TY - JOUR
T1 - Development of railway track condition monitoring from multi-train in-service vehicles
AU - Balouchi, Farouk
AU - Bevan, Adam
AU - Formston, Roy
N1 - Funding Information:
The authors are grateful to the United Kingdom Railway Safety Standards Board (RSSB 1709 and RSSB 1814) for the financial support during the feasibility stage of the development of the project. To Network Rail (NR) for their cooperation in providing NMT data and to Siemen Plc. for taking the initiative to follow the development through to commercialisation. The authors would like to thank RSSB for the financial support during the feasibility stage of the development of the project and?Network Rail for their support during the development of the?monitoring system, especially for the NMT data that was?used?to validate the system during feasibility and testing phase of the development.
Funding Information:
The authors would like to thank RSSB for the financial support during the feasibility stage of the development of the project and Network Rail for their support during the development of the monitoring system, especially for the NMT data that was used to validate the system during feasibility and testing phase of the development.
Publisher Copyright:
© 2020 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.
PY - 2021/9/1
Y1 - 2021/9/1
N2 - 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.
AB - 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.
KW - Remote condition monitoring
KW - safety improvement
KW - network performance improvement
KW - rough ride improvement
KW - railway track maintenance improvement
UR - http://www.scopus.com/inward/record.url?scp=85084135150&partnerID=8YFLogxK
U2 - 10.1080/00423114.2020.1755045
DO - 10.1080/00423114.2020.1755045
M3 - Article
VL - 59
SP - 1397
EP - 1417
JO - Vehicle System Dynamics
JF - Vehicle System Dynamics
SN - 0042-3114
IS - 9
ER -