Automated processing of railway track deflection signals obtained from velocity and acceleration measurements
Automated processing of railway track deflection signals obtained from velocity and acceleration measurements
Measurements of low frequency vibration are increasingly being used to assess the condition and performance of railway track. Displacements used to characterise track movement under train loads are commonly obtained from velocity or acceleration signals. Artefacts from signal processing, which lead to a shift in the datum associated with the at-rest position, as well as variability between successive wheels, mean that interpreting measurements is non-trivial. As a result, deflections are often interpreted by inspection rather than following an algorithmic or statistical process. This can limit the amount of data that can be usefully analysed in practice, militating against widespread or long-term use of track vibration measurements for condition or performance monitoring purposes. This paper shows how the cumulative distribution function of the track deflection can be used to identify the at-rest position and to interpret the typical range of track movement from displacement data. This process can be used to correct for the shift in the at-rest position in velocity or acceleration data, to determine the proportion of upward and downward movement and to align data from multiple transducers to a common datum for visualising deflection as a function of distance along the track. The technique provides a means of characterising track displacement automatically, which can be used as a measure of system performance. This enables large volumes of track vibration data to be used for condition monitoring.
Milne, David
6b321a45-c19a-4243-b562-517a69e5affc
Le Pen, Louis
4a38e256-d113-4bba-b0d4-32d41995928a
Powrie, William
600c3f02-00f8-4486-ae4b-b4fc8ec77c3c
Thompson, David
bca37fd3-d692-4779-b663-5916b01edae5
Milne, David
6b321a45-c19a-4243-b562-517a69e5affc
Le Pen, Louis
4a38e256-d113-4bba-b0d4-32d41995928a
Powrie, William
600c3f02-00f8-4486-ae4b-b4fc8ec77c3c
Thompson, David
bca37fd3-d692-4779-b663-5916b01edae5
Milne, David, Le Pen, Louis, Powrie, William and Thompson, David
(2018)
Automated processing of railway track deflection signals obtained from velocity and acceleration measurements.
Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit.
(doi:10.1177/0954409718762172).
Abstract
Measurements of low frequency vibration are increasingly being used to assess the condition and performance of railway track. Displacements used to characterise track movement under train loads are commonly obtained from velocity or acceleration signals. Artefacts from signal processing, which lead to a shift in the datum associated with the at-rest position, as well as variability between successive wheels, mean that interpreting measurements is non-trivial. As a result, deflections are often interpreted by inspection rather than following an algorithmic or statistical process. This can limit the amount of data that can be usefully analysed in practice, militating against widespread or long-term use of track vibration measurements for condition or performance monitoring purposes. This paper shows how the cumulative distribution function of the track deflection can be used to identify the at-rest position and to interpret the typical range of track movement from displacement data. This process can be used to correct for the shift in the at-rest position in velocity or acceleration data, to determine the proportion of upward and downward movement and to align data from multiple transducers to a common datum for visualising deflection as a function of distance along the track. The technique provides a means of characterising track displacement automatically, which can be used as a measure of system performance. This enables large volumes of track vibration data to be used for condition monitoring.
Text
Interpreting railway track deflection signal
- Accepted Manuscript
More information
Accepted/In Press date: 25 January 2018
e-pub ahead of print date: 19 March 2018
Identifiers
Local EPrints ID: 417683
URI: http://eprints.soton.ac.uk/id/eprint/417683
ISSN: 0954-4097
PURE UUID: 6d661fd9-8959-48ba-8255-19c4fc714652
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Date deposited: 09 Feb 2018 17:30
Last modified: 16 Mar 2024 06:09
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