Cooperative Static and Dynamic Correlation-Aware Learning for Vehicle Maintenance Demand Prediction
Accurate prediction of vehicle maintenance demands is crucial for enhancing service longevity and minimizing costs. However, current methods are limited to predicting maintenance demands for individual vehicle components. They fail to offer a comprehensive prediction that encompasses diverse mainten...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10818662/ |
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| _version_ | 1841550812892889088 |
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| author | Fanghua Chen Deguang Shang Gang Zhou Muhao Xu Ke Ye Fujie Ren Guofang Wu |
| author_facet | Fanghua Chen Deguang Shang Gang Zhou Muhao Xu Ke Ye Fujie Ren Guofang Wu |
| author_sort | Fanghua Chen |
| collection | DOAJ |
| description | Accurate prediction of vehicle maintenance demands is crucial for enhancing service longevity and minimizing costs. However, current methods are limited to predicting maintenance demands for individual vehicle components. They fail to offer a comprehensive prediction that encompasses diverse maintenance demands. Additionally, vehicle maintenance demand prediction must consider the interrelationships among various maintenance projects and maintenance project records. To address these issues, we propose a vehicle maintenance demand prediction method that employs a collaborative approach. This method utilizes both static and dynamic correlation-aware learning techniques. We design a static correlation-aware method for maintenance project representation learning by leveraging prior statistical data from various maintenance projects. To effectively capture the temporal correlations inherent in different maintenance project records, we propose an attention-based dynamic correlation-aware technique. Experiments conducted on real-world datasets demonstrate that the proposed model outperforms existing methods. |
| format | Article |
| id | doaj-art-fab3d159c2f24a3088904945d6043f33 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-fab3d159c2f24a3088904945d6043f332025-01-10T00:02:51ZengIEEEIEEE Access2169-35362025-01-01132970298110.1109/ACCESS.2024.352443310818662Cooperative Static and Dynamic Correlation-Aware Learning for Vehicle Maintenance Demand PredictionFanghua Chen0https://orcid.org/0009-0006-6441-1986Deguang Shang1https://orcid.org/0000-0002-2217-6774Gang Zhou2https://orcid.org/0009-0007-9152-6559Muhao Xu3https://orcid.org/0000-0001-8773-2789Ke Ye4https://orcid.org/0009-0004-5758-9669Fujie Ren5https://orcid.org/0009-0004-3966-2439Guofang Wu6https://orcid.org/0000-0002-5815-8594College of Mechanical and Energy Engineering, Beijing University of Technology, Beijing, ChinaCollege of Mechanical and Energy Engineering, Beijing University of Technology, Beijing, ChinaAutomobile Transportation Research Center, Research Institute of Highway Ministry of Transport, Beijing, ChinaInstitute of Information Science, Beijing Jiaotong University, Beijing, ChinaCollege of Mechanical and Energy Engineering, Beijing University of Technology, Beijing, ChinaAutomobile Transportation Research Center, Research Institute of Highway Ministry of Transport, Beijing, ChinaCollege of Mechanical and Energy Engineering, Beijing University of Technology, Beijing, ChinaAccurate prediction of vehicle maintenance demands is crucial for enhancing service longevity and minimizing costs. However, current methods are limited to predicting maintenance demands for individual vehicle components. They fail to offer a comprehensive prediction that encompasses diverse maintenance demands. Additionally, vehicle maintenance demand prediction must consider the interrelationships among various maintenance projects and maintenance project records. To address these issues, we propose a vehicle maintenance demand prediction method that employs a collaborative approach. This method utilizes both static and dynamic correlation-aware learning techniques. We design a static correlation-aware method for maintenance project representation learning by leveraging prior statistical data from various maintenance projects. To effectively capture the temporal correlations inherent in different maintenance project records, we propose an attention-based dynamic correlation-aware technique. Experiments conducted on real-world datasets demonstrate that the proposed model outperforms existing methods.https://ieeexplore.ieee.org/document/10818662/Vehicle maintenancedemand predictioncorrelation-aware learningco-occurrence matrixattention mechanism |
| spellingShingle | Fanghua Chen Deguang Shang Gang Zhou Muhao Xu Ke Ye Fujie Ren Guofang Wu Cooperative Static and Dynamic Correlation-Aware Learning for Vehicle Maintenance Demand Prediction IEEE Access Vehicle maintenance demand prediction correlation-aware learning co-occurrence matrix attention mechanism |
| title | Cooperative Static and Dynamic Correlation-Aware Learning for Vehicle Maintenance Demand Prediction |
| title_full | Cooperative Static and Dynamic Correlation-Aware Learning for Vehicle Maintenance Demand Prediction |
| title_fullStr | Cooperative Static and Dynamic Correlation-Aware Learning for Vehicle Maintenance Demand Prediction |
| title_full_unstemmed | Cooperative Static and Dynamic Correlation-Aware Learning for Vehicle Maintenance Demand Prediction |
| title_short | Cooperative Static and Dynamic Correlation-Aware Learning for Vehicle Maintenance Demand Prediction |
| title_sort | cooperative static and dynamic correlation aware learning for vehicle maintenance demand prediction |
| topic | Vehicle maintenance demand prediction correlation-aware learning co-occurrence matrix attention mechanism |
| url | https://ieeexplore.ieee.org/document/10818662/ |
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