High-speed Railway最新文献

A knowledge modeling method for high-speed railway emergency faults based on structured logic diagrams and knowledge graphs 基于结构化逻辑图和知识图的高速铁路应急故障知识建模方法

High-speed Railway

Pub Date : 2026-03-01 Epub Date: 2025-11-04 DOI: 10.1016/j.hspr.2025.10.004

Senshen Li , Chun Zhang , Guoyuan Yang , Wei Bai , Shaoxiong Pang , Xiaoshu Wang , Jian Yao , Ning Zhang

Knowledge graphs, which combine structured representation with semantic modeling, have shown great potential in knowledge expression, causal inference, and automated reasoning, and are widely used in fields such as intelligent question answering, decision support, and fault diagnosis. As high-speed train systems become increasingly intelligent and interconnected, fault patterns have grown more complex and dynamic. Knowledge graphs offer a promising solution to support the structured management and real-time reasoning of fault knowledge, addressing key requirements such as interpretability, accuracy, and continuous evolution in intelligent diagnostic systems. However, conventional knowledge graph construction relies heavily on domain expertise and specialized tools, resulting in high entry barriers for non-experts and limiting their practical application in frontline maintenance scenarios. To address this limitation, this paper proposes a fault knowledge modeling approach for high-speed trains that integrates structured logic diagrams with knowledge graphs. The method employs a seven-layer logic structure—comprising fault name, applicable vehicles, diagnostic logic, signal parameters, verification conditions, fault causes, and emergency measures—to transform unstructured knowledge into a visual and hierarchical representation. A semantic mapping mechanism is then used to automatically convert logic diagrams into machine-interpretable knowledge graphs, enabling dynamic reasoning and knowledge reuse. Furthermore, the proposed method establishes a three-layer architecture—logic structuring, knowledge graph transformation, and dynamic inference—to bridge human-expert logic with machine-based reasoning. Experimental validation and system implementation demonstrate that this approach not only improves knowledge interpretability and inference precision but also significantly enhances modeling efficiency and system maintainability. It provides a scalable and adaptable solution for intelligent operation and maintenance platforms in the high-speed rail domain.

知识图将结构化表示与语义建模相结合，在知识表达、因果推理、自动推理等方面显示出巨大的潜力，广泛应用于智能问答、决策支持、故障诊断等领域。随着高速列车系统日益智能化和互联化，故障模式变得更加复杂和动态。知识图提供了一个有前途的解决方案，支持结构化管理和实时推理的故障知识，解决关键的需求，如可解释性，准确性和智能诊断系统的持续发展。然而，传统的知识图谱构建严重依赖于领域专业知识和专业工具，导致非专家的进入门槛很高，限制了他们在一线维护场景中的实际应用。为了解决这一问题，本文提出了一种将结构化逻辑图与知识图相结合的高速列车故障知识建模方法。该方法采用故障名称、适用车辆、诊断逻辑、信号参数、验证条件、故障原因和应急措施等七层逻辑结构，将非结构化知识转化为可视化的分层表示。然后使用语义映射机制将逻辑图自动转换为机器可解释的知识图，实现动态推理和知识重用。此外，该方法建立了逻辑结构、知识图转换和动态推理三层体系结构，将人类专家逻辑与基于机器的推理连接起来。实验验证和系统实现表明，该方法不仅提高了知识的可解释性和推理精度，而且显著提高了建模效率和系统的可维护性。为高铁领域的智能运维平台提供可扩展、适应性强的解决方案。

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引用次数: 0

Deep learning-based method for damage identification and localization of the maglev track stator surface 基于深度学习的磁悬浮轨道定子表面损伤识别与定位方法

High-speed Railway

Pub Date : 2026-03-01 Epub Date: 2025-09-27 DOI: 10.1016/j.hspr.2025.09.007

Shihua Huang, Tiange Wang, Guofeng Zeng

The stator of the maglev track plays a crucial role in the operation of the maglev system. Currently, the efficiency of maglev track inspection is limited by several factors, including the large span of elevated structures, manual visual inspection, short inspection window times, and limited GPS positioning accuracy. To address these issues, this paper proposes a deep learning-based method for detecting and locating stator surface damage. This study establishes a maglev track stator surface image dataset, trains different object detection models, and compares their performance. Ultimately, YOLO and ByteTrack object tracking algorithms were chosen as the basic framework and enhanced to achieve automatic identification of high-speed maglev track stator surface damage images and track and count stator surface localization feature images. By matching the identified damaged images with their corresponding stator segment and beam segment sequence numbers, the location of the damage is pinpointed to the corresponding stator segment, enabling rapid and accurate identification and localization of complex damage to the maglev track stator surface.

磁悬浮轨道定子在磁悬浮系统的运行中起着至关重要的作用。目前，磁悬浮轨道检测的效率受到高架结构跨度大、人工目视检测、检测窗口时间短、GPS定位精度有限等因素的制约。为了解决这些问题，本文提出了一种基于深度学习的定子表面损伤检测和定位方法。本研究建立了磁悬浮轨道定子表面图像数据集，训练了不同的目标检测模型，并对其性能进行了比较。最终选择YOLO和ByteTrack目标跟踪算法作为基本框架，并对其进行增强，实现高速磁悬浮轨道定子表面损伤图像的自动识别和轨道定子表面定位特征图像的自动识别。通过将识别出的损伤图像与其对应的定子段和波束段序列号进行匹配，将损伤位置定位到对应的定子段上，实现了对磁悬浮轨道定子表面复杂损伤的快速准确识别和定位。

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引用次数: 0

A generation-based defect detection system for rail transit infrastructure 基于生成的轨道交通基础设施缺陷检测系统

High-speed Railway

Pub Date : 2026-03-01 Epub Date: 2025-09-25 DOI: 10.1016/j.hspr.2025.09.004

Xinyu Zheng , Lingfeng Zhang , Yuhao Luo , Tiange Wang

The use of Unmanned Aerial Vehicles (UAVs) for defect detection on railway slopes is becoming increasingly widespread due to their ability to capture high-resolution images over large, inaccessible, and topographically complex areas. However, current UAV-based detection methods face several critical limitations, including constrained deployment frequency, limited availability of annotated defect data, and the lack of mature risk assessment frameworks. To address these challenges, this study introduces a novel approach that integrates diffusion models with Large Language Models (LLMs) to generate high-quality synthetic defect images tailored to railway slope scenarios. Furthermore, an improved transformer-based architecture is proposed, incorporating attention mechanisms and LLM-guided diffusion-generated imagery to enhance defect recognition performance under complex environmental conditions. Experimental evaluations conducted on a dataset of 300 field-collected images from high-risk railway slopes demonstrate that the proposed method significantly outperforms existing baselines in terms of precision, recall, and robustness, indicating strong applicability for real-world railway infrastructure monitoring and disaster prevention.

由于无人机能够在大范围、难以接近和地形复杂的区域捕获高分辨率图像，因此在铁路斜坡上使用无人机进行缺陷检测正变得越来越普遍。然而，当前基于无人机的检测方法面临着几个关键的限制，包括受限的部署频率、有限的标注缺陷数据的可用性以及缺乏成熟的风险评估框架。为了应对这些挑战，本研究引入了一种新的方法，将扩散模型与大语言模型（llm）相结合，生成适合铁路斜坡场景的高质量综合缺陷图像。此外，提出了一种改进的基于变压器的结构，将注意力机制和llm引导的扩散生成图像结合起来，以提高复杂环境条件下的缺陷识别性能。对300幅高风险铁路边坡现场采集的图像数据集进行的实验评估表明，该方法在精度、召回率和鲁棒性方面明显优于现有基线，表明该方法在现实世界的铁路基础设施监测和灾害预防方面具有很强的适用性。

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引用次数: 0

Research on intelligent management of air compression refrigeration system in the environmental wind tunnel of high-speed railway trains 高速铁路列车环境风洞空气压缩制冷系统智能管理研究

High-speed Railway

Pub Date : 2026-03-01 Epub Date: 2025-10-14 DOI: 10.1016/j.hspr.2025.09.008

Junjun Zhuang , Meng Liu , Lingfeng Sun , Jun Wang

The environmental wind tunnel of high-speed railway trains serves as a crucial experimental facility for the research and development of high-speed railway technology. The refrigeration system within the wind tunnel is an important subsystem. However, the design of the wind tunnel refrigeration system management program presents significant scientific challenges and limitations. Traditional management approaches in wind tunnel refrigeration systems suffer from prolonged decision-making times and reliance on experiential knowledge, necessitating the need for intelligent transformation. This paper aims to address these issues by exploring existing intelligent management methodologies and defining the concept of a wind tunnel intelligent laboratory along with its primary modules. Furthermore, we propose a water cooler failure prediction model based on the existing equipment model of the wind tunnel’s refrigeration system. This model effectively predicts the Remaining Useful Life (RUL) of the water cooler in the case of fouling failure, contributing to enhanced efficiency, cost reduction, and safety improvements in laboratories.

高速铁路列车环境风洞是高速铁路技术研究与开发的重要实验设施。风洞内制冷系统是一个重要的子系统。然而，风洞制冷系统管理方案的设计提出了重大的科学挑战和局限性。风洞制冷系统的传统管理方法存在决策时间长、依赖经验知识的问题，需要进行智能化改造。本文旨在通过探索现有的智能管理方法和定义风洞智能实验室及其主要模块的概念来解决这些问题。在风洞制冷系统现有设备模型的基础上，提出了水冷机故障预测模型。该模型有效地预测了在结垢故障的情况下，水冷器的剩余使用寿命（RUL），有助于提高效率，降低成本，并提高实验室的安全性。

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引用次数: 0

Digital twin-driven structural damage monitoring via multilevel Lamb wave enhancement and transfer learning 基于多级Lamb波增强和迁移学习的数字双驱动结构损伤监测

High-speed Railway

Pub Date : 2026-03-01 Epub Date: 2025-10-03 DOI: 10.1016/j.hspr.2025.10.001

Yuan Huang, Xinlin Qing

As structural damage patterns and service environments become more complex, digital twin-based structural health monitoring, with its unique advantages, can compensate for the limitations of data-driven methods regarding data dependency and model interpretability. However, it still faces challenges in modeling complexity, simulation accuracy, and discrepancies between real and virtual features. This study proposes a balanced fidelity digital twin for structural damage monitoring based on Lamb wave multilevel feature enhancement and adaptive space interaction. Firstly, multilevel refined features are extracted from few-shot guided wave signals obtained in physical and digital space, and the adversarial synthetic balancing algorithm is proposed for feature enhancement. Additionally, the learning phase of the damage monitoring model based on the feature-mapping convolutional network is driven by virtual samples of readily accessible balanced fidelity in digital space. To reduce the feature distributional difference between the two spaces, an interactive transfer approach is introduced to establish a shared feature digital twin space. Overall, this study provides a feasible technique to enhance the accessibility and generalizability of digital twins for real engineering structures.

随着结构损伤模式和服务环境变得越来越复杂，基于数字孪生的结构健康监测以其独特的优势，可以弥补数据驱动方法在数据依赖性和模型可解释性方面的局限性。然而，它仍然面临着建模复杂性、仿真精度、真实特征与虚拟特征差异等方面的挑战。提出了一种基于Lamb波多层特征增强和自适应空间相互作用的平衡保真数字孪生结构损伤监测方法。首先，从物理空间和数字空间获取的少量导波信号中提取多级精细特征，并提出对抗综合平衡算法对特征进行增强；此外，基于特征映射卷积网络的损伤监测模型的学习阶段由数字空间中易于获取的平衡保真度的虚拟样本驱动。为了减小两个空间之间的特征分布差异，引入交互传递方法，建立共享特征的数字孪生空间。总体而言，本研究提供了一种可行的技术，以提高数字孪生对实际工程结构的可及性和泛化性。

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引用次数: 0

Estimated carrying capacity based on different signal types for Vietnam’s high-speed railway plan 根据越南高速铁路计划的不同信号类型估计的承载能力

High-speed Railway

Pub Date : 2026-03-01 Epub Date: 2025-10-10 DOI: 10.1016/j.hspr.2025.10.002

Thai Nguyen, Hong Le Xuan, Dong Doan Van

Research on high-speed railways is a relatively new yet highly significant field in Vietnam. Among its key components, train control signaling plays a critical role, as it directly affects various interconnected systems, including infrastructure, traction power supply, operational planning, and overall railway safety. This article focuses on evaluating the capacity of the line based on the types of signals suitable for high-speed railways that have been effectively implemented in several European countries and successfully adapted in China. The research and simulation are conducted using MATLAB software, a reliable and widely adopted tool in the scientific community. The findings demonstrate that under normal conditions, the European Railway Traffic Management System/European Train Control System (ERTMS/ETCS) Level 2 signaling can support up to 23.7485 trains/hour/direction. Meanwhile, ERTMS/ETCS Level 3 with full moving block can accommodate up to 30.8735 trains/hour/direction, and ERTMS/ETCS Level 3 with fixed virtual blocks up to 29.4694 trains/hour/direction. In emergency scenarios, ERTMS/ETCS Level 3 with full moving block reduces headway by 33.27 % compared to CTCS Level 3, while ERTMS/ETCS Level 3 with fixed virtual blocks achieves a 28.78 % reduction. Overall, the ERTMS/ETCS Level 3 emerges as a state-of-the-art signaling technology offering high capacity and operational efficiency, and is recommended as a forward-looking solution for future implementation in Vietnam.

在越南，高速铁路的研究是一个相对较新的领域，但意义重大。在其关键组成部分中，列控信号起着至关重要的作用，因为它直接影响到各种互联系统，包括基础设施、牵引供电、运营规划和整体铁路安全。本文的重点是根据适用于高速铁路的信号类型来评估线路的容量，这些信号类型已经在几个欧洲国家有效实施，并在中国成功应用。研究和仿真是使用MATLAB软件进行的，这是科学界广泛采用的可靠工具。研究结果表明，在正常情况下，欧洲铁路交通管理系统/欧洲列车控制系统（ERTMS/ETCS） 2级信令最多可支持23.7485列/小时/方向。同时，全移动模块的ERTMS/ETCS三级列车可达30.8735列/小时/方向，固定虚拟模块的ERTMS/ETCS三级列车可达29.4694列/小时/方向。在紧急情况下，与CTCS 3级相比，具有完整移动块的ERTMS/ETCS 3级可减少车头时距33.27 %，而具有固定虚拟块的ERTMS/ETCS 3级可减少28.78 %。总的来说，ERTMS/ETCS Level 3是一种最先进的信号技术，提供了高容量和操作效率，并被推荐为越南未来实施的前瞻性解决方案。

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引用次数: 0

Analysis of loading characteristics of windshield wiper structure on high-speed train 高速列车风挡雨刷结构载荷特性分析

High-speed Railway

Pub Date : 2026-03-01 Epub Date: 2025-10-06 DOI: 10.1016/j.hspr.2025.09.009

Honglei Yuan, Quanwei Che, Sicong Zhao

This paper studies the structural response of high-speed train wipers under the combined action of complex flow fields and scraping actions. The stress concentration areas are determined through simulation analysis, and the stress and aerodynamic load measurement points are reasonably arranged accordingly. The actual measurement is carried out in combination with the operating conditions of the existing lines. The stress variations and spectral characteristics of the train under different speed levels (80 , 160 , 180 , 200 km/h), tunnel entry and exit, and scraper action conditions were compared and analyzed. The stress amplification factors under tunnel intersection and scraper action were obtained, providing boundary conditions for the design of wipers for high-speed s. The research results show that the maximum stress of the wiper structure obtained through simulation calculation is concentrated at the connection of the wiper arm. Structural stress increases with the rise of speed grade. The stress increases by 1.11 times when the tunnel meets. When the scraper operates, the stress on the scraper arm increases by 4.1–7.6 times. Due to the broadband excitation effect of the aerodynamic load, the spectral energy of the structure is relatively high at the natural frequency, which excites the natural mode of the wiper.

研究了高速列车雨刷在复杂流场和刮擦作用下的结构响应。通过仿真分析确定应力集中区，并合理布置应力测点和气动载荷测点。实际测量是结合现有线路的运行情况进行的。对比分析了列车在不同速度水平（80 、160 、180 、200 km/h）、隧道进出口和刮板作用条件下的应力变化和频谱特征。得到了隧道交叉口和刮板作用下的应力放大系数，为高速公路刮水器的设计提供了边界条件。研究结果表明：仿真计算得到的刮水器结构的最大应力集中在刮水器臂的连接处。结构应力随速度等级的升高而增大。隧道相遇时应力增加1.11倍。刮板运行时，刮板臂上的应力增加4.1-7.6 倍。由于气动载荷的宽带激励效应，结构在固有频率处的谱能相对较高，激发了雨刷器的固有模态。

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引用次数: 0

Study on life prediction method for rail vehicle critical components based on deep learning models and track load spectra 基于深度学习模型和轨道载荷谱的轨道车辆关键部件寿命预测方法研究

High-speed Railway

Pub Date : 2026-03-01 Epub Date: 2025-09-25 DOI: 10.1016/j.hspr.2025.09.006

Haitao Hu, Quanwei Che, Weihua Wang, Xiaojun Wang, Ziming Wang

Deep learning and fatigue life prediction remain focal research areas in rail vehicle engineering. This study addresses the vibration fatigue of wheelset lifting lug in Chengdu Metro Line 1 bogies, aiming to develop a fatigue life prediction method for critical bogie components using deep learning models and measured track load spectra. Extensive field tests on Chengdu Metro Line 1 were conducted to acquire acceleration and stress response data of the wheelset lifting lug, generating training samples for the neural network system. Component stress responses were calculated via time-domain track acceleration and validated against in-situ stress measurements. Results show that neural network-fitted dynamic stress values exhibit excellent consistency with measured data, with errors constrained within 5 %. This study validates the proposed small-sample deep learning approach as an effective and accurate solution for fatigue life prediction of critical bogie components under operational load conditions.

深度学习和疲劳寿命预测一直是轨道车辆工程研究的热点。本研究针对成都地铁1号线转向架轮对吊耳的振动疲劳问题，旨在利用深度学习模型和实测轨道载荷谱建立转向架关键部件的疲劳寿命预测方法。在成都地铁1号线进行了大量的现场试验，获取轮对吊耳的加速度和应力响应数据，为神经网络系统生成训练样本。通过时域轨迹加速度计算构件应力响应，并根据地应力测量结果进行验证。结果表明，神经网络拟合的动态应力值与实测数据具有良好的一致性，误差控制在5 %以内。该研究验证了所提出的小样本深度学习方法是一种有效和准确的解决方案，用于在运行载荷条件下预测转向架关键部件的疲劳寿命。

引用次数: 0

Reactivation of the railway line from Surabaya to Madura: Enhancing regional connectivity and transportation infrastructure 恢复从泗水到马杜拉的铁路线：加强区域连通性和交通基础设施

High-speed Railway

Pub Date : 2025-12-01 Epub Date: 2025-09-20 DOI: 10.1016/j.hspr.2025.09.005

Gunawan Gunawan , Basil David Daniel , Slamet Budi Utomo , Jenny Caroline

Indonesia is facing severe congestion and high accident rates as motor vehicle growth continues to outpace road capacity, underscoring the urgent need for alternative mass transportation. A promising solution is the reactivation of the Surabaya–Madura railway, an abandoned infrastructure with significant potential to enhance regional connectivity and urban mobility. However, academic studies on railway reactivation remain limited, particularly in the Madura context where dependence on road-based transport persists. This research gap highlights the importance of examining reactivation not only as a transportation alternative but also as a catalyst for regional development. This study adopts a qualitative approach through descriptive surveys to evaluate infrastructure conditions, identify feasible routes, and analyze broader spatial implications. Findings reveal that railway reactivation could strengthen multimodal integration, reduce congestion, and support sustainable growth. This study provides the first empirical evidence of the strategic value of the Surabaya–Madura railway within Indonesia’s transport and regional development discourse.

由于机动车辆的增长继续超过道路容量，印度尼西亚正面临严重的拥堵和高事故率，这突出表明迫切需要替代的大众交通工具。一个有希望的解决方案是重新激活泗水-马杜拉铁路，这是一个废弃的基础设施，具有增强区域连通性和城市流动性的巨大潜力。然而，关于铁路复兴的学术研究仍然有限，特别是在仍然依赖公路运输的马杜拉地区。这一研究差距突出了研究再激活的重要性，不仅作为一种交通选择，而且作为区域发展的催化剂。本研究采用定性方法，通过描述性调查来评估基础设施条件，确定可行的路线，并分析更广泛的空间影响。研究结果表明，铁路复兴可以加强多式联运一体化，减少拥堵，并支持可持续增长。本研究为泗水-马杜拉铁路在印尼交通和区域发展话语中的战略价值提供了第一个经验证据。

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引用次数: 0

A method for predicting random vibration response of train-track-bridge system based on GA-BP neural network 基于GA-BP神经网络的列车-轨道-桥梁系统随机振动响应预测方法

High-speed Railway

Pub Date : 2025-12-01 Epub Date: 2025-09-02 DOI: 10.1016/j.hspr.2025.08.006

Jianfeng Mao , Yun Zhang , Li Zheng , Mansoor Khan , Zhiwu Yu

To enhance the efficiency of stochastic vibration analysis for the Train-Track-Bridge (TTB) coupled system, this paper proposes a prediction method based on a Genetic Algorithm-optimized Backpropagation (GA-BP) neural network. First, initial track irregularity samples and random parameter sets of the Vehicle–Bridge System (VBS) are generated using the stochastic harmonic function method. Then, the stochastic dynamic responses corresponding to the sample sets are calculated using a developed stochastic vibration analysis model of the TTB system. The track irregularity data and vehicle–bridge random parameters are used as input variables, while the corresponding stochastic responses serve as output variables for training the BP neural network to construct the prediction model. Subsequently, the Genetic Algorithm (GA) is applied to optimize the BP neural network by considering the randomness in excitation and parameters of the TTB system, improving model accuracy. After optimization, the trained GA-BP model enables rapid and accurate prediction of vehicle–bridge responses. To validate the proposed method, predictions of vehicle–bridge responses under varying train speeds are compared with numerical simulation results. The findings demonstrate that the proposed method offers notable advantages in predicting the stochastic vibration response of high-speed railway TTB coupled systems.

为了提高列车-轨道-桥梁（TTB）耦合系统随机振动分析的效率，提出了一种基于遗传算法优化反向传播（GA-BP）神经网络的随机振动预测方法。首先，利用随机调和函数法生成车桥系统（VBS）的初始轨道不规则性样本和随机参数集；然后，利用所建立的TTB系统随机振动分析模型，计算了样本集对应的随机动力响应。以轨道不平顺度数据和车桥随机参数作为输入变量，相应的随机响应作为输出变量，训练BP神经网络构建预测模型。随后，利用遗传算法（GA）对BP神经网络进行优化，考虑了TTB系统激励和参数的随机性，提高了模型精度。优化后的GA-BP模型能够快速准确地预测车桥响应。为了验证所提出的方法，将不同列车速度下的车桥响应预测结果与数值模拟结果进行了比较。研究结果表明，该方法在预测高速铁路TTB耦合系统随机振动响应方面具有显著优势。

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引用次数: 0