Resilient wheel (RW) is gradually promoted for use in China’s subway vehicles recent years, which has been widely used in tram in Europe and China. As measured, the RWs in service on the metro trains have experienced eccentric wear and even polygonization of high order after 30,000 km running. Given that most subway lines have small curve radii, this study assesses the operation safety of vehicle passing through curves with RWs having polygonal wear, as well as their effect on vibration reduction compared to conventional wheels. A numeric simulation model is developed to model the metro vehicle-track dynamic system installed with resilient wheel, where a conventional wheel-rail contact model is modified considering the composite structure of a RW. A RW-rail contact model considering relatively independent motion of the wheel hub and rim is developed, which is integrated into a metro vehicle-track dynamic system model equipped with the RWs. Upon comparing the curving performance of vehicles equipped with conventional wheels, the findings reveal that, in scenarios of surplus-superelevation, the wheel load reduction ratio (WLRR) of conventional wheels demonstrates superior performance. While curving in the case of deficient-superelevation, the WLRR of RWs is lower. Furthermore, the safety performance of the RWs surpasses that of conventional wheels, particularly as the severity of polygonal wear on the wheel increases. These conclusions can be used as a reference for maintenance strategy of the RW applied on metro trains, which is urgently to be built considering the potentially wide promotion of the RW to the subway system.

To further investigate the impact of prestressing on the internal force and deformation of the double-system composite guideway (DSCG), theoretical equations for internal force and deformation were established based on the principle of energy variation. The theoretical results were validated through finite element modelling and elastic analysis methods. The proposed DSCG theoretical model, derived from the energy method, effectively addresses the influence of symmetric and asymmetric loads on the internal force and deformation of the guideway. The findings indicated that the ratios of interface slip, axial force, and vertical deformation under the two loading conditions were 1.16, 1.06, and 1.06, respectively, showing close agreement. The prestressing effect significantly impacted the mechanical behaviour of the guideway, particularly the vertical deformation, with a maximum influence of 4.92%. Moreover, the results of the energy and elastic analysis methods exhibited a high degree of consistency, with the maximum deviation in the calculated internal force and deformation results not exceeding 3%. This research fills a gap in the theoretical study of the DSCG and provides valuable insights for developing multi-system monorail transit systems.

Wheel-rail force identification is one of the most challenging issues in the railway industry, which can provide real-time safety evaluation and fault diagnosis for railway vehciles in operation. A new real-time polygonal wheel-rail force identification method is proposed. Firstly, aiming at the characteristic of high-order polygon feature frequency of wheelset, multi-rigid dynamics model and flexibility-rigid dynamics model are established in SIMPACK to obtain data. Then, the data of rail force and vibration acceleration of vehicle components are normalized, graphically and discretized processed. Finally, the data are input into the designed real-time polygonal wheel-rail force identification network for learning. Simulation data are used for network learning and comparison. The experimental results demonstrate that the vibration acceleration of vehicle components along with the vertical displacement data of primary springs, exhibit optimal performance in the identification of wheel-rail forces when employed as inputs for the network. Interval usage polygonal data of different orders to fine-tuning the network yield the most accurate identification of polygonal wheel-rail forces. For the multi-rigid model, the average absolute error and determination coefficient of vertical force identification are 1039 N and 0.895, and the lateral force is 362 N and 0.833. For the flexibility-rigid model are 1529.2 N and 0.929 in vertical force identification, and 1734.5 N and 0.948 in lateral force identification. Furthermore, the wheel-rail identification can be real-time because the average calculation time is far less than the sampling time. Consequently, the proposed method can provide strong support for the safety evaluation of running railway vehicles based on monitoring data.

The safety features of Electric Multiple Units (EMUs) are intricate and redundant, and the associated data is massive, multi-sourced, heterogeneous, and interdisciplinary. Constructing appropriate safety feature quantities by fully and effectively utilizing this data is a prerequisite for establishing a safety prevention and control network for EMUs. This paper proposes a model that matches risks in the operation and maintenance safety of EMUs with associated unsafe events, utilizing regular expression and pattern-matching technologies. The relationship between these risks and unsafe events is thoroughly analyzed and mined based on unsafe event data analysis. The paper presents a data-driven method for risk assessment that effectively tackles the issue of subjective bias in existing studies that rely on expert evaluations. The method automatically extracts key risk information, such as the likelihood and severity of consequences, identifies high-risk elements, and scientifically measures the safety risks of EMUs.

With the rapid construction of rail transportation infrastructure, vibration issues caused by wheel–rail interactions during train operation have become a significant concern. Excessive vibration can lead to rail defects, such as corrugation, and also cause environmental vibration and noise problems in surrounding buildings. Rail dampers have been proposed to mitigate these issues by reducing rail vibration across multiple frequency bands. This paper presents a comprehensive experimental study on evaluating the effectiveness of rail dampers in reducing vibration and noise, as well as in suppressing the development of rail corrugation. Specifically, numerical simulations were conducted to design the optimal mass distribution of the rail dampers. The designed dampers were then installed on two operating railway sections, and the dynamic characteristics of the rails were measured and analyzed before and after rail damper installation to demonstrate their effectiveness in mitigating train-induced vibration and noise. Additionally, rail corrugation was measured over a period of 463 days, comparing development with and without rail damper installation. The experiments demonstrate the practical effectiveness of rail damper in mitigating rail vibration issues, and these findings can serve as a reference for related research and engineering applications.

Given the demand for real-time operation state simulation technology for integrating significant amounts of renewable energy into the traction power systems (TPSS), and considering the substantial volatility and intermittence of renewable energy (photovoltaic) output, the accuracy and real-time performance of traditional mechanism simulation models are low. This paper proposes a new digital twin (DT) modeling method for the TPSS, driven by a combination of data and mechanism models. Firstly, the mechanism model of the TPSS is established, with the external power supply simplified to a three-phase Thevenin equivalent circuit. The traction substation is replaced by a traction transformer, and the AT substation is replaced by an AT transformer. The traction network is simplified into a four-conductor model of T, F, P, and R, represented by a π-type equivalent circuit. Secondly, based on the measured data of photovoltaic (PV) power, the data are segmented according to its output time characteristics after preprocessing. The data source is derived by considering the form of a controlled current source. The PV data-driven model is established by importing the real-time data source into Simulink and outputting it to the controlled current source. Thirdly, the railway static power conditioner is used to effectively integrate the TPSS mechanism model with the PV data model, completing the coupling modeling of the two. Finally, the system is simulated and verified by modeling the typical working conditions of two power supply arms with heavy loads (8 MW) and one power supply arm with a heavy load (8 MW) and a light load (2 MW). The results show that the system can achieve the average distribution of power according to the external input PV output data and can reduce the traction energy consumption by about 0.5 MW for the two power supply arms. This is of great significance for the simulation and application of the novel TPSS.