Adhesion estimation at the wheel-rail interface using advanced model-based filtering

C. P. Ward, R. M. Goodall, R. Dixon, G. A. Charles

Research output: Contribution to journalArticle

20 Citations (Scopus)

Abstract

The railway industry in the UK is currently expanding the use of condition monitoring of railway vehicles. These systems can be used to improve maintenance procedures or could potentially be used to monitor current vehicle running conditions without the use of cost prohibitive sensors. This paper looks at a novel method for the online detection of areas of low adhesion in the wheel/rail contact that cause significant disruption to the running of a network, particularly in the autumn season. The proposed method uses a Kalman-Bucy filter to estimate the creep forces in the wheel-rail contact area; post-processing is then applied to provide information indicative of the actual adhesion level. The algorithm uses data that, in practice, would be available from a set of modest cost inertial sensors mounted on the vehicle bogie and wheel-sets. The efficacy of the approach is demonstrated using simulation data from a nonlinear dynamic model of the vehicle and its track interface.

Original languageEnglish
Pages (from-to)1797-1816
Number of pages20
JournalVehicle System Dynamics
Volume50
Issue number12
Early online date24 Jul 2012
DOIs
Publication statusPublished - 1 Dec 2012
Externally publishedYes

Fingerprint

Rails
Wheels
Adhesion
Sensors
Condition monitoring
Kalman filters
Costs
Dynamic models
Creep
Processing
Industry

Cite this

Ward, C. P. ; Goodall, R. M. ; Dixon, R. ; Charles, G. A. / Adhesion estimation at the wheel-rail interface using advanced model-based filtering. In: Vehicle System Dynamics. 2012 ; Vol. 50, No. 12. pp. 1797-1816.
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Adhesion estimation at the wheel-rail interface using advanced model-based filtering. / Ward, C. P.; Goodall, R. M.; Dixon, R.; Charles, G. A.

In: Vehicle System Dynamics, Vol. 50, No. 12, 01.12.2012, p. 1797-1816.

Research output: Contribution to journalArticle

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