Hybrid learning strategies: integrating supervised and reinforcement techniques for railway wheel wear management with limited measurement data
Train wheel wear significantly impacts wheel-rail interaction forces and is an unavoidable issue in the railway industry. This study focuses on regular wear, specifically changes in wheel profiles such as tread wear, flange height, and flange thickness. Effective wheel wear management is crucial for...
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Built Environment |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fbuil.2025.1546957/full |
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| author | Jessada Sresakoolchai Chayut Ngamkhanong Sakdirat Kaewunruen |
| author_facet | Jessada Sresakoolchai Chayut Ngamkhanong Sakdirat Kaewunruen |
| author_sort | Jessada Sresakoolchai |
| collection | DOAJ |
| description | Train wheel wear significantly impacts wheel-rail interaction forces and is an unavoidable issue in the railway industry. This study focuses on regular wear, specifically changes in wheel profiles such as tread wear, flange height, and flange thickness. Effective wheel wear management is crucial for maintaining the reliability, safety, and efficiency of rail systems. However, regular measurement of wheel profiles is often limited by constraints such as dense traffic, budget, time, and remote assets, which reduces the effectiveness of traditional maintenance approaches. This study proposes a hybrid learning strategy combining supervised and reinforcement learning techniques to optimize train wheel wear management under these constraints and achieve predictive maintenance. The supervised learning model, developed from validated simulations, predicts wear progression, while reinforcement learning improves maintenance decision-making using basic operational data without regular measurements. Various machine-learning techniques are explored and fine-tuned to identify the best models for preventing faulty wheels without the need for frequent inspections. By integrating these two learning approaches, the framework enhances the accuracy of wear predictions and optimizes maintenance schedules, reducing the risk of over-maintenance or unexpected failures. This integrated model addresses challenges such as system complexity, limited data, and cost-effectiveness in the industry. In terms of supervised learning, the R2 for tread wear prediction improves from 0.94 to 0.95 compared to previous studies, and the model, when integrated with reinforcement learning, significantly reduces defects based on wear and irregular wheel dimensions. This research is the first to integrate supervised and reinforcement learning specifically for train wheel wear management under limited measurement data constraints, offering a breakthrough compared to traditional methods that rely on regular inspections. The study provides significant benefits for the railway industry, including reduced maintenance costs, improved maintenance efficiency, lower defect rates, reduced possession and inspection time, and enhanced passenger comfort and safety. |
| format | Article |
| id | doaj-art-59342ab87eec4a1fab7c4439ab035b6d |
| institution | Kabale University |
| issn | 2297-3362 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Built Environment |
| spelling | doaj-art-59342ab87eec4a1fab7c4439ab035b6d2025-01-27T06:40:44ZengFrontiers Media S.A.Frontiers in Built Environment2297-33622025-01-011110.3389/fbuil.2025.15469571546957Hybrid learning strategies: integrating supervised and reinforcement techniques for railway wheel wear management with limited measurement dataJessada Sresakoolchai0Chayut Ngamkhanong1Sakdirat Kaewunruen2Department of Civil and Environmental Engineering, Faculty of Engineering, Prince of Songkla University, Songkhla, ThailandAdvanced Railway Infrastructure, Innovation and Systems Engineering (ARIISE) Research Unit, Department of Civil Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok, ThailandDepartment of Civil Engineering, University of Birmingham, Birmingham, United KingdomTrain wheel wear significantly impacts wheel-rail interaction forces and is an unavoidable issue in the railway industry. This study focuses on regular wear, specifically changes in wheel profiles such as tread wear, flange height, and flange thickness. Effective wheel wear management is crucial for maintaining the reliability, safety, and efficiency of rail systems. However, regular measurement of wheel profiles is often limited by constraints such as dense traffic, budget, time, and remote assets, which reduces the effectiveness of traditional maintenance approaches. This study proposes a hybrid learning strategy combining supervised and reinforcement learning techniques to optimize train wheel wear management under these constraints and achieve predictive maintenance. The supervised learning model, developed from validated simulations, predicts wear progression, while reinforcement learning improves maintenance decision-making using basic operational data without regular measurements. Various machine-learning techniques are explored and fine-tuned to identify the best models for preventing faulty wheels without the need for frequent inspections. By integrating these two learning approaches, the framework enhances the accuracy of wear predictions and optimizes maintenance schedules, reducing the risk of over-maintenance or unexpected failures. This integrated model addresses challenges such as system complexity, limited data, and cost-effectiveness in the industry. In terms of supervised learning, the R2 for tread wear prediction improves from 0.94 to 0.95 compared to previous studies, and the model, when integrated with reinforcement learning, significantly reduces defects based on wear and irregular wheel dimensions. This research is the first to integrate supervised and reinforcement learning specifically for train wheel wear management under limited measurement data constraints, offering a breakthrough compared to traditional methods that rely on regular inspections. The study provides significant benefits for the railway industry, including reduced maintenance costs, improved maintenance efficiency, lower defect rates, reduced possession and inspection time, and enhanced passenger comfort and safety.https://www.frontiersin.org/articles/10.3389/fbuil.2025.1546957/fullhybrid learning strategiessupervised learningreinforcement learningtrain wheel wearpredictive maintenanceconditional monitoring |
| spellingShingle | Jessada Sresakoolchai Chayut Ngamkhanong Sakdirat Kaewunruen Hybrid learning strategies: integrating supervised and reinforcement techniques for railway wheel wear management with limited measurement data Frontiers in Built Environment hybrid learning strategies supervised learning reinforcement learning train wheel wear predictive maintenance conditional monitoring |
| title | Hybrid learning strategies: integrating supervised and reinforcement techniques for railway wheel wear management with limited measurement data |
| title_full | Hybrid learning strategies: integrating supervised and reinforcement techniques for railway wheel wear management with limited measurement data |
| title_fullStr | Hybrid learning strategies: integrating supervised and reinforcement techniques for railway wheel wear management with limited measurement data |
| title_full_unstemmed | Hybrid learning strategies: integrating supervised and reinforcement techniques for railway wheel wear management with limited measurement data |
| title_short | Hybrid learning strategies: integrating supervised and reinforcement techniques for railway wheel wear management with limited measurement data |
| title_sort | hybrid learning strategies integrating supervised and reinforcement techniques for railway wheel wear management with limited measurement data |
| topic | hybrid learning strategies supervised learning reinforcement learning train wheel wear predictive maintenance conditional monitoring |
| url | https://www.frontiersin.org/articles/10.3389/fbuil.2025.1546957/full |
| work_keys_str_mv | AT jessadasresakoolchai hybridlearningstrategiesintegratingsupervisedandreinforcementtechniquesforrailwaywheelwearmanagementwithlimitedmeasurementdata AT chayutngamkhanong hybridlearningstrategiesintegratingsupervisedandreinforcementtechniquesforrailwaywheelwearmanagementwithlimitedmeasurementdata AT sakdiratkaewunruen hybridlearningstrategiesintegratingsupervisedandreinforcementtechniquesforrailwaywheelwearmanagementwithlimitedmeasurementdata |