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.
The article deals with the view of modern wheelset drive construction design in the first part. Next part deals with dynamics of the wheelset individual drive torsion system in electric traction vehicles, explained by the drive model in several variants. The basics of time simulation of the transition dynamics effects have been explained and the frequency analysis of the system has been shown. Identification of transition dynamics effects in the wheelset drive enables to evaluate precisely the loading on individual parts during the design stage, to apply the results in anti-skid protection and probably also to disclose some effects due to wear in the wheel-rail contact.
Advanced antislip control methods are available these days. However, due to increasing requirements with regard to demand and emerging technologies in the field of railways, further research on antislip control is required. Therefore, in this study, an antislip control algorithm, based on a sliding mode control, is proposed to stabilize the slip and improve the traction ability of a full-scale tram wheel test stand. To verify the validity of the control scheme, a numerical model of a tram wheel test stand has been generated using the MATLAB editor. The Freibauer and Polach contact theory has been employed to determine the coefficient of adhesion and adhesion force. Moreover, the derived algorithm was implemented on a full-scale tram wheel test stand. Experiments were carried out under several wheel-roller surface conditions. The results of the refined numerical model are in good agreement with the experimental data obtained from the tram wheel test stand. For both the experimental tests and the numerical model, the response of the proposed control algorithm is rather satisfactory with regard to the stabilization of the slip and improvement of the traction ability.
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.
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