| Autor: |
Fanghua Chen, Deguang Shang, Gang Zhou, Ke Ye, Fujie Ren |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
|
| Zdroj: |
Applied Sciences, Vol 14, Iss 16, p 7341 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2076-3417 |
| DOI: |
10.3390/app14167341 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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