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Enhanced performance monitoring of a ballasted high-speed railway

Enhanced performance monitoring of a ballasted high-speed railway
Enhanced performance monitoring of a ballasted high-speed railway
Maintenance of railway track is required to ensure regular track geometry, adequate support conditions and good performance. Understanding the long and the short term performance and how track features and interventions affect the behaviour is important for maintenance. Lineside monitoring techniques are an effective means of assessing performance and are often used to characterise typical track movements and to determine the track stiffness. However, to study long term performance, lengths of deployment and the frequency of measurement need to increase, with implications for the measurement systems and analysis methods. This research develops lineside monitoring techniques and applies them to railway track over extended periods, for condition monitoring, investigating deterioration and evaluating maintenance on High Speed One, a ballasted high-speed railway in the United Kingdom.

Laboratory and field tests show that inexpensive Micro Electro Mechanical Systems accelerometers can provide data of sufficient quality for quantifying track deflection under high speed conditions, enabling long term condition monitoring. This generates large volumes of data, which need to be processed and analysed automatically. Train geometries mean that track vibration signals have properties which facilitate new analysis techniques for characterising deflection and determining stiffness. Characterising the range of total and downward deflection is non-trivial due to variability and signal processing artefacts. The cumulative distribution function for track deflection is used to overcome these issues. This enables the typical downward deflection, the at-rest position and uplift to be identified, while implicitly averaging over vehicles. The frequency and magnitude of the dominant peaks in the spectrum for low frequency track vibration depends on the train (or vehicle) geometry, wheel loads and track stiffness. This property has been used to determine the track stiffness in the frequency domain. Monitoring based on this approach has been applied at defect sites on High Speed One to investigate performance and inform and evaluate maintenance. Records of deflection and stiffness highlighted problems and helped improve maintenance. Using long-term monitoring to understand the condition of the track and then specify remediation work was more effective than standard practice. Results show sustained reductions in deflection and restored support conditions after an intervention, giving the infrastructure managers confidence in using monitoring to innovate maintenance.
University of Southampton
Milne, David
6b321a45-c19a-4243-b562-517a69e5affc
Milne, David
6b321a45-c19a-4243-b562-517a69e5affc
Powrie, William
600c3f02-00f8-4486-ae4b-b4fc8ec77c3c

Milne, David (2017) Enhanced performance monitoring of a ballasted high-speed railway. University of Southampton, Doctoral Thesis, 287pp.

Record type: Thesis (Doctoral)

Abstract

Maintenance of railway track is required to ensure regular track geometry, adequate support conditions and good performance. Understanding the long and the short term performance and how track features and interventions affect the behaviour is important for maintenance. Lineside monitoring techniques are an effective means of assessing performance and are often used to characterise typical track movements and to determine the track stiffness. However, to study long term performance, lengths of deployment and the frequency of measurement need to increase, with implications for the measurement systems and analysis methods. This research develops lineside monitoring techniques and applies them to railway track over extended periods, for condition monitoring, investigating deterioration and evaluating maintenance on High Speed One, a ballasted high-speed railway in the United Kingdom.

Laboratory and field tests show that inexpensive Micro Electro Mechanical Systems accelerometers can provide data of sufficient quality for quantifying track deflection under high speed conditions, enabling long term condition monitoring. This generates large volumes of data, which need to be processed and analysed automatically. Train geometries mean that track vibration signals have properties which facilitate new analysis techniques for characterising deflection and determining stiffness. Characterising the range of total and downward deflection is non-trivial due to variability and signal processing artefacts. The cumulative distribution function for track deflection is used to overcome these issues. This enables the typical downward deflection, the at-rest position and uplift to be identified, while implicitly averaging over vehicles. The frequency and magnitude of the dominant peaks in the spectrum for low frequency track vibration depends on the train (or vehicle) geometry, wheel loads and track stiffness. This property has been used to determine the track stiffness in the frequency domain. Monitoring based on this approach has been applied at defect sites on High Speed One to investigate performance and inform and evaluate maintenance. Records of deflection and stiffness highlighted problems and helped improve maintenance. Using long-term monitoring to understand the condition of the track and then specify remediation work was more effective than standard practice. Results show sustained reductions in deflection and restored support conditions after an intervention, giving the infrastructure managers confidence in using monitoring to innovate maintenance.

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Published date: October 2017

Identifiers

Local EPrints ID: 417853
URI: http://eprints.soton.ac.uk/id/eprint/417853
PURE UUID: 40553ca7-4a4b-4600-8d21-8321712f1cf7
ORCID for David Milne: ORCID iD orcid.org/0000-0001-6702-3918
ORCID for William Powrie: ORCID iD orcid.org/0000-0002-2271-0826

Catalogue record

Date deposited: 15 Feb 2018 17:30
Last modified: 16 Mar 2024 06:11

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Contributors

Author: David Milne ORCID iD
Thesis advisor: William Powrie ORCID iD

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