Abstract.A modern railway system relies on sophisticated monitoring systems for maintenance and renewal activities. Some of the existing conditions monitoring techniques perform fault detection using advanced filtering, system identification and signal analysis methods. These theoretical approaches do not require complex mathematical models of the system and can overcome potential difficulties associated with nonlinearities and parameter variations in the system. Practical applications of condition monitoring tools use sensors which are mounted either on the track or rolling stock. For instance, monitoring wheelset dynamics could be done through the use of track-mounted sensors, while vehicle-based sensors are preferred for monitoring the train infrastructure. This paper attempts to collate and critically appraise the modern techniques used for condition monitoring of railway vehicle dynamics by analysing the advantages and shortcomings of these methods.

This paper is an attempt to collate and critically appraise the recent advances in control strategies used to solve challenges related to railway vehicles which present nonlinearities and uncertainties. These strategies concentrate on stability of solid axle-wheelsets, guidance for wheelsets to provide the function of track following and curving to reduce all unnecessary creep forces and associated wear/noise. The focus is on active primary and secondary suspensions, braking and traction subsystems. This paper examines potential new and efficient applications of modern predictive control methods, analysis tools and techniques which could be used in effective and reliable condition monitoring systems allowing informed decision making on maintenance and renewals activities.