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Please use this identifier to cite or link to this item: https://dspace.lboro.ac.uk/2134/6639

Title: Wheel-rail profile condition monitoring
Authors: Ward, Christopher P.
Goodall, Roger M.
Dixon, Roger
Keywords: Accelerometers
Fault diagnosis/detection
Kalman filters
Nonlinear systems
Piecewise linear analysis
Recursive least squares
Railways vehicle dynamics
Issue Date: 2010
Publisher: © IEEE
Citation: WARD, C.P., GOODALL, R.M. and DIXON, R., 2010. Wheel-rail profile condition monitoring. UKACC International Conference on Control, Coventry, UK, 7-10 September, 6pp.
Abstract: Increased railway patronage worldwide is putting pressure on rolling stock and infrastructure to operate at higher capacity and with improved punctuality. Condition monitoring is seen as a contributing factor in enabling this and is highlighted here in the context of rolling stock being procured with high capacity data buses, multiple sensors and centralised control. This therefore leaves scope for advanced computational diagnostic concepts. The rail vehicle bogie and associated wheelsets are one of the largest and most costly areas of maintenance on rolling stock and presented here is a potential method for real time estimation of wheel-rail contact wear to move this currently scheduled based assessment to condition based assessment. This technique utilises recursive ‘grey box’ least squares system identification, used in a piecewise linear manner, to capture the strongly discontinuous nonlinear nature of the wheel-rail geometry.
Description: This is a conference paper.
Version: Accepted for publication
DOI: 10.1049/ic.2010.0448
URI: https://dspace.lboro.ac.uk/2134/6639
Publisher Link: http://dx.doi.org/10.1049/ic.2010.0448
ISBN: 9781846000386
Appears in Collections:Conference Papers and Presentations (Mechanical, Electrical and Manufacturing Engineering)

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