# Cumulative Failure Rate Prediction of EDCU in Subway Vehicles Based on RF-CNN-LSTM Model
## 5 Conclusions  
Based on the current situation of rail transportation failure rate prediction, this paper proposes an RF-CNN-LSTM prediction model and conducts a failure rate prediction study with the subway door system EDCU as an example. The article describes the RF-CNN-LSTM prediction model process and parameter settings in detail, and the failure rate prediction is carried out by taking the EDCU of a metro line in a city as an example and introduces the ARIMA model, the multilayer perceptron neural network model, and the LSTM model to compare with the new model proposed in this paper; and finally it evaluates all the models by the R\({}^{2}\) accuracy and the adjusted R\({}^{2}\) accuracy, with the results showing that the RF-CNN-LSTM model can predict the failure rate better than the other models, which is not the case. CNN-LSTM model can predict the failure of subway vehicle door controllers better; finally, the model is applied to predict the failure rate of door controllers in the next five years. From the forecast results, the following conclusions can be drawn:  
(1) In this paper, an RF-CNN-LSTM prediction model is used to conduct a failure rate prediction study of the subway vehicle EDCUs; the advantages of this method are to ensure the integrity of the data through Random Forests, to provide data support for prediction, and to combine the local feature extraction capability of CNN and the time-dependent capture capability of LSTM, which can understand the sequence data more comprehensively; at the same time, it can quickly extract the features and reduce the data dimension of LSTM processing, improve the overall model training efficiency, and be applied to sequence data with complex spatial-temporal patterns, such as a multivariate time series and sequences with periodicity and trend. The introduction of the R\({}^{2}\) accuracy and adjusted R\({}^{2}\) accuracy were evaluated to assess the prediction accuracy of the model, and it was finally shown that the RF-CNN-LSTM model can predict the cumulative failure rate of the subway vehicle EDCU in a better way. (2) By predicting the failure rate of vehicle type A and vehicle type B and analyzing the results, the failure rate of the EDCU of vehicle type A will reach the peak in about 10 years, which is 4.2% and 4.5%, respectively. The subway company carries out overhaul of the vehicle every five or ten years for vehicle type A, which shows that the overhaul plan of vehicle type A is in line with the failure rate of the door controller. The failure rate of vehicle type B reaches a peak of 10.6% about ten years after it is put into service, but the prediction curve shows that the failure rate of the EDCU is higher after 2024, all of which are around 10%, so the failure rate of vehicle type B's EDCU is at a high level at the late stage of its life, and it may not be able to satisfy the high reliability demanded for operation through a frame overhaul, and thus, the batch updating of the maintenance strategy can be considered. (3) In order to protect the safety of vehicle operation and service quality for key components, regardless of the actual state of the components, the maintenance mode of planned repair is often used. However, sometimes the failure rate of the key components is reduced, but the overall reliability does not affect the vehicle operation, so from an economic point of view, under the premise of reliability to meet the requirements, you can extend the planned repair cycle, or according to the actual state of the equipment, the implementation of the maintenance mode of state repair. (4) The failure rate of urban rail transit electrical component parts is consistent with the Weibull distribution [21]. The method in this paper can also be used to research the failure rate prediction of other key electrical control units of the vehicle, such as traction control units and other components, which is of great significance to provide data support for the preventive maintenance of key electrical components of the metro subway system.