A hybrid ensemble deep reinforcement learning model for locomotive axle temperature using the deterministic and probabilistic strategy

Abstract

This paper proposes a hybrid deep reinforcement learning framework for locomotive axle temperature by combining the wavelet packet decomposition (WPD), long short-term memory (LSTM), the gated recurrent unit (GRU) reinforcement learning, and generalized autoregressive conditional heteroskedasticity (GARCH) algorithms. The WPD is utilized to decompose the raw nonlinear series into subseries. Then the deep learning predictors LSTM and GRU are established to predict the future axle temperatures in each subseries. The Q-learning could generate optimal ensemble weights to integrate the predictors to finish the deterministic forecasting and GARCH is used to conduct the deterministic forecasting based on the deterministic forecasting residual. These parts of the hybrid ensemble structure contributed to optimal modeling accuracy and provided effective support in the real-time monitoring and fault diagnosis of transportation.