A modified Kik–Piotrowski (MKP) model is proposed in this paper for an accurate and robust calculation of wheel–rail normal contact problem. The presented method is able to consider the relationship between the elastic deformation of a line and the normal pressure distribution within the contact patch. A novel shape correction method is put forward to correctly describe the elastic deformation of the contact patch. Taking the results estimated by Kalker’s variational method and Kik–Piotrowski method as references, the proposed method is validated by three contact cases, including the assumed standardised non-Hertzian contact and the two-point contact, as well as the contact behaviours based on three actual wheel–rail profiles. The simulation results indicate that, compared with Kik–Piotrowski method, the proposed MKP method achieves better agreement with Kalker’s variational method. Moreover, the MKP method can avoid the abrupt change of wheel–rail normal force due to the sudden transfer of the contact point, which contributes to a better computational stability.

The selection of a wheel profile is a topic of great interest as it can affect running performances and wheel wear, which needs to be determined based on the actual operational line. Most existing studies, however, aim to improve running performances or reduce contact forces/wear/rolling contact fatigue (RCF) on curves with ideal radii, with little attention to the track layout parameters, including curves, superelevation, gauge, and cant, etc. In contrast, with the expansion of urbanization, as well as some unique geographic or economic reasons, more and more railway vehicles shuttle on fixed lines. For these vehicles, the traditional wheel profile designing method may not be the optimal choice. In this sense, this paper presents a novel wheel profile designing method, which combines FaSrtip, wheel material loss function developed by University of Sheffield (USFD function), and Kriging surrogate model (KSM), to reduce wheel wear for these vehicles that primarily operate on fixed lines, for which an Sgnss wagon running on the German Blankenburg–Rübeland railway line is introduced as a case. Besides, regarding the influence of vehicle suspension characteristics on wheel wear, most of the studies have studied the lateral stiffness, longitudinal stiffness, and yaw damper characteristics of suspension systems, since these parameters have an obvious influence on wheel wear. However, there is currently little research on the relationship between the vertical suspension characteristics and wheel wear. Therefore, it is also investigated in this paper, and a suggestion for the arrangement of the vertical primary spring stiffness of the Y25 bogie is given.

The rail fastener plays an important role in supporting the rail and isolating the train-track system vibrations. Its damage detection is therefore an inevitable part in railway maintenance to ensure the system performance. Recently, deep learning techniques are widely applied to structural health monitoring, including damage detection of the rail fastener based on visions. However, the vision-based detection is mainly limited to visible damages. To address this limitation, this work presents a vibration-based detection method by introducing the fully convolutional network (FCN) to identify invisible damages of fasteners. Firstly, three damage categories are defined and five damage degrees are equivalently represented by modifying the stiffness and damping coefficients of target fasteners. Then, a vehicle-track vertically coupled dynamics model with variable vehicle speeds is established to obtain axle box accelerations (ABAs) under excitations of fastener damage and track irregularity. Finally, a fastener damage detection network is designed based on the FCN architecture to predict damage degrees by inputting the ABA, the track irregularity and the vehicle speed simultaneously. The detection performance is estimated and the network robustness to noise is analysed. The results show that the proposed method is capable of achieving accurate, real-time and robust identification of the fastener damage.

A new method is proposed to efficiently simulate vehicle-track-subgrade vertical dynamic interactions based on the Green function method. In this simulation, the vehicle-track-subgrade system is divided into two subsystems, the vehicle-rail subsystem and the slab-subgrade subsystem, which are coupled through fastener connections. In the vehicle-rail subsystem, the vehicle is modelled as a multi-rigid-body system with 10 degrees of freedom, and the rail is simplified as a Bernoulli-Euler beam supported by rail pads. Dynamic equations of the vehicle-rail subsystem are described by differential equations. In the slab-subgrade subsystem, a three-dimensional finite element model is established to obtain the Green functions of the system induced by a unit impulse excitation. Based on the derived Green functions, dynamic equations of the slab-subgrade subsystem are formulated by the Duhamel integral. The interaction between the two subsystems is transferred by the fastener forces, and the dynamic responses of the whole system are solved through explicit numerical methods. The proposed method is subsequently validated through a detailed numerical comparison with the result obtained by the finite element method. Finally, the method is applied to investigate the dynamic property of the subgrade system under high-speed train operation.