Collaborative Fusion Attention Mechanism for Vehicle Fault Prediction

Hong Jia, Dalin Qian, Fanghua Chen () and Wei Zhou
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Hong Jia: School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China
Dalin Qian: School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China
Fanghua Chen: Automobile Transportation Research Center, Research Institute of Highway Ministry of Transport, Beijing 100088, China
Wei Zhou: Automobile Transportation Research Center, Research Institute of Highway Ministry of Transport, Beijing 100088, China

Future Internet, 2025, vol. 17, issue 9, 1-13

Abstract: In this study, we investigate a deep learning-based vehicle fault prediction model aimed at achieving accurate prediction of vehicle faults by analyzing the correlations among different faults and the impact of critical faults on future fault development. To this end, we propose a collaborative modeling approach utilizing multiple attention mechanisms. This approach incorporates a graph attention mechanism for the fusion representation of fault correlation information and employs a novel learning method that combines a Long Short-Term Memory (LSTM) network with an attention mechanism to capture the impact of key faults. Based on experimental validation using real-world vehicle fault record data, the model significantly outperforms existing prediction models in terms of fault prediction accuracy.

Keywords: vehicle maintenance; fault prediction; graph attention; mechanism; attention mechanism (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2025
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