Wear of rails in turnouts is a common problem during the operation of high-speed railways. It can seriously affect the running safety of trains and the service lives of wheels and turnout rails. In this study, a numerical prediction model for rail wear development in high-speed railway turnouts was established. According to the material wear theory developed by Archard, the wear depth distribution in the wheel–rail contact patch was calculated based on a vehicle–turnout coupling dynamics simulation and wheel–rail rolling contact analysis. For the dynamics model, various components of the vehicle and complex nonlinear interactions between the components were simulated in detail to guarantee consistency with reality. The combination relationship of the switch and stock rails and the irregular and variable cross-sections of the rails in the switch panel of the turnout were considered. Spatial interpolation was used to achieve three-dimensional transitions between adjacent irregular cross-sections to model the compromised rails in the turnout. In addition, the stiffness and damping characteristics of the track in the turnout zone were taken into account. The rail wear rates for every characteristic section of the switch panel were calculated by the superposition model for rail profile wear. An adaptive-step algorithm was adopted in the iterative computations to update the rail profiles for every characteristic section position, which could reduce the cumulative errors and effectively improve the stability and reliability of the numerical model. Finally, case studies were conducted to investigate the wear developments of the switch and stock rails of high-speed turnouts using the developed model. In addition, the rail wear status of turnouts in the Shanghai–Nanjing high-speed railway was measured. The numerical prediction results are consistent with those of the actual situations in the field, verifying the rationality of the established model. This work shows the potential for guiding the maintenance and optimal design of turnouts and improving the understanding of the formation mechanism and influencing factors of rail wear in turnouts.

The problem of excessive wear of pantograph strips frequently occurs on China’s Z City Metro Line 1. After on-site investigation and analysis by the metro operating company, it was speculated that the problem was related to abnormal track irregularities. Therefore, taking Z City Metro Line 1 as the main research object, the measured track irregularity of the whole line was analyzed and compared with other typical track spectra, and a track-vehicle-pantograph-catenary coupling dynamics model was established to analyze the relationship between the pantograph-catenary dynamic characteristics and the track irregularity. The two frequency ranges of the track irregularity that have a significant impact on the pantograph-catenary contact were found. Finally, after numerical calculation and analysis, it is recommended to focus on the irregularities with the two wavelength ranges of 9~16 m and 3~4 m in the maintenance.

In this paper, a numerical prediction model was established to investigate the development of rail wear on high-speed railways, and a corresponding program was written using Matlab. According to Archard’s material wear theory, the wear depth distribution in the wheel–rail contact patch and along the rail profile was calculated based on a simulation of vehicle–track dynamics and a wheel–rail rolling contact analysis. In the dynamics model, various structural components and the complex nonlinear interactions between components were precisely simulated to ensure consistency with reality. Simulations were then conducted for every possible load case, and dimensionless weight factors were introduced to model the diverse operating conditions of a high-speed railway. An adaptive step algorithm was adopted to iteratively update the rail profile and reduce cumulative deviation or errors, improving the stability and reliability of the numerical model. Finally, a case study was conducted to investigate the development of wear in different track sections on a high-speed railway using the developed model. The results indicated that in the circular curve and transition sections, the side wear of the outer rail was obvious, and the wear of the inner rail was relatively smaller and mostly distributed in the middle of the railhead. The wear of the outer rail was more severe in the circular curve section compared to that in the transition sections. The closer to the rail shoulder, the greater the difference between the wear in the circular curve section and that in the transition section. In the tangent section, the wear of both rails was similarly distributed in the middle of the railhead and far less severe than in either the circular curve or transition sections. The agreement between the calculated results and field observations verified the rationality of the established rail wear model, which shows promise for improving the maintenance planning of high-speed railways and furthering the understanding of the rail wear processes.

Railway turnout is a critical railway infrastructure that guides trains in switching tracks. Over time, uneven rail wear can lead to switch rail reduction value (SRRV) deviation, a typical structural defect that compromises turnout functionality and jeopardizes train operation safety. Current SRRV deviation detection methods rely primarily on inefficient manual inspections, making it difficult to ensure operational safety. To address this issue, the study carried out a comprehensive investigation combining numerical and experimental analyses. First, a rigid–flexible coupled dynamics model of a vehicle-turnout system was developed to analyze the wheel–rail dynamic interaction forces and contact relationships under various SRRV deviation conditions. The results revealed that SRRV deviation significantly affects both wheel–rail interaction forces and the turnout structural irregularity wavelength. Thus, based on discrete wavelet transform (DWT), a wheel–rail force trend component was derived that can effectively analyze the turnout structural irregular wavelength, and the mapping relationship between SRRV deviation and the wheel–rail force trend component was then established. Finally, an efficient and accurate method for identifying SRRV deviation based on wheel–rail force trend component was proposed and validated using field-measured data from trains passing through turnouts. This study contributes to the timely detection of track defects, helping to prevent safety incidents during train operations.