Development of railway track condition monitoring from multi-train in-service vehicles

• Published in: Vehicle system dynamics Vol. 59; no. 9; pp. 1397 - 1417
• Main Authors: Balouchi, F., Bevan, A., Formston, R.
• Format: Journal Article
• Language: English
• Published: Abingdon Taylor & Francis 02.09.2021; Taylor & Francis Ltd
• Subjects: Accuracy; Cellular communication; Condition monitoring; Data acquisition; Defects; Inspection; Machine learning; Maintenance; network performance improvement; Passenger comfort; railway track maintenance improvement; Railway tracks; Remote condition monitoring; rough ride improvement; safety improvement; Signal processing; Statistical correlation; Trains; Vibration response
• ISSN: 0042-3114; 1744-5159
• DOI: 10.1080/00423114.2020.1755045

A cab-based track monitoring system has been developed which makes use of the existing on-board GSM-R cab radio present in the majority of trains operating in the UK. With the addition of a low-cost sensor, type, location and severity of the track defects are reported using the system. The system improves safety and network performance by efficiently directing maintenance crews to the location of defects, minimising time spent on maintenance and inspection. Initially, vehicle dynamic simulation was used to test the feasibility of the system for defect monitoring and to develop compensation factors for vehicle type and operating speed. Novel on-board signal processing techniques are also presented through comparison of vibration response from sites with known defects and outputs from Network Rail's (NR) New Measurement Train (NMT). Good agreement was reported for track faults in relation to vertical and lateral alignment and dip faults. Statistically, good agreement has been demonstrated, suggesting that the data acquired could be used to provide an indication of track quality thereby improving network performance, reducing rough ride and leading to improved passenger comfort. Improvements in the measured and statistical correlation are anticipated through the use, of multi-train / multi-journey and machine learning methods.