A Spatiotemporal Locomotive Axle Temperature Prediction Approach Based on Ensemble Graph Convolutional Recurrent Unit Networks

Abstract

Spatiotemporal axle temperature forecasting is crucial for real-time failure detection in locomotive control systems, significantly enhancing reliability and facilitating early maintenance. Motivated by the need for more accurate and reliable prediction models, this paper proposes a novel ensemble graph convolutional recurrent unit network. This innovative approach aims to develop a highly reliable and accurate spatiotemporal axle temperature forecasting model, thereby increasing locomotive safety and operational efficiency. The modeling structure involves three key steps: (1) the GCN module extracts and aggregates spatiotemporal temperature data and deep feature information from the raw data of different axles; (2) these features are fed into GRU and BiLSTM networks for modeling and forecasting; (3) the ICA algorithm optimizes the fusion weight coefficients to combine the forecasting results from GRU and BiLSTM, achieving superior outcomes. Comparative experiments demonstrate that the proposed model achieves RMSE values of 0.2517 °C, 0.2011 °C, and 0.2079 °C across three temperature series, respectively, indicating superior prediction accuracy and reduced errors compared to benchmark models in all experimental scenarios. The Wilcoxon signed-rank test further confirms the statistical significance of the result improvements with high confidence.