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 October 2024 | Viewed by 476

Special Issue Editor


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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

Published Papers (1 paper)

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Research

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
Viewed by 340
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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