# Cumulative Failure Rate Prediction of EDCU in Subway Vehicles Based on RF-CNN-LSTM Model
###### Abstract  
Based on the current research status of fault prediction in rail transit reliability, this paper proposes a cumulative failure rate prediction method for key components of subway vehicles based on the RF-CNN-LSTM model. The article describes the prediction method based on cumulative failure rate data and takes the subway EDCU as an example of cumulative failure rate prediction. Three models, ARIMA, MLP, and LSTM, are introduced and compared with the RF-CNN-LSTM model by R\({}^{2}\) and adjusted R\({}^{2}\) index. The results show that the RF-CNN-LSTM model can predict the failure rate of the underground door controller well, with accuracy rates of 99.78% and 97.88%. Based on the prediction results, the cumulative failure rate of the EDCU peaks in about 10 years at 4.5% and 10.6%, respectively; the maintenance strategies can be adjusted through the actual situation of the EDCU to reduce maintenance costs and optimize maintenance plans. c 2025157188  
experimental examples; [PERSON] et al. [4] proposed an improved BP neural network algorithm for predicting faults in rail circuits with a dragonfly algorithm to optimize and improve the weights and thresholds of the initial BP neural network, used track voltage data collected by the electric workshop to train the improved BP neural network, and predicted and analyzed the rail out 1 and rail out 2 voltage values of the track circuit to obtain the probability of the occurrence of the red band and the trend of the track circuit; [PERSON] et al. [5] proposed a serial (STDM) and artificial neural network (ANN) combination model for the daily failure rate of the water distribution network, which can improve prediction accuracy; [PERSON] et al. [6] proposed a back-propagation neural network based on the optimization of genetic algorithms to simulate and predict pipeline failures, so as to improve the accuracy of pipeline failure prediction; [PERSON] et al. [7] applied the theory of task-driven behaviors and the theory of the system to reveal the general framework of MRTS applied the task-driven behavioral theory and system theory to reveal a general framework for escalator accident mechanisms and used Lasso-Logistic Regression (LLR) for escalator damage prediction; [PERSON] [8] proposed a two-stage train delay prediction method based on data smoothing and multi-model fusion, which is effective in predicting train delays; [PERSON] et al. [9] built a model combining analytical ripple and GM(2,1) for online aging monitoring and life prediction of electrolytic capacitors for power supply in urban rail transit systems; [PERSON] et al. [10] built a Weibull distribution combined with ratio-ratio to construct a prediction model with Bayesian assurance of data accuracy to predict the number of meter faults; [PERSON] et al. [11] came up with a novel model based on the Bayesian neural network (BNN) for predicting the number of distribution faults for predicting weather-related failures in distribution systems caused by wind, rain and lightning, through which confidence intervals for the prediction results can be obtained, providing sufficient information to guide the risk management of utility companies; [PERSON] et al. [12; 13] proposed a Bayesian and hyperellipsoid-based brake system reliability analysis, by which the accuracy of brake system reliability analysis can be improved; [PERSON] et al. [14] proposed an LSTM model based on the PdM background for predicting equipment failures, estimating the probability of failure and the RUL, and compared it with the random forest model to obtain a method that performs relatively well in fault classification and outperforms RF in RUL prediction. There are still some problems with the above fault prediction methods; for example, the problems with machine learning methods are mainly the high cost of prediction and the large amount of data needed for support. The BP neural network and Bayesian method has the problem of high requirements for initial data and parameter selection and is prone to the problem of gradient explosion.