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Applied Sciences
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Vehicle-Track Interaction and Railway Dynamics
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Vehicle-Track Interaction and Railway Dynamics

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Transportation and Future Mobility".

Deadline for manuscript submissions: 20 May 2025 | Viewed by 2007

Special Issue Editor

Prof. Dr. Rong Chen

E-Mail Website
Guest Editor
School of Civil Engineering, Southwest Jiaotong University, Chengdu 610031, China
Interests: track engineering and maintenance of high-speed and heavy haul railway; urban rail transit track structure and dynamics

Special Issue Information

Dear Colleagues,

Railways have always played a significant role in the development of societal wealth creation capabilities. With the continuous improvement of railway train speed and the rapid development of high-speed and heavy-duty railways, the dynamic interaction between locomotives and track structures is increasingly strengthened, leading to more serious and complex dynamic and vibration problems. The higher the running speed of the train is, the more prominent the issues of safety and running the stability of locomotive and vehicle operation on the railway are. It is necessary to ensure that the locomotive and vehicle do not overturn or derail during high-speed (fast) operation, and as well as that high-speed trains have good riding comfort. The larger the carrying weight of the vehicle is, the stronger the dynamic interaction between the wheel and rail, and the more severe the dynamic damage of the vehicle to the track structure. This requires reducing the dynamic interaction between heavy-duty trains and the track. Railways have been in a high-load-transportation state for a long time, leading to more prominent vehicle/track interaction problems. This Special Issue is launched in order to showcase the cutting-edge research on the dynamic interaction between railway vehicles and track, and to discuss the difficulties and issues faced in the development of railway dynamics.

Prof. Dr. Rong Chen
Guest Editor

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Keywords

  • passive safety and control of railway trains
  • vibration and noise control of railway systems
  • the interaction between railway train operation and natural environment
  • railway train operation and maintenance/health management and risk prevention and control
  • resilience improvement of the railway system
  • dynamic action of the railway system
  • train dynamics in railway systems
  • railway vehicle/track interaction
  • rail infrastructure operation and maintenance
  • detection and monitoring of the status of railway infrastructure
  • evolution and evaluation of service performance of railway infrastructure
  • maintenance and performance maintenance of railway infrastructure
  • disaster prevention and emergency response of railway infrastructure

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Published Papers (3 papers)

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Research

16 pages, 7952 KiB  
Article
Influence of Aging Conditions on the Dynamic Stiffness of EPDM and EVA Rail Pads
by Isaac Rivas, Jose A. Sainz-Aja, Diego Ferreño, Isidro Carrascal, Jose Casado and Soraya Diego
Appl. Sci. 2025, 15(8), 4394; https://doi.org/10.3390/app15084394 - 16 Apr 2025
Viewed by 245
Abstract
The railway sector plays a crucial role in sustainable transportation by reducing greenhouse gas emissions while supporting an increasing volume of freight and passenger transport. Rail pads, essential components in railway infrastructure, mitigate vibrations and distribute loads; however, their long-term performance is influenced [...] Read more.
The railway sector plays a crucial role in sustainable transportation by reducing greenhouse gas emissions while supporting an increasing volume of freight and passenger transport. Rail pads, essential components in railway infrastructure, mitigate vibrations and distribute loads; however, their long-term performance is influenced by environmental and mechanical degradation, affecting track durability and maintenance costs. Despite their significance, the degradation mechanisms impacting the dynamic stiffness of EPDM (Ethylene Propylene Diene Monomer) and EVA (Ethylene Vinyl Acetate) rail pads remain insufficiently characterized. This study examines the effects of mechanical and chemical aging on the stiffness of these materials through 864 dynamic stiffness tests, analyzing three types of rail pads under mechanical cycling (up to 2,000,000 cycles), UV (ultraviolet light) exposure (100–500 h), and hydrocarbon exposure (100–500 h). Mechanical aging increases stiffness across all pads, with Pad C (EVA) exhibiting the most pronounced increase (27%). The effects of UV exposure vary by material, leading to a stiffness reduction of up to 11.5% in Pad B (EPDM), whereas Pad C (EVA) experiences a 9.5% increase under prolonged exposure. Hydrocarbon exposure also presents material-dependent behavior, with Pad A (EPDM) experiencing an 11.5% stiffness reduction at low exposure but partial recovery at higher exposure, while Pad C (EVA) shows a 5% increase in stiffness under prolonged exposure. These findings offer valuable insights into the aging mechanisms of rail pads and underscore the importance of considering degradation effects in track maintenance strategies. Full article
(This article belongs to the Special Issue Vehicle-Track Interaction and Railway Dynamics)
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18 pages, 3063 KiB  
Article
Slipping Trend Prediction Based on Improved Informer
by Jingchun Huang, Sheng He, Haoxiang Feng and Yongjiang Yu
Appl. Sci. 2025, 15(8), 4112; https://doi.org/10.3390/app15084112 - 9 Apr 2025
Viewed by 239
Abstract
During locomotive operation, large amounts of operation data are recorded by the TCU (Traction Control Unit). The prediction and detection of slipping through the analysis of large amounts of data are of great significance for energy saving and locomotive operation safety. The TCU [...] Read more.
During locomotive operation, large amounts of operation data are recorded by the TCU (Traction Control Unit). The prediction and detection of slipping through the analysis of large amounts of data are of great significance for energy saving and locomotive operation safety. The TCU records time series data with a step length of 1 s. The transformer-based Informer algorithm performs well in time series prediction and analysis. Based on the improved Informer algorithm, this paper proposes a slip trend prediction method, which can predict the slipping state of n time steps according to the data of the previous seconds. By adding the improved prediction model of Informer to the classification model, this study, rather than adding a classification branch to the prediction model, directly improves the output structure, so as to realize long-sequence prediction with a multi-classification model. The model can effectively extract the important features in the data, and can realize multi-axle synchronous prediction and output the slipping state in parallel over the next few seconds. The comprehensive accuracy of this model in multi-axle synchronous prediction tasks can reach 94.75%. Finally, the model is analyzed according to the predicted results, and the effects of different models are compared. The attention mechanism and experimental data are analyzed by visualization. Full article
(This article belongs to the Special Issue Vehicle-Track Interaction and Railway Dynamics)
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19 pages, 7635 KiB  
Article
Research on Fault Prediction Method for Electric Multiple Unit Gearbox Based on Gated Recurrent Unit–Hidden Markov Model
by Cheng Liu, Shengfang Zhang, Ziguang Wang, Fujian Ma and Zhihua Sha
Appl. Sci. 2024, 14(12), 5320; https://doi.org/10.3390/app14125320 - 20 Jun 2024
Cited by 1 | Viewed by 889
Abstract
Due to the limited availability of fault samples and the expensive nature of marking fault samples in Electric Multiple Unit (EMU) gearbox monitoring data, a study was conducted to simulate the degradation process of key components in the CRH5 gearbox using rigid–flexible coupling [...] Read more.
Due to the limited availability of fault samples and the expensive nature of marking fault samples in Electric Multiple Unit (EMU) gearbox monitoring data, a study was conducted to simulate the degradation process of key components in the CRH5 gearbox using rigid–flexible coupling dynamics. Vibration acceleration data from the simulation were utilized to create a six-dimensional hybrid feature domain representing the degradation process. By leveraging the capabilities of the Hidden Markov Model (HMM) for handling hidden transitive probabilities in temporal data and Gated Recurrent Unit (GRU) for addressing long-distance and high-dependence temporal data, a GRU-HMM fault prediction model was developed. This model was validated using monitoring data and the six-dimensional hybrid feature domain from the CRH5 gearbox and compared against actual maintenance records. The findings indicated that the GRU-HMM fault prediction model can effectively recognize the degradation patterns of multiple components, offering higher accuracy in fault prediction compared to traditional models. These research outcomes are expected to optimize EMU maintenance schedules based on usage conditions, enhance EMU utilization rates, and reduce operational and maintenance costs, thereby providing valuable theoretical support. Full article
(This article belongs to the Special Issue Vehicle-Track Interaction and Railway Dynamics)
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