Guy Charles

Model-based condition monitoring at the wheel–rail interface

The dynamics of a rail vehicle is driven by the interaction between the wheel and rail. Any change to, for example, the shape of the wheel–rail profile or the contact adhesion conditions will change the response of the vehicle. The condition monitoring challenge is to interpret these changes into useful condition information. This paper presents the ongoing research into model-based condition monitoring at the wheel–rail interface applied to two applications: (i) wheel–rail profile estimation; and (ii) low adhesion detection. The wheel–rail profile estimation was carried out on a linearised simulation model that included a nonlinear conicity function. This function could be successfully estimated by also estimating the lateral track irregularities and giving the Kalman Filter self-updating information about the shape of the conicity function. The low adhesion detection was carried out on a complex nonlinear half vehicle model that included saturating contact force equations. The contact forces could be estimated by considering the half vehicle floating on a set of contact forces. Low adhesion conditions can be implied by the relative magnitudes of these contact forces.

Low adhesion estimation

Low adhesion in wheel-rail contact causes huge problems for scheduling and safety of railway networks. Braking and guidance forces have the same force generation mechanism, via the creep forces at the wheel-rail contact. This paper demonstrates the application of a Kalman filter to estimate the adhesion condition on the rails. The key aspect of this approach is that estimation is carried out on-line, during normal running, i.e. before the brakes have been applied. This will give a full network-wide picture of low adhesion conditions. The results show that the different adhesion levels cause different responses in the railway vehicle, and that a Kalman filter can estimate wheelset forces well, to give an indication of the adhesion level. The work is successfully applied to a Simulink half vehicle model.

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

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.

Model predictive control with constraints for a nonlinear adaptive cruise control vehicle model in transition manoeuvres

A vehicle following control law, based on the model predictive control method, to perform transition manoeuvres (TMs) for a nonlinear adaptive cruise control (ACC) vehicle is presented in this paper. The TM controller ultimately establishes a steady-state following distance behind a preceding vehicle to avoid collision, keeping account of acceleration limits, safe distance, and state constraints. The vehicle dynamics model is for continuous-time domain and captures the real dynamics of the sub-vehicle models for steady-state and transient operations. The ACC vehicle can execute the TM successfully and achieves a steady-state in the presence of complex dynamics within the constraint boundaries.

Wheel-rail profile estimation

The dynamic response of a railway vehicle is dependent upon the interaction between the wheel and rail. Currently these key components are monitored for wear and condition off-line and separately. However, it is the combined nonlinear interaction that really affects the dynamics. This paper presents the initial Rail Research UK (RRUK) sponsored feasibility study into estimating the wheel-rail profiles in real-time under normal running. A Kalman filter approach is used to estimate the relevant system parameters, and it is applied to a simplified wheelset model that includes the nonlinear geometries as a nonlinear conicity function. This approach is successfully applied in a simulation study to recreate the nonlinear conicity function as the wheelset moves laterally over the rail.

Detection of low adhesion in the railway vehicle wheel/rail interface: assessment of multi-bodied simulation data

Low adhesion in the wheel/rail interface of railway vehicles creates safety and punctuality issues in terms of missed station stops and signals passed at danger. RSSB project T959 is tasked with developing advanced monitoring techniques for the detection of adhesion in this key interface. A number of techniques were developed and initially tested on simplified models of a rail vehicle. The efficacy of these techniques is now being tested with more representative data produced by multi-bodied physics simulation package Vampire. This paper therefore covers the outcomes of the Vampire testing, initial application of a Kalman-Bucy filter creep force estimator to the Vampire data, and application of a data comparison method based upon the Sprague and Geers method, also to the Vampire data.

Least Squares Method Applied to Rail Vehicle Contact Condition Monitoring

Abstract The dynamics of a railway vehicle are driven by the geometry and conditions at the wheel-rail contact. Typically the condition and shape of the wheel and rail are monitored separately and off-line. The work presented here is part of ongoing research into on-line model-based estimation of parameters in the wheel-rail contact dynamics. This paper outlines a practical approach to estimating a nonlinear function within a dynamic system by using a piecewise cubic functions. The parameters for the cubic functions are estimated with a least squared approach applied to the dynamic measurements taken from the system. A simplified plan-view wheelset and suspended mass model is introduced to use as an application of this technique. A contact geometry term, conicity, which is a nonlinear function of the relative lateral wheel-rail position, is included in the rail vehicle model. The conicity is successfully estimated using the least-squares method outlined in the paper.