Abstract
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.
Highlights
With the recent development of sensors and information technology (IT), conditions in railway facilities can be monitored continually by using sensors installed in the rolling stock and in areas adjacent to the track
We analysed the effects of railway track irregularities on car-body vibrations through railway vehicle travel simulations conducted by using the SIMPACK software package
We subsequently developed an algorithm based on machine learning to diagnose track conditions by extracting features to detect track irregularities
Summary
With the recent development of sensors and information technology (IT), conditions in railway facilities can be monitored continually by using sensors installed in the rolling stock and in areas adjacent to the track. Track condition monitoring systems that observe car-body vibrations in in-service vehicles have already been developed for regional railways and have exhibited the capability to oversee track conditions [1]. Recent studies proposed employing a Kalman filter to predict track irregularities from the acceleration measurements of car-body vibrations of railway vehicles [2]. Low-cost monitoring systems have been developed based on on-board sensing devices and global navigation satellite system (GNSS) data. In this system, the sensing device is installed in an in-service vehicle to measure car-body vibrations. We adopted machine learning techniques and developed an algorithm to automatically diagnose track conditions from car-body vibrations to automatically detect track irregularities and identify them by type. We examined the possibility of diagnosing track conditions for an actual regional railway line
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