Jul 2026· European Transport Research Review· Vol 18· 0 citations· 28 references
TL;DR
A refined stochastic model-based evaluation framework is presented to aid in comparing various IPS redundant architectures with respect to dependability characteristics, mainly focusing on reliability and availability metrics.
Abstract
Rail transport is increasingly preferred for its safety and reliability, which can be achieved through a variety of advanced design solutions and technological supports. Among them, ensuring a continuous power supply is crucial for the reliable execution of energy-dependent critical operations. Inspired by the Italian railway infrastructure, here the focus is on Integrated Power Supply (IPS) systems that support critical functionalities, such as the signaling system that regulates railway traffic movement. Fault-tolerant IPS designs are implemented to address unforeseen fault events at the IPS level, which could lead to safety or availability issues. This paper presents a refined stochastic model-based evaluation framework to aid in comparing various IPS redundant architectures with respect to dependability characteristics, mainly focusing on reliability and availability metrics. Major contributions pertain to i) the modeling of four IPS architectures; ii) the integration of models to analyze the IPS behavior under normal and critical conditions; iii) the consideration and modeling of battery failures; iv) a comprehensive analysis campaign to compare the considered IPS architectures in terms of both dependability and cost. A designer can effectively utilize the findings from this analysis to determine the most appropriate IPS organization that meets dependability criteria while also considering potential energy consumption savings. The analyses conducted under realistic parameter settings demonstrate that architectures featuring redundancy at the level of individual IPS components yield the best performance in terms of both dependability and cost. For instance, under specific parameter settings, the CR and IR architectures outperform the SR and MR architectures by roughly three orders of magnitude in terms of Mean Time Between Failures (MTBF), achieving MTBF values of up to approximately \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$6\times 10^7$$\end{document} h.
The data center is a critical infrastructure that demands high reliability of power supply. Electrical system failures, including those caused by human error such as the incident in July 2024, can result in significant financial and reputational losses. This study aims to analyze and compare three redundancy configurations of data center electrical systems—N, N+1, and 2N—in order to enhance the operational reliability of the JTT Pasiranji Data Center. The research method was conducted through planning studies, dual-bus electrical system design analysis, and validation via a series of staged commissioning tests (CX 1 to CX 5), which included insulation testing, UPS and ATS switching time testing, generator performance testing, and failover tests under three failure scenarios. The results show that the 2N configuration provides an availability level of 99.982%, equivalent to the Uptime Institute Tier III standard, significantly surpassing N+1 (99.741%) and N (99.671%). Switching time tests confirmed that the double-conversion online UPS operates without switching gap (0 ms), ATS performs automatic transfer in an average of 8.2–8.5 seconds, and generators reach full load readiness within 11.3–11.8 seconds. Failover tests across all three failure scenarios verified that the 2N configuration maintained 100% server uptime without service interruption. This study also found that system reliability is not solely determined by technical design superiority, but is also influenced by human error, adherence to commissioning procedures, real-time monitoring, and preventive maintenance.
Edwin Prima Handi, Randy Rahmanto· International Journal of Hea...· 0 citations
This paper proposes a novel level crossing (LC) control architecture designed for railway systems operating under ERTMS Levels 2 and 3. The approach aims to prevent identified hazardous scenarios and to facilitate the integration of LCs into the ERTMS/ETCS framework by moving from passive control schemes to a supervised, communication-based paradigm. The proposed strategy specifically addresses two critical issues: excessive level crossing closure time (ELCCT) and overly short opening durations (OSOD), both of which may lead to unsafe road user behavior. A formal behavioral model based on Time Petri Nets (TPN) is developed using a modular approach to represent the interactions between LC control and railway traffic. The model is enriched with observers to express safety requirements as temporal logic properties, which are verified using model-checking techniques with the TINA tool. The results show that the proposed architecture effectively prevents the considered risky scenarios.
Mohamed Ghazel· International Conference on...· 0 citations
This document proposes a methodology for enhancing the scheduling of maintenance activities for railway infrastructure using deterioration and optimisation models. The approach combines a deterioration model with a semi-Markov model through the degradation variable (Weibull distribution). The objective is to determine the optimal risk level that triggers maintenance interventions to achieve the most significant economic benefits. The creation of digital twins for both assets and infrastructures facilitates the use of real-time operational data and enables the implementation of analytical models to support maintenance decision-making. Organisations identify and collect relevant asset data, convert this data into model inputs and subsequently use the model outputs to evaluate and select the most advantageous maintenance strategies. This methodology advances current railway decision-support systems for preventive maintenance, transitioning from a traditional table-based approach (subjectively defined risk levels) to an objective method that identifies the risk level which triggers the maintenance intervention based on economic factors. The model can be applied to a wide range of railway assets and is designed to be integrated into a digital twin system. A case study demonstrates its application to the management of track sections. The paper highlights the critical role of data-driven approaches in achieving economically efficient maintenance strategies, while also acknowledging that other key factors in the railway sector, such as safety and transport efficiency, complement these benefits.
Alfonso Carballo-Menayo, A. Sánchez-Herguedas, A. Márquez· Proceedings of the Instituti...· 0 citations
The paper investigates the critical problem of ensuring cyber resilience within the integrated ecosystems of automotive transport and energy infrastructure, which are becoming increasingly interdependent due to the mass adoption of electric vehicles and Smart Grid technologies. The study provides a comprehensive analysis of the threat landscape, focusing on specific attack vectors targeting electric vehicle charging stations (EVCS) and the communication protocols of the Vehicle-to-Grid (V2G) interface. It is demonstrated that vulnerabilities in the ISO/IEC 15118 and OCPP protocols can be exploited to initiate cascading failures that transcend the boundaries of the transport network and impact the stability of the regional power grid. The central contribution of this research is the development of a formalized mathematical model for multi-layer risk assessment, which utilizes a probabilistic approach to quantify the impact of cyber-physical attacks on system availability and data integrity. Unlike existing one-dimensional models, the proposed methodology accounts for the interconnectedness of nodes, where a security breach in a single vehicle or charging point acts as a catalyst for large-scale energy imbalances. The paper details a systematic risk minimization framework that integrates proactive and reactive measures: from the deployment of specialized intrusion detection systems (IDS) optimized for industrial control protocols to the implementation of adaptive load management algorithms that mitigate the effects of malicious demand-side manipulation. Simulation results presented in the study confirm that the proposed model effectively identifies high-risk convergence points with a sensitivity improvement of 15-20% compared to traditional NIST-based frameworks. The research findings provide a theoretical and practical basis for government agencies and critical infrastructure operators to develop robust cybersecurity strategies in the era of total digitalization of transport and energy assets. The developed models contribute to the creation of autonomous defense mechanisms capable of maintaining operational continuity under adversarial conditions.
B. Pokhodenko· Information Technologies and...· 0 citations
The railway track is analyzed as a complex technical system, the management of which is based on regulatory approaches that limit the predictive control of critical sections. A set of system analysis methods has been applied: structural decomposition, functional and structural analysis, classification and multi-criteria analysis. The hierarchical interrelation-ships of the elements of the upper and lower structures have been revealed, and the cascading nature of degradation has been established, where the malfunction of the underlying element acts as the primary factor in the destruction of the overlying ones. The division of components into critical (providing instant safety) and fundamental (determining long-term stability) is justified. A multilevel life cycle model is proposed, which establishes a discrete scale of states correlating with the probability of failure. The concept of a hybrid digital twin integrating stationary sensor networks and data from mobile diagnostic complexes has been developed.
D. A. Pyatkov· Вестник Ростовского государс...· 0 citations
The reliability of traction systems in urban rail transit vehicles is critical to safe and efficient operations. However, existing reliability assessment methods face challenges such as state space explosion, computational complexity, and difficulties in modeling fault interdependencies and imperfect maintenance. This paper proposes an integrated framework that combines dynamic fault trees with fault dependency models and Monte Carlo simulation. The method captures fault dependencies through functional dependency gates incorporating fault impact factors, models component degradation using a two-parameter Weibull distribution, and accounts for imperfect maintenance through a age reduction factor. The simulation efficiency of three random number generators—LCG, MT, and PCG64—was compared. A Monte Carlo simulation of 100,000 trials under maintenance-free conditions yielded a system MTBF of 29.9 months. A mode significance analysis identified the pantograph control unit and the traction control unit as the most critical components. Under targeted maintenance based on component criticality, the MTBF increased to 39.77 months—a 33% improvement—while the peak failure rate was maintained at approximately 3%. The MT generator exhibited the fastest convergence, achieving stability within 1% after 3000 iterations. This framework provides a practical foundation for optimizing preventive maintenance strategies for urban rail transit systems, and sensitivity analysis confirmed the robustness of weak link identification to parameter variations. This method is applicable to other complex systems with interdependent failures and multiple maintenance schedules.