2019 Fiscal Year Final Research Report
Development and evaluation of a track condition monitoring system using a machine learning technique
| Project/Area Number |
17K06240
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Research Field |
Dynamics/Control
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| Research Institution | Nihon University |
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Principal Investigator |
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| Project Period (FY) |
2017-04-01 – 2020-03-31
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| Keywords | 鉄道 / 軌道 / 状態監視 / 振動 / 機械学習 / 安全性 |
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| Outline of Final Research Achievements |
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.
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| Free Research Field |
機械力学・制御工学
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| Academic Significance and Societal Importance of the Research Achievements |
地方鉄道では，十分な軌道検査が行えない事業者も少なくない．このような問題を解決するためには，地方鉄道の営業車両の走行データを一括収集・管理し，軌道の状態を診断・予測するセンターが有効であると考えられ，多くの地方鉄道事業者における軌道保守に関する問題を解決できるものと考えられる．本研究の成果は，このデータセンター（令和元年12月開設済み）における診断業務に活用され実績をあげている．
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