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.

车轴温度预测技术对于监视火车转向架的状态并防止热车轴和其他危险事故非常重要。为了实现对车轴温度的高精度预测，提出了一种基于分解预处理方法，参数优化方法和BP神经网络的混合车轴温度时间序列预测模型。建模过程包括三个阶段。在第一阶段，经验小波变换（EWT）方法通过将原始车轴温度序列分解为几个子序列来对其进行预处理。在阶段II中，Q学习算法用于优化BP神经网络的初始权重和阈值。在第三阶段 Q-BPNN网络用于建立预测模型并完成对所有子序列的预测。通过组合所有子系列的预测结果来生成最终的预测结果。通过对三个案例预测的所有结果进行比较，可以得出以下结论：（a）提出的基于Q学习的参数优化方法可有效提高BP神经网络的精度，并且比传统的基于种群的优化方法效果更好；（b）所提出的混合轴温度预测模型可以在所有情况下获得准确的预测结果，并在八个通用模型中提供最佳的准确性。（a）所提出的基于Q学习的参数优化方法可有效提高BP神经网络的精度，并且比传统的基于种群的优化方法效果更好; （b）所提出的混合轴温度预测模型可以在所有情况下获得准确的预测结果，并在八个通用模型中提供最佳的准确性。（a）所提出的基于Q学习的参数优化方法可有效提高BP神经网络的精度，并且比传统的基于种群的优化方法效果更好; （b）所提出的混合轴温度预测模型可以在所有情况下获得准确的预测结果，并在八个通用模型中提供最佳的准确性。