure rate decreased. For vehicle type A, the metro company conducts a frame overhaul of the vehicle every five or ten years, which is consistent with the change in cumulative failure rate exhibited in Figure 11, indicating that the frame overhaul schedule for vehicle type A is consistent with the pattern of the EDCU controller failure rate. 2. According to Figure 12, the EDCU controller failure rate of vehicle type B reached a maximum value of 10.6% in 2018. According to Table 7, vehicle type B was overhauled in 2016-2019, and the failure rate decreased after the overhaul, which is more consistent with the results in Figure 12; according to the prediction curve of the EDCU failure rate in Figure 12, it can be seen that the failure rates of the EDCU is higher  
Figure 11: Prediction of the RF-CNN-LSTM model for the failure rate of the EDCU of vehicle type A.  
Figure 12: Prediction of the RF–CNN–LSTM model for the failure rate of the EDCU of vehicle type B.  
after 2024, which are all around 10%, and the failure rate reaches the maximum value of 10.6% in 2028. Vehicles undergo a frame overhaul every 5 or 10 years, and the cumulative failure rate decreases after the overhaul, which is consistent with the pattern of change in the EDCU failure rate in Figure 12, but due to the service life of the EDCU in about 15 years, the failure rate of the vehicle type B EDCU is at a high level in its late life, and the frame overhaul may not be able to satisfy the high reliability needed for operation. Thus, a batch updating of the maintenance strategy can be considered. 3.