A computationally efficient engineering model to predict adhesion in rolling contact in the presence of water is presented which may be implemented in multibody dynamics software or in railway vehicle braking models. This model has been developed in a project funded by the Rail Safety and Standards Board (RSSB) and Network Rail. It is referred to as the water-induced low adhesion creep force (WILAC) model. The model covers a wide range of conditions from dry over damp to wet. Special emphasis is put on little amounts of water which can cause low adhesion without any oil or grease. Such conditions may be encountered in humid weather or at the onset of rain. The model is parameterised based on experimental results from a tram wheel test rig. Adhesion values as low as 0.06 are observed at high creep with only wear debris and little water present in the contact. The model results also agree with experimental data from locomotive tests in dry and wet conditions.V tomto článku je představen výpočetně efektivní model pro predikci adheze ve valivém kontaktu za přítomnosti vody, který lze využít v softwaru pro dynamiku soustav tuhých těles nebo v modelech brzdění železničních vozidel. Tento model byl vyvinut v projektu financovaném organizací Rail Safety and Standards Board (RSSB) a Network Rail. Je označen jako model skluzových sil při nízké adhezi způsobené vodou (angl. zkratka WILAC). Model pokrývá široký rozsah podmínek od suchých přes vlhké až po mokré povrchy. Zvláštní pozornost je věnována malému množství vody, jež může výrazně zhoršit adhezi i bez přítomnosti oleje či tuku. Takové podmínky mohou nastat ve vlhkém počasí nebo při začátku deště. Parametry modelu jsou stanoveny na základě experimentálních výsledků na zkušebním stavu tramvajového kola. Při nich byly pozorovány hodnoty součinitele adheze klesající až k 0,06 při vyšším skluzu, přičemž v kontaktu byly přítomny jen částice opotřebení a malé množství vody. Výstupy modelu rovněž souhlasí s daty získanými na lokomotivách v suchých a mokrých podmínkách

Investigation of traction and braking phenomena in rail vehicles requires proper modelling of friction in the wheel–rail contact in the mode of full sliding. Adhesion characteristic with falling velocity-dependent friction is therefore often employed. Such a function however still describes a steady state, steady dry friction conditions. It is observed that the conditions may be variable, though; energy dissipation in the contact area brings a cleaning (conditioning) effect marked particularly at low adhesion and improving friction above the initial level. The present paper explains the role of this effect in measured wheel–rail adhesion characteristics obtained in laboratory experiments. They are understood and evaluated as transitions between steady characteristics for contaminated and cleaned surfaces. Mathematical description of the transition is proposed, suitable for use with any contact model using the coefficient of friction as an input.

In recent years, there has been an increasing interest in designing intelligent vehicles such that they can take necessary actions according to the environmental changes around them and they can inform decision makers about these changes. For safer and cheaper transport, dynamic modelling of these vehicles and identification of such changes in environment based on these models plays an important role. In this study, a sigma point Kalman filter-based scheme (i.e. joint unscented Kalman filter) is proposed to estimate maximum friction coefficient as a parameter in wheel–rail interface. This estimation scheme uses interpretation of lateral and yaw dynamic response of a wheelset to identify maximum friction coefficient. This joint unscented Kalman filter-based approach provides information about the friction conditions in wheel–rail interface without post-processing of estimated data.

Monitoring the conditions of railway vehicle systems plays an important role in the maintenance of safety and performance of railway vehicles. Rolling radius is one of the properties that should be monitored continuously for the predictive maintenance of a railway vehicle since it changes with time due to wheel wear. In this study, a model-based condition monitoring methodology, which is based on an unscented Kalman filter, is proposed. The model includes the torsional dynamics of an independently rotating tram wheel with a traction motor and a contact model. The rolling radius is estimated by considering the traction effort of the motor and the angular velocity measurements. The proposed methodology is tested on a tram wheel test stand (roller rig), which has a wheel on roller configuration. First, a mathematical model is validated by the measurements taken from the test stand. Second, the unscented Kalman filter is applied as a parameter estimator. The results demonstrate that the proposed scheme is a promising option to be used in the predictive condition monitoring of the wheel profile for traction vehicles.