Wheel flat detection using long short-term memory and transformer models with a 1:10 scale railway test rig
In railway systems, the detection of wheel flats is essential for ensuring safety and reducing maintenance costs. This study compares the performance of Long Short-Term Memory and Transformer models in detecting wheel flats using data from a 1:10 scale railway test rig. The findings indicate that th...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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SAGE Publishing
2025-01-01
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| Series: | Advances in Mechanical Engineering |
| Online Access: | https://doi.org/10.1177/16878132251314988 |
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| _version_ | 1850173373864214528 |
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| author | Yong Cui Euiyoul Kim Shizhe Yan Qing Yu |
| author_facet | Yong Cui Euiyoul Kim Shizhe Yan Qing Yu |
| author_sort | Yong Cui |
| collection | DOAJ |
| description | In railway systems, the detection of wheel flats is essential for ensuring safety and reducing maintenance costs. This study compares the performance of Long Short-Term Memory and Transformer models in detecting wheel flats using data from a 1:10 scale railway test rig. The findings indicate that the Transformer model significantly outperforms the Long Short-Term Memory model, especially when feature-level sensor fusion is employed, achieving an average error as low as 0.0069 mm with percentage of error at 5.30%, minimizing the maximum error to 0.0985 mm. The study emphasizes the potential of Transformer models in railway diagnostics, particularly for applications requiring high accuracy and reliability. The insights gained from this research have practical implications for improving the precision of wheel flat detection in real-world railway operations, enhancing both safety and efficiency. |
| format | Article |
| id | doaj-art-ab5e9b73a6b04b82bbff528b1b725c25 |
| institution | OA Journals |
| issn | 1687-8140 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Advances in Mechanical Engineering |
| spelling | doaj-art-ab5e9b73a6b04b82bbff528b1b725c252025-08-20T02:19:51ZengSAGE PublishingAdvances in Mechanical Engineering1687-81402025-01-011710.1177/16878132251314988Wheel flat detection using long short-term memory and transformer models with a 1:10 scale railway test rigYong Cui0Euiyoul Kim1Shizhe Yan2Qing Yu3Chinese-German Research and Development Centre for Railway and Transportation Technology Stuttgart (CDFEB e. V.), Stuttgart, GermanyChinese-German Research and Development Centre for Railway and Transportation Technology Stuttgart (CDFEB e. V.), Stuttgart, GermanySchool of Urban Construction and Transportation, Anhui Provincial Key Laboratory of Urban Rail Transit Safety and Emergency Management, Hefei University, Hefei, Anhui, ChinaChinese-German Research and Development Centre for Railway and Transportation Technology Stuttgart (CDFEB e. V.), Stuttgart, GermanyIn railway systems, the detection of wheel flats is essential for ensuring safety and reducing maintenance costs. This study compares the performance of Long Short-Term Memory and Transformer models in detecting wheel flats using data from a 1:10 scale railway test rig. The findings indicate that the Transformer model significantly outperforms the Long Short-Term Memory model, especially when feature-level sensor fusion is employed, achieving an average error as low as 0.0069 mm with percentage of error at 5.30%, minimizing the maximum error to 0.0985 mm. The study emphasizes the potential of Transformer models in railway diagnostics, particularly for applications requiring high accuracy and reliability. The insights gained from this research have practical implications for improving the precision of wheel flat detection in real-world railway operations, enhancing both safety and efficiency.https://doi.org/10.1177/16878132251314988 |
| spellingShingle | Yong Cui Euiyoul Kim Shizhe Yan Qing Yu Wheel flat detection using long short-term memory and transformer models with a 1:10 scale railway test rig Advances in Mechanical Engineering |
| title | Wheel flat detection using long short-term memory and transformer models with a 1:10 scale railway test rig |
| title_full | Wheel flat detection using long short-term memory and transformer models with a 1:10 scale railway test rig |
| title_fullStr | Wheel flat detection using long short-term memory and transformer models with a 1:10 scale railway test rig |
| title_full_unstemmed | Wheel flat detection using long short-term memory and transformer models with a 1:10 scale railway test rig |
| title_short | Wheel flat detection using long short-term memory and transformer models with a 1:10 scale railway test rig |
| title_sort | wheel flat detection using long short term memory and transformer models with a 1 10 scale railway test rig |
| url | https://doi.org/10.1177/16878132251314988 |
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