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Open AccessArticle

Condition Monitoring of Railway Tracks from Car-Body Vibration Using a Machine Learning Technique

Department of Mechanical Engineering, Nihon University, Chiba 275-8575, Japan
Current Address: 1-2-1 Izumi-cho, Narashino-shi, Chiba, Japan.
Appl. Sci. 2019, 9(13), 2734; https://doi.org/10.3390/app9132734
Received: 6 June 2019 / Revised: 3 July 2019 / Accepted: 4 July 2019 / Published: 5 July 2019
(This article belongs to the Special Issue Vibration-Based Structural Health Monitoring)
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. View Full-Text
Keywords: railway; condition monitoring; fault detection; preventive maintenance; machine learning railway; condition monitoring; fault detection; preventive maintenance; machine learning
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Tsunashima, H. Condition Monitoring of Railway Tracks from Car-Body Vibration Using a Machine Learning Technique. Appl. Sci. 2019, 9, 2734.

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