Mileage-Aware for Vehicle Maintenance Demand Prediction

• Published in: Applied sciences Vol. 14; no. 16; p. 7341
• Main Authors: Chen, Fanghua, Shang, Deguang, Zhou, Gang, Ye, Ke, Ren, Fujie
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-08-2024
• Subjects: Artificial intelligence; attention mechanism; Deep learning; demand prediction; Electric vehicles; Energy consumption; Fault diagnosis; Kalman filters; long and short-term memory networks; Machine learning; maintenance mileage; maintenance project; Methods; Neural networks; Sensors; Useful life; vehicle maintenance; Germany
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app14167341

It is of paramount importance to accurately predict the maintenance demands of vehicles in order to guarantee their sustainable use. Nevertheless, the current methodologies merely predict a partial aspect of a vehicle’s maintenance demands, rather than the comprehensive maintenance demands. Moreover, the process of predicting vehicle maintenance demands must give due consideration to the influence of mileage on such demands. In light of the aforementioned considerations, we put forth a vehicle overall maintenance demand prediction method that incorporates vehicle mileage awareness. In order to address the discrepancy between the vector space of mileage and that of the project, we put forth a mileage representation method for the maintenance demand prediction task. To capture the significant impact of key mileage and projects on future demand, we propose a learning module for key temporal information using a fusion of Long Short-Term Memory (LSTM) networks and attention mechanism. Moreover, to integrate maintenance mileage and projects, we propose a fusion method based on a gated unit. The experimental results obtained from real datasets demonstrate that the proposed model exhibits a superior performance compared to existing methods.