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

Abstract

The issue of low adhesion between the wheel and the rail has been a problem for thedesign and operation of the railway vehicles. The level of adhesion can be influenced bymany different factors, such as contamination, climate, and vegetation, and it isextremely difficult to predict with certainty. Changes in the adhesion conditions can berapid 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 asignificant scientific challenge to effectively design a suitable technique to tackle thisproblem. This thesis presents the development of a unique, vehicle based technique forthe real-time estimation of the contact conditions using multiple models to representvariations in the adhesion level and different contact conditions. The proposed solutionexploits the fact that the dynamic behaviour of a railway vehicle is strongly affected bythe nonlinearities and the variations in creep characteristics. The purpose of the proposedscheme is to interpret these variations in the dynamic response of the wheelset,developing useful contact condition information. The proposed system involves the useof a number of carefully selected mathematical models (or estimators) of a rail vehicle tomimic train dynamic behaviours in response to different track conditions. Each of theestimators is tuned to match one particular track condition to give the best results at thespecific design point. Increased estimation errors are expected if the contact condition isnot at or near the chosen operating point. The level of matches/mismatches is reflected inthe estimation errors (or residuals) of the models concerned when compared with the realvehicle (through the measurement output of vehicle mounted inertial sensors). Theoutput residuals from all the models are then assessed using an artificial intelligencedecision-making approach to determine which of the models provides a best match to thepresent operating condition and, thus, provide real-time information about trackconditions