A Spatiotemporal Locomotive Axle Temperature Prediction Approach Based on Ensemble Graph Convolutional Recurrent Unit Networks
Year: 2024
Type: article
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.
Authors: Ye Li, Limin Yang, Yutong Wan, Yu Bai
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,137
Award: 基于多方会话的多时钟同步规范合成与验证机制的研究
OpenAlex ID: W4401908450