Work

Collaborative Fusion Attention Mechanism for Vehicle Fault Prediction

Year: 2025

Type: article

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.

Source: Future Internet

DOI: 10.3390/fi17090428

Authors: Hong Jia, Dalin Qian, Fanghua Chen, Wei Zhou

Institutions: Beijing Jiaotong University, Ministry of Transport, Research Institute of Highway

Language: English

Topic: Machine Fault Diagnosis Techniques

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $1,930