Research on Railway System Reliability and Its Development Trends

Ning Xu; Kexin Zhang; Pei Liu; Mingming Wang

doi:doi:10.11648/j.sd.20251303.13

Research Article |

| Peer-Reviewed

Research on Railway System Reliability and Its Development Trends

Ning Xu, Kexin Zhang, Pei Liu^*, Mingming Wang

Published in Science Discovery (Volume 13, Issue 3)

Received: 21 April 2025 Accepted: 11 June 2025 Published: 22 June 2025

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Abstract

With the continuous advancement in the intelligence of railway systems and the increasing complexity of their service environments, traditional reliability assessment methods face significant challenges in dynamic risk modeling, the systematic integration of intelligent technologies (big-data predictive maintenance, AI fault diagnosis), and cross-subsystem collaborative analysis. This study aims to systematically organize reliability assessment indicator systems and proposes multi-dimensional construction principles encompassing probability-based (reliability), time-based (MTBF, MTTR), and ratio-based (failure rate) metrics. Through an in-depth comparison of the technical characteristics and applicability of Fault Tree Analysis (FTA), Reliability Block Diagram (RBD), and the Markov method, combined with research practices on key subsystems such as the traction power supply system (failure probability modeling considering harmonics and load characteristics), track infrastructure (operational safety reliability assessment based on track irregularity data), and communication and signaling systems (reliability comparison of TACS and CBTC architectures), the significant limitations of existing methods are revealed. These limitations include difficulties in handling dynamic fault logic, analyzing multi-state coupling characteristics, and capturing cascading failure effects.Future development trends necessitate the integration of Dynamic Bayesian Networks (DBNs) (to enable automatic fault propagation inference) and digital twin simulation technology (to construct multi-scenario validation platforms). This integration will establish a data-driven (integrating real-time monitoring, historical fault databases, and operation and maintenance logs) full lifecycle assessment framework. Furthermore, exploring the application of Model-Based Systems Engineering (MBSE) and blockchain traceability technology for embedding reliability requirements and enabling closed-loop management is crucial. By constructing a reliability analysis system characterized by dynamic adaptability, environmental robustness, and engineering practicality, this research provides solid theoretical support and engineering assurance for intelligent railway systems to transition their risk prevention and control mode from passive maintenance to proactive prevention.

Published in	Science Discovery (Volume 13, Issue 3)
DOI	10.11648/j.sd.20251303.13
Page(s)	51-55
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2025. Published by Science Publishing Group

Keywords

Railway Systems, Reliability Assessment, Fault Tree, Block Diagram Method, Markov

1 引言

铁路作为复杂大系统，子系统的可靠性直接决定运营安全与效率。依据GB/T 21562标准，铁路系统可靠性内涵可细化为实现安全高效运输的能力，当前研究主要聚焦于量化评估指标与方法体系的构建。以故障率、平均无故障时间（MTBF）及修复时间（MTTR）等指标量化设备性能；采用故障树分析（FTA）、失效模式与影响分析（FMEA）等传统模型结合EN 50126标准的RAMS全生命周期管理框架形成可靠性多维评估方法。但现有方法存在显著局限包括动态风险因素（如环境突变、网络攻击）的建模能力不足；大数据预测性维护、AI故障诊断等智能技术未形成系统性应用；缺乏跨子系统协同评估机制，难以捕捉级联失效效应。未来的发展趋势是构建融合动态仿真与智能监测的评估体系，推动数字孪生、区块链等新兴技术与标准的深度融合，实现铁路系统从被动维修向主动预防的转型。

2．系统可靠性指标

2.1．可靠性指标分类与定义

系统可靠性指标是衡量产品在规定时间内、规定条件下完成规定功能能力的量化标准。根据计量方式不同，可靠性指标可分为三类: ①概率类指标以可靠度为核心，定义为系统在规定时间内无故障运行的概率。②时间类指标即失效前的平均工作时间（MTTF）、MTBF和MTTR，MTBF反映系统连续无故障运行能力，MTTR衡量故障修复效率。③比率类指标如故障率，表示单位时间内系统发生故障的频率。

表1 系统可靠性指标分类定义公式表。

指标类别	定义	计算公式/示例
可靠度（概率类）	系统在规定时间内的无故障概率	R(t)=e⁻^λt
MTBF（时间类）	平均故障间隔时间	MTBF=总运行时间/故障次数
故障发生率（比率类）	单位时间内故障发生的频率	F(t)=总故障次数/（设备总数×运行时间）

2.2．可靠性指标的多维度特性与确定原则

可靠性指标的多维度特性与确定原则需以系统性视角构建评估体系，涵盖MTBF、MTTR、可靠度等核心参数，因此其确定需要多维度综合考量。环境与工况方面，铁路系统需考虑振动、温湿度、电磁干扰等外部条件对设备可靠性的影响，例如轨道电路在极端温湿度下的信号衰减特性需纳入指标阈值设计；设计要求需贯彻冗余架构（如联锁系统的二乘二取二结构）、故障导向安全原则（如信号降级为红灯的强制措施）等核心准则，通过分项系数法量化安全完整性等级（SIL4）要求；寿命周期维度强调全生命周期的动态调整机制，例如立项阶段通过CMMI模型定义过程域成熟度，运维阶段基于BIM技术实现质检数据协同化跟踪，下线阶段评估残余风险对指标的长期影响。

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Figure 1. 图1 系统生命周期表。

2.3．铁路系统可靠性指标的构建方法

铁路系统可靠性指标的构建需遵循科学性、可量化原则，具体步骤包括: 系统分解，基于功能模块（如信号控制、制动系统）提取关键指标；专家论证，通过德尔菲法或故障树分析筛选代表性指标；量化验证，结合历史数据（如故障率统计）和仿真测试验证指标有效性。

表2 铁路系统指标示例表。

子系统	关键指标	目标值
机车设备	配属机车设备故障率	＜0.15件/百万公里
信号控制系统	平均无故障时间（MTBF）	≥10^5小时
联锁系统	系统可用度	≥99.99%

3．系统可靠性评估方法

系统可靠性评估是根据系统的结构和行为过程，推测系统可靠性特性及其发展规律。从系统角度出发，研究系统可靠性的评估方法主要包括故障树分析法、可靠性框图法、马尔可夫（Markov）分析法、蒙特卡罗（Monte-Carlo）仿真方法、GO法和Petri网方法等。系统可靠性评估的目的旨在达成针对系统的量化评估要求，即在数值上合理确定并给出系统的可靠性特征量。

3.1．故障树分析法

故障树分析是一种基于布尔代数与概率论的自顶向下系统可靠性分析方法，通过树状逻辑模型将系统故障（顶事件）逐层分解为基本事件（底事件），结合定性与定量分析识别系统薄弱环节。^[2-3]其理论框架需基于系统结构、功能及故障机制的深入理解，分析过程包含定性分析即通过最小割集识别导致顶事件的关键事件组合，计算结构重要度确定核心影响因素，揭示系统失效的潜在路径；定量分析，即基于基本事件发生概率，结合蒙特卡罗法计算顶事件发生概率及系统可靠性指标（如失效概率、平均故障间隔时间），为风险评估提供量化依据，具体分析环节如图2。

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Figure 2. 图2 故障树分析过程。

近年研究聚焦多方法协同，结合模糊理论处理多态系统不确定性，如集成贝叶斯网络实现双向推理具备故障溯源能力；计算机辅助建树技术突破传统人工局限，提升复杂系统分析效率，通过图形化界面与自动化算法提升大规模系统分析的可行性。

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3.2．可靠性框图法

可靠性框图法（RBD）通过图形化逻辑关系描述系统各组件故障对整体功能的影响，并基于数学表达式量化系统可靠性。典型模型包括串联、并联、表决、旁联及网络系统，其标准化应用由GB/T 37981-2019提供指导。

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在工程领域，RBD被广泛应用于系统可靠性建模与评估。例如，机械装置通过组件可靠性指标预测系统整体可靠性，而机电系统则结合层次分析法构建多层次模型，通过权重系数反映单元对系统的影响差异。

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随着基于模型的系统工程（MBSE）发展，RBD与MBSE的集成进一步提升了可靠性分析的精准性和效率，通过模型化需求传递优化了安全性与可靠性设计。该方法凭借直观的逻辑表达和量化优势，成为多领域系统可靠性评估的重要工具。

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3.3．马尔可夫分析法

马尔可夫分析法是一种通过系统状态转移关系建模解决可靠性与独立性问题的数学工具，其核心在于将动态过程转化为状态转移概率模型，适用于含维修能力及多重降级状态的复杂系统评估。其分析步骤如表3

表3 Markov分析法实施步骤。

步骤	关键内容
1.状态空间定义	确定系统所有可能状态（如N单元并联系统存在N+1种状态，涵盖全正常运行至完全故障）
2.转移矩阵构建	建立状态转移概率矩阵，量化各状态间转换规律（含维修能力、多重降级状态）
3.方程求解与概率分布	基于马尔可夫链无记忆性建立微分方程组，求解特定时刻状态概率分布
4.可靠性指标计算	综合稳态概率计算可靠度、可用度，推导平均故障间隔时间等特征量

该方法在多个领域得到广泛应用，如在铁路系统中用于接触网可靠性建模，推导出稳态有效度与可靠度的计算公式以优化维护策略。其数学建模能力为工程可靠性设计与决策提供量化支撑，实现复杂系统状态演变的精确描述。

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4．铁路系统可靠性评估研究现状

铁路可靠性技术发展始于英国60年代机车性能评估，日本70年代拓展至新干线设备研究并引入威布尔分析法测算检修周期。我国起步较晚，当前聚焦子系统稳定性提升等基础研究，用于提高运输系统的稳定性和减少因技术故障等。

4.1．牵引供电可靠性研究

牵引供电系统可靠性评估已从传统单设备分析转向全生命周期多因素融合研究。例如基于高速铁路负荷的冲击性、非线性及不平衡性特点，采用Arrhenius-Weibull模型计算牵引变压器失效概率，结合谐波损耗因子及热路模型量化谐波影响，结果显示计及谐波时失效概率的95%概率值和平均值均高于未计及情况。

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可靠性建模方式有通过构建SQL数据库与B/S架构软件系统，集成牵引供电设备运行数据与可靠性指标算法，基于数据搜集与评价平台，实现与生产调度系统的信息融合。该模型应用于京沪高铁的评估显示，2015年其牵引供电单元可靠系数均值达99.99837%。研究证实谐波、负荷波动及设备状态是多因素评估的关键参数，而数据驱动与智能化分析技术可有效提升评估精度与工程适用性。

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4.2．轨道线路可靠性研究

轨道不平顺的状态控制分析是保证高速铁路行车安全性的核心问题之一。如通过实测轨道不平顺数据以及蒙特卡洛法进行实例分析与验证，采用响应面与轮轨动力学相结合的方法可以有效地实现行车安全性状态的可靠性评估，且现有的轨道不平顺状态可以保证列车的安全运行。

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为满足铁路轨道不平顺实时监测、时‐频特征精细化提取和线路状态评估的需要，还可基于维格纳‐威尔分布WVD时‐频能量密度、轨道不平顺功率谱密度和TQI指标的数学物理关系，联合EMD方法的时‐频分解特性分析方法在轨道状态表征、病害诊断、特征波长提取与定位等方面的有效性。

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4.3．通信信号可靠性研究

我国铁路部门规定的产品可靠性标准主要有MTBF和MTTF，目前通号系统研究普遍采用FTA和RBD评估系统可靠性。例如，针对车-车通信（TACS）和车-地通信（CBTC）系统，通过构建故障导致运行延误的可靠性模型，量化对比两种架构的可靠性差异，发现TACS在故障恢复效率上更具优势。

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同时引入信号微机监测系统、扩展频谱时域反射法（SSTDR）等技术，可实现电缆网络故障的在线检测与预警，显著提升故障响应速度。

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4.4．机车车辆可靠性研究

铁路移动装备的寿命问题是可靠性工程中的一个重要领域，可靠性设计的主要内容有对产品组成部分进行可靠性分配，做到零部件可靠性同步匹配，避免出现薄弱环节；利用FMECA及FTA技术寻找产品薄弱环节，采取补救措施。针对机车车辆及其主要零件，目前采用可靠性分析技术有FMEA及其风险分析、三维有限元分析方法、疲劳裂纹的萌生、增长机理分析和疲劳寿命估计与预测、建模分析，包括各种任务模型、三维非线性结构分析、组合软件体系和自适应解算策略等。

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此外，我国铁路部门不断加大移动装备和基础设施可靠性工程方面的投入，各项技术创新不断推进，通过进行长期综合试验对机车车辆及其主要零部件的性能、结构强度及可靠性进行科学研究，发现并解决问题，从而延长铁路移动装备和基础设施的可靠性和寿命。铁路综合试验主要的项目有可靠性试验、型式试验、部件应用试验等，以2015—2017年的大西综合试验为例，可靠性分析研究贯穿于该次试验多维度验证体系中。大西试验通过系统性测试中国标准动车组的综合性能，验证了养护维修技术的有效性，历时710天的持续试验（含1.6万列次列车运行）积累了海量数据，为轮轨接触、振动噪声等关键技术的可靠性建模提供了实测支撑。

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通过跨专业协同机制，铁路综合试验建立了我国高铁自主化装备的可靠性评估范式。

5．存在的问题及发展趋势分析

5.1．可靠性评估的问题

随着铁路系统集成化、智能化发展，其可靠性分析面临动态性、多态性、时序相关性及环境复杂性的多重挑战。现有方法多聚焦子系统评估（如转向架、牵引供电等），缺乏基于大系统视角的整体可靠性表征机制，难以揭示风险机理及环境适应性规律。动态故障逻辑分析依赖人工推理，存在覆盖度不足、准确性低的问题，且传统模型如马尔可夫链难以处理多状态耦合特性。亟需构建融合动态故障树、多态流网络及数据驱动模型的系统级可靠性评估体系，结合全生命周期风险量化与实时监测数据，实现复杂环境下系统可靠性的精准建模与主动调控。

5.2．未来发展趋势

铁路可靠性分析未来发展趋势需要构建多维度协同的可靠性评估体系，具体发展创新方向如通过数据驱动的动态可靠性建模，通过整合实时监测数据、历史故障记录和运维日志，构建基于机器学习的可靠性预测模型。采用故障树分析与马尔可夫链相结合的方法，建立动态贝叶斯网络模型，实现故障传播路径的自动推理；建立仿真驱动的多场景验证技术，基于数字孪生技术构建虚实映射的仿真平台，涵盖极端天气、高寒、高海拔及设备老化等各类风险场景。通过故障注入模拟和蒙特卡洛抽样，量化评估冗余设计、维修策略等；建立模型驱动的全生命周期评估，构建MBSE综合模型，将可靠性需求嵌入系统设计规范、接口控制文件等，通过自动生成故障模式库和可靠性框图，结合区块链溯源技术建立覆盖设计、监测、预警的全生命周期评估生态，实现从需求分析到退役处置的全过程闭环管理。

6．结论

当前铁路系统可靠性评估面临的核心挑战在于传统静态模型难以捕捉动态故障时序与多态耦合效应，复杂服役环境下风险的量化能力薄弱，以及子系统割裂导致级联失效规律难以揭示。未来需以数据驱动为核心，整合实时监测数据与历史故障库，构建机器学习驱动的可靠性预测模型，并结合故障树-Markov混合建模实现故障传播路径的自动推理；基于数字孪生技术搭建多场景仿真平台，通过极端环境模拟与蒙特卡洛故障注入量化冗余设计及维修策略有效性；通过MBSE框架将可靠性需求嵌入系统设计规范，构建全生命周期模型。通过多技术融合与系统级优化，最终形成兼具动态适应性、环境鲁棒性与工程实用性的可靠性分析体系，为铁路系统的主动预防性维护提供理论支撑与工程保障。

基金项目

中国铁道科学研究院集团有限公司基金/ China Academy of Railway Sciences Co., Ltd Foundation （2023YJ252）

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Xu, N., Zhang, K., Liu, P., Wang, M. (2025). Research on Railway System Reliability and Its Development Trends. Science Discovery, 13(3), 51-55. https://doi.org/10.11648/j.sd.20251303.13

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ACS Style

Xu, N.; Zhang, K.; Liu, P.; Wang, M. Research on Railway System Reliability and Its Development Trends. Sci. Discov. 2025, 13(3), 51-55. doi: 10.11648/j.sd.20251303.13

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AMA Style

Xu N, Zhang K, Liu P, Wang M. Research on Railway System Reliability and Its Development Trends. Sci Discov. 2025;13(3):51-55. doi: 10.11648/j.sd.20251303.13

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@article{10.11648/j.sd.20251303.13,
  author = {Ning Xu and Kexin Zhang and Pei Liu and Mingming Wang},
  title = {Research on Railway System Reliability and Its Development Trends
},
  journal = {Science Discovery},
  volume = {13},
  number = {3},
  pages = {51-55},
  doi = {10.11648/j.sd.20251303.13},
  url = {https://doi.org/10.11648/j.sd.20251303.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.sd.20251303.13},
  abstract = {With the continuous advancement in the intelligence of railway systems and the increasing complexity of their service environments, traditional reliability assessment methods face significant challenges in dynamic risk modeling, the systematic integration of intelligent technologies (big-data predictive maintenance, AI fault diagnosis), and cross-subsystem collaborative analysis. This study aims to systematically organize reliability assessment indicator systems and proposes multi-dimensional construction principles encompassing probability-based (reliability), time-based (MTBF, MTTR), and ratio-based (failure rate) metrics. Through an in-depth comparison of the technical characteristics and applicability of Fault Tree Analysis (FTA), Reliability Block Diagram (RBD), and the Markov method, combined with research practices on key subsystems such as the traction power supply system (failure probability modeling considering harmonics and load characteristics), track infrastructure (operational safety reliability assessment based on track irregularity data), and communication and signaling systems (reliability comparison of TACS and CBTC architectures), the significant limitations of existing methods are revealed. These limitations include difficulties in handling dynamic fault logic, analyzing multi-state coupling characteristics, and capturing cascading failure effects.Future development trends necessitate the integration of Dynamic Bayesian Networks (DBNs) (to enable automatic fault propagation inference) and digital twin simulation technology (to construct multi-scenario validation platforms). This integration will establish a data-driven (integrating real-time monitoring, historical fault databases, and operation and maintenance logs) full lifecycle assessment framework. Furthermore, exploring the application of Model-Based Systems Engineering (MBSE) and blockchain traceability technology for embedding reliability requirements and enabling closed-loop management is crucial. By constructing a reliability analysis system characterized by dynamic adaptability, environmental robustness, and engineering practicality, this research provides solid theoretical support and engineering assurance for intelligent railway systems to transition their risk prevention and control mode from passive maintenance to proactive prevention.
},
 year = {2025}
}

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TY  - JOUR
T1  - Research on Railway System Reliability and Its Development Trends

AU  - Ning Xu
AU  - Kexin Zhang
AU  - Pei Liu
AU  - Mingming Wang
Y1  - 2025/06/22
PY  - 2025
N1  - https://doi.org/10.11648/j.sd.20251303.13
DO  - 10.11648/j.sd.20251303.13
T2  - Science Discovery
JF  - Science Discovery
JO  - Science Discovery
SP  - 51
EP  - 55
PB  - Science Publishing Group
SN  - 2331-0650
UR  - https://doi.org/10.11648/j.sd.20251303.13
AB  - With the continuous advancement in the intelligence of railway systems and the increasing complexity of their service environments, traditional reliability assessment methods face significant challenges in dynamic risk modeling, the systematic integration of intelligent technologies (big-data predictive maintenance, AI fault diagnosis), and cross-subsystem collaborative analysis. This study aims to systematically organize reliability assessment indicator systems and proposes multi-dimensional construction principles encompassing probability-based (reliability), time-based (MTBF, MTTR), and ratio-based (failure rate) metrics. Through an in-depth comparison of the technical characteristics and applicability of Fault Tree Analysis (FTA), Reliability Block Diagram (RBD), and the Markov method, combined with research practices on key subsystems such as the traction power supply system (failure probability modeling considering harmonics and load characteristics), track infrastructure (operational safety reliability assessment based on track irregularity data), and communication and signaling systems (reliability comparison of TACS and CBTC architectures), the significant limitations of existing methods are revealed. These limitations include difficulties in handling dynamic fault logic, analyzing multi-state coupling characteristics, and capturing cascading failure effects.Future development trends necessitate the integration of Dynamic Bayesian Networks (DBNs) (to enable automatic fault propagation inference) and digital twin simulation technology (to construct multi-scenario validation platforms). This integration will establish a data-driven (integrating real-time monitoring, historical fault databases, and operation and maintenance logs) full lifecycle assessment framework. Furthermore, exploring the application of Model-Based Systems Engineering (MBSE) and blockchain traceability technology for embedding reliability requirements and enabling closed-loop management is crucial. By constructing a reliability analysis system characterized by dynamic adaptability, environmental robustness, and engineering practicality, this research provides solid theoretical support and engineering assurance for intelligent railway systems to transition their risk prevention and control mode from passive maintenance to proactive prevention.

VL  - 13
IS  - 3
ER  -

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Author Information

Ning Xu

Railway Science & Technology Research & Development Center, China Academy of Railway Sciences Corporation Limited

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Kexin Zhang

Railway Science & Technology Research & Development Center, China Academy of Railway Sciences Corporation Limited

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Pei Liu

Railway Science & Technology Research & Development Center, China Academy of Railway Sciences Corporation Limited

Contact Email
Mingming Wang

Railway Science & Technology Research & Development Center, China Academy of Railway Sciences Corporation Limited

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Figure 1

Figure 1. 图 1 系统生命周期表。
Figure 2

Figure 2. 图 2 故障树分析过程。

Table 1

表1 系统可靠性指标分类定义公式表。
Table 2

表2 铁路系统指标示例表。
Table 3

表3 Markov分析法实施步骤。

Plain Text BibTeX RIS

APA Style

Xu, N., Zhang, K., Liu, P., Wang, M. (2025). Research on Railway System Reliability and Its Development Trends. Science Discovery, 13(3), 51-55. https://doi.org/10.11648/j.sd.20251303.13

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ACS Style

Xu, N.; Zhang, K.; Liu, P.; Wang, M. Research on Railway System Reliability and Its Development Trends. Sci. Discov. 2025, 13(3), 51-55. doi: 10.11648/j.sd.20251303.13

Copy | Download

AMA Style

Xu N, Zhang K, Liu P, Wang M. Research on Railway System Reliability and Its Development Trends. Sci Discov. 2025;13(3):51-55. doi: 10.11648/j.sd.20251303.13

Copy | Download

@article{10.11648/j.sd.20251303.13,
  author = {Ning Xu and Kexin Zhang and Pei Liu and Mingming Wang},
  title = {Research on Railway System Reliability and Its Development Trends
},
  journal = {Science Discovery},
  volume = {13},
  number = {3},
  pages = {51-55},
  doi = {10.11648/j.sd.20251303.13},
  url = {https://doi.org/10.11648/j.sd.20251303.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.sd.20251303.13},
  abstract = {With the continuous advancement in the intelligence of railway systems and the increasing complexity of their service environments, traditional reliability assessment methods face significant challenges in dynamic risk modeling, the systematic integration of intelligent technologies (big-data predictive maintenance, AI fault diagnosis), and cross-subsystem collaborative analysis. This study aims to systematically organize reliability assessment indicator systems and proposes multi-dimensional construction principles encompassing probability-based (reliability), time-based (MTBF, MTTR), and ratio-based (failure rate) metrics. Through an in-depth comparison of the technical characteristics and applicability of Fault Tree Analysis (FTA), Reliability Block Diagram (RBD), and the Markov method, combined with research practices on key subsystems such as the traction power supply system (failure probability modeling considering harmonics and load characteristics), track infrastructure (operational safety reliability assessment based on track irregularity data), and communication and signaling systems (reliability comparison of TACS and CBTC architectures), the significant limitations of existing methods are revealed. These limitations include difficulties in handling dynamic fault logic, analyzing multi-state coupling characteristics, and capturing cascading failure effects.Future development trends necessitate the integration of Dynamic Bayesian Networks (DBNs) (to enable automatic fault propagation inference) and digital twin simulation technology (to construct multi-scenario validation platforms). This integration will establish a data-driven (integrating real-time monitoring, historical fault databases, and operation and maintenance logs) full lifecycle assessment framework. Furthermore, exploring the application of Model-Based Systems Engineering (MBSE) and blockchain traceability technology for embedding reliability requirements and enabling closed-loop management is crucial. By constructing a reliability analysis system characterized by dynamic adaptability, environmental robustness, and engineering practicality, this research provides solid theoretical support and engineering assurance for intelligent railway systems to transition their risk prevention and control mode from passive maintenance to proactive prevention.
},
 year = {2025}
}

Copy | Download

TY  - JOUR
T1  - Research on Railway System Reliability and Its Development Trends

AU  - Ning Xu
AU  - Kexin Zhang
AU  - Pei Liu
AU  - Mingming Wang
Y1  - 2025/06/22
PY  - 2025
N1  - https://doi.org/10.11648/j.sd.20251303.13
DO  - 10.11648/j.sd.20251303.13
T2  - Science Discovery
JF  - Science Discovery
JO  - Science Discovery
SP  - 51
EP  - 55
PB  - Science Publishing Group
SN  - 2331-0650
UR  - https://doi.org/10.11648/j.sd.20251303.13
AB  - With the continuous advancement in the intelligence of railway systems and the increasing complexity of their service environments, traditional reliability assessment methods face significant challenges in dynamic risk modeling, the systematic integration of intelligent technologies (big-data predictive maintenance, AI fault diagnosis), and cross-subsystem collaborative analysis. This study aims to systematically organize reliability assessment indicator systems and proposes multi-dimensional construction principles encompassing probability-based (reliability), time-based (MTBF, MTTR), and ratio-based (failure rate) metrics. Through an in-depth comparison of the technical characteristics and applicability of Fault Tree Analysis (FTA), Reliability Block Diagram (RBD), and the Markov method, combined with research practices on key subsystems such as the traction power supply system (failure probability modeling considering harmonics and load characteristics), track infrastructure (operational safety reliability assessment based on track irregularity data), and communication and signaling systems (reliability comparison of TACS and CBTC architectures), the significant limitations of existing methods are revealed. These limitations include difficulties in handling dynamic fault logic, analyzing multi-state coupling characteristics, and capturing cascading failure effects.Future development trends necessitate the integration of Dynamic Bayesian Networks (DBNs) (to enable automatic fault propagation inference) and digital twin simulation technology (to construct multi-scenario validation platforms). This integration will establish a data-driven (integrating real-time monitoring, historical fault databases, and operation and maintenance logs) full lifecycle assessment framework. Furthermore, exploring the application of Model-Based Systems Engineering (MBSE) and blockchain traceability technology for embedding reliability requirements and enabling closed-loop management is crucial. By constructing a reliability analysis system characterized by dynamic adaptability, environmental robustness, and engineering practicality, this research provides solid theoretical support and engineering assurance for intelligent railway systems to transition their risk prevention and control mode from passive maintenance to proactive prevention.

VL  - 13
IS  - 3
ER  -

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