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Multiple model based real time estimation of wheel-rail contact conditions

Hussain, I 2012, Multiple model based real time estimation of wheel-rail contact conditions , PhD thesis, Salford: University of Salford.

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Abstract

The issue of low adhesion between the wheel and the rail has been a problem for the design and operation of the railway vehicles. The level of adhesion can be influenced by many different factors, such as contamination, climate, and vegetation, and it is extremely difficult to predict with certainty. Changes in the adhesion conditions can be rapid and short-lived, and values can differ from position to position along a route, depending on the type and degree of contamination. All these factors present a significant scientific challenge to effectively design a suitable technique to tackle this problem. This thesis presents the development of a unique, vehicle based technique for the real-time estimation of the contact conditions using multiple models to represent variations in the adhesion level and different contact conditions. The proposed solution exploits the fact that the dynamic behaviour of a railway vehicle is strongly affected by the nonlinearities and the variations in creep characteristics. The purpose of the proposed scheme is to interpret these variations in the dynamic response of the wheelset, developing useful contact condition information. The proposed system involves the use of a number of carefully selected mathematical models (or estimators) of a rail vehicle to mimic train dynamic behaviours in response to different track conditions. Each of the estimators is tuned to match one particular track condition to give the best results at the specific design point. Increased estimation errors are expected if the contact condition is not at or near the chosen operating point. The level of matches/mismatches is reflected in the estimation errors (or residuals) of the models concerned when compared with the real vehicle (through the measurement output of vehicle mounted inertial sensors). The output residuals from all the models are then assessed using an artificial intelligence decision-making approach to determine which of the models provides a best match to the present operating condition and, thus, provide real-time information about track conditions.

Item Type: Thesis (PhD)
Schools: Schools > School of Computing, Science and Engineering
Depositing User: WM Taylor
Date Deposited: 09 Mar 2016 09:33
Last Modified: 29 Apr 2016 13:04
URI: http://usir.salford.ac.uk/id/eprint/38094

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