Condition Monitoring of Railway Tracks from Car-Body Vibration Using a Machine Learning Technique
A track condition monitoring system that uses a compact on-board sensing device has been developed and applied for track condition monitoring of regional railway lines in Japan. Monitoring examples show that the system is effective for regional railway operators. A classifier for track faults has be...
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-07-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/9/13/2734 |
| _version_ | 1818927943445905408 |
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| author | Hitoshi Tsunashima |
| author_facet | Hitoshi Tsunashima |
| author_sort | Hitoshi Tsunashima |
| collection | DOAJ |
| description | A track condition monitoring system that uses a compact on-board sensing device has been developed and applied for track condition monitoring of regional railway lines in Japan. Monitoring examples show that the system is effective for regional railway operators. A classifier for track faults has been developed to detect track fault automatically. Simulation studies using SIMPACK and field tests were carried out to detect and isolate the track faults from car-body vibration. The results show that the feature of track faults is extracted from car-body vibration and classified from proposed feature space using machine learning techniques. |
| first_indexed | 2024-12-20T03:21:03Z |
| format | Article |
| id | doaj.art-541c5d3d9658447ea54fff2e5d106d32 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-12-20T03:21:03Z |
| publishDate | 2019-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-541c5d3d9658447ea54fff2e5d106d322022-12-21T19:55:14ZengMDPI AGApplied Sciences2076-34172019-07-01913273410.3390/app9132734app9132734Condition Monitoring of Railway Tracks from Car-Body Vibration Using a Machine Learning TechniqueHitoshi Tsunashima0Department of Mechanical Engineering, Nihon University, Chiba 275-8575, JapanA track condition monitoring system that uses a compact on-board sensing device has been developed and applied for track condition monitoring of regional railway lines in Japan. Monitoring examples show that the system is effective for regional railway operators. A classifier for track faults has been developed to detect track fault automatically. Simulation studies using SIMPACK and field tests were carried out to detect and isolate the track faults from car-body vibration. The results show that the feature of track faults is extracted from car-body vibration and classified from proposed feature space using machine learning techniques.https://www.mdpi.com/2076-3417/9/13/2734railwaycondition monitoringfault detectionpreventive maintenancemachine learning |
| spellingShingle | Hitoshi Tsunashima Condition Monitoring of Railway Tracks from Car-Body Vibration Using a Machine Learning Technique Applied Sciences railway condition monitoring fault detection preventive maintenance machine learning |
| title | Condition Monitoring of Railway Tracks from Car-Body Vibration Using a Machine Learning Technique |
| title_full | Condition Monitoring of Railway Tracks from Car-Body Vibration Using a Machine Learning Technique |
| title_fullStr | Condition Monitoring of Railway Tracks from Car-Body Vibration Using a Machine Learning Technique |
| title_full_unstemmed | Condition Monitoring of Railway Tracks from Car-Body Vibration Using a Machine Learning Technique |
| title_short | Condition Monitoring of Railway Tracks from Car-Body Vibration Using a Machine Learning Technique |
| title_sort | condition monitoring of railway tracks from car body vibration using a machine learning technique |
| topic | railway condition monitoring fault detection preventive maintenance machine learning |
| url | https://www.mdpi.com/2076-3417/9/13/2734 |
| work_keys_str_mv | AT hitoshitsunashima conditionmonitoringofrailwaytracksfromcarbodyvibrationusingamachinelearningtechnique |