Recent increases in railway patronage worldwide have created pressure on rolling stock and railway infrastructure through the demand to improve the capacity and punctuality of the whole system, and this demand must also be balanced with reducing life-cycle costs. Condition monitoring is seen as a significant contributor in achieving this. The emphasis of this article is on the use of sensors mounted on rolling stock to monitor the condition of infrastructure and the rolling stock itself. This is set in the context of modern rolling stock being fitted with high-capacity communication buses and multiple sensors, resulting in the potential for advanced processing of collected data. This article brings together linked research that uses a similar set of rolling stock sensors, and discusses: general usage and benefits, a track defect detection method, running gear condition monitoring, and absolute train speed detection.

Railway switch and crossing (S&C) systems have a very complex structure that requires not only a large number of components (such as rails, check rails, switches, crossings, turnout bearers, slide chair, etc.) but also different types of components and technologies (mechanical devices to operate switches, electrical and/or electronic devices for control, etc.). This complexity of railway S&C systems makes them vulnerable to failures and malfunctions that can ultimately cause delays and even fatal accidents. Thus, it is crucial to develop suitable condition monitoring techniques to deal with fault detection and diagnosis (FDD) in railway S&C systems. The main contribution of this paper is to present a comprehensive review of the existing FDD techniques for railway S&C systems. The aim is to overview the state of the art in rail S&C and in doing so to provide a platform for researchers, railway operators, and experts to research, develop and adopt the best methods for their applications; thereby helping ensure the rapid evolution of monitoring and fault detection in the railway industry at a time of the increased interest in condition based maintenance and the use of high-speed trains on the rail network.

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.

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.