A novel axle temperature forecasting method based on decomposition, reinforcement learning optimization and neural network

Abstract Axle temperature forecasting technology is important for monitoring the status of the train bogie and preventing the hot axle and other dangerous accidents. In order to achieve high-precision forecasting of axle temperature, a hybrid axle temperature time series forecasting model based on decomposition preprocessing method, parameter optimization method, and the Back Propagation (BP) neural network is proposed in this study. The modeling process consists of three phases. In stage I, the empirical wavelet transform (EWT) method is used to preprocess the original axle temperature series by decomposing them into several subseries. In stage II, the Q-learning algorithm is used to optimize the initial weights and thresholds of the BP neural network. In stage III, the Q-BPNN network is used to build the forecasting model and complete predicting all subseries. And the final forecasting results are generated by combining all prediction results of subseries. By comparing all results over three case predictions, it can be concluded that: (a) the proposed Q-learning based parameter optimization method is effective in improving the accuracy of the BP neural network and works better than the traditional population-based optimization methods; (b) the proposed hybrid axle temperature forecasting model can get accurate prediction results in all cases and provides the best accuracy among eight general models.