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.

This paper compares different track model formulations for the simulation of dynamic vehicle–track interaction in switches and crossings (S&C, turnouts) in a multi-body simulation (MBS) environment. The investigations are an extension of the S&C simulation Benchmark with the addition of a finite element model of a 60E1-760-1:15 turnout. This model constitutes a common reference from which four different track formulations are derived: co-running, modal superposition, finite element incorporated into the MBS model and finite element coupled to MBS using a co-simulation approach. For the different track models, the difference in modelling technique, results, simulation time, and suitability for different simulation tasks is compared. A good agreement is found between the different track model formulations for wheel–rail contact forces and rail displacements. This study found a better agreement between co-running and structural track models compared to previous studies in the prediction of wheel–rail contact forces. This appears to be due to the increased complexity of co-running track model used in this study together with a tuning of the co-running track model to the reference model in a wider frequency range. For the reader interested to reproduce the results in this paper, the reference track model is available for download.

This paper proposes a three-layered co-running track model for predicting and analysing the dynamic behaviour of railway turnouts during the passage of trains. It is a multi-body dynamic model with space-dependent track parameters. The method is demonstrated by applying a UIC60-760-1:15 turnout (with curve radius 760 m and turnout angle 1:15). Equivalent track properties are introduced, based on the turnout track flexibility, and the rail receptance and impact force results are compared with the results from a finite element (FE) model. The validation results show a good agreement with those of the FE model, but with far less computational expense in terms of power and time. The new model is found to capture the dominant dynamic behaviour of the turnout across all frequencies up to 2000 Hz. The results have drawn that consideration of higher frequencies is important due to the nature of dynamic forces affecting the crossing region and high-speed cases.

Sensors are inevitable components in a machine or in a mechatronic system, to measure physical quantities. Due to the inherent property of a piezoelectric material, it can be used as an actuator to bring in a displacement in the system by applying an actuator voltage or it can be used as a sensor where a force applied on it is translated as voltage. This property called as self-sensing, is one way to reduce the number of sensors needed in an active system. In the present application, piezoelectric actuators are mounted at the bearings of a rotor. The bearing displacement can be determined from the deflection of the piezos. This deflection can be reconstructed from the current and voltage. By feeding the reconstructed deflection to a finite element (FE) model, faults such as unbalance can be detected. The modal expansion theory helps to determine the deflection at any degree of freedom from few measured signals such as the bearing displacements. Moreover, the forces at each node can be calculated and detected for presence of unbalance faults. With the help of least squares minimization, the magnitude and phase of the unbalance can be determined.