1,720,971 research outputs found
Risk-Based Optimal Scheduling for the Predictive Maintenance of Railway Infrastructure
No full textIn this thesis a risk-based decision support system to schedule the predictive maintenance activities, is proposed. The model deals with the maintenance planning of a railway infrastructure in which the due-dates are defined via failure risk analysis.The novelty of the approach consists of the risk concept introduction in railway maintenance scheduling, according to ISO 55000 guidelines, thus implying that the maintenance priorities are based on asset criticality, determined taking into account the relevant failure probability, related to asset degradation conditions, and the consequent damages
A Rolling-Horizon Approach for Predictive Maintenance Planning to Reduce the Risk of Rail Service Disruptions
No full textThis article proposes a model for the risk-based scheduling of predictive maintenance activities on a railway line to intervene when a track segment has reached a certain state of degradation, thus preventing faults and possible failures. With the aim of taking into account the stochastic nature of real environments, the rail-track degradation process is represented as a stochastic process, and the failure probability is evaluated as the probability of reaching a degradation threshold. Moreover, a rolling-horizon framework is introduced to manage newly available real-time information and unpredicted faults or maintenance activity delays. Whereas the traditional scheduling models are offline models that cover the long-term horizon but neglect operational disturbances, the presented model allows for dynamic day-to-day
planning and adaptation of the maintenance plan to real-time information, thereby responding to the increasing understanding of real-world processes. The optimization problem on maintenance scheduling is formulated as a mixed-integer linear programming problem based on risk minimization, in adherence to ISO 55 000 guidelines. Finally, the application of the approach to a real rail network is reported and discussed, with a focus on the planning of tamping activities at the operational level
A multimodal solution approach for mitigating the impact of planned maintenance on metro rail attractiveness
No full textThe possible unavailability of urban rail-based transport services due to planned maintenance activities may have significant consequences on the perceived quality of service, thus affecting railway attractiveness.
To cope with the mitigation of planned service interruptions and to guarantee a seamless journey and a good travel experience for passengers, it is possible to exploit the existing services differently and/or provide additional on-demand services, such as temporary supplemental bus lines.
In this context, this paper aims to develop a mathematical programming model for planning service interruptions due to maintenance considering passenger transport demand dynamics. In particular, the proposed approach deals with service interruptions characterized by a long duration for which timetable adaption strategies are not applicable, suggesting mitigation actions that exploit the already existing services and/or the activation of additional ones, with the aim of minimizing users’ inconvenience. In doing so, the planned infrastructure status (i.e., available or under maintenance), as well as the forecasted transport demand, are taken into account to adapt the service accordingly by offering a multimodal transport solution to passengers.
To find the best solution, a decomposition solution approach is proposed in combination with a multistage cooperative framework with feedback that models the negotiation process between the involved actors.
Finally, the applicability of the proposed approach to real case studies is discussed based on some performance indicators
Stochastic scheduling approach for predictive risk-based railway maintenance
No full textThis paper presents a stochastic model for scheduling predictive and risk-based maintenance activities in rail sector. The novelty of the approach consists of the risk concept introduction in railway maintenance scheduling, thus implying that the maintenance priorities are based on criticality of assets, determined by the relevant failure probability, related to asset degradation conditions, and by the consequent direct and indirect damages. This approach belongs to the framework of 'predictive maintenance' which aims at intervening when an asset has reached a certain degradation state, being the future track conditions forecasted by appropriate models. In particular, this work explicitly considers the stochastic nature of risk and of the real-world maintenance operations, introducing stochastic deadlines. In doing so, it is worth noting that, the adaptive rescheduling models only partially solve this issue, since they consider deterministic sub-problems of the overall problem and they cannot vary continuously the stochastic input variables. Therefore, to cope with this problem, in this paper, the risk-based maintenance planning problem is formulated in term of stochastic programming. After providing a formal methodology description, some experimental results are reported and some indications about its future developments are given
A modular model to schedule predictive railway maintenance operations
No full textThis paper presents a modular model for the optimal railway maintenance scheduling problem. In particular, an innovative approach to predictive railway maintenance scheduling is applied to track maintenance, also taking into account the risk assessment, according to the ISO 55000 guidelines, and the real-time track conditions. The novelty of this approach consists of the introduction of the concept of risk in railway maintenance scheduling, thus implying that the maintenance activity priorities are based on asset criticalities, such as track degradation conditions and repair costs, and the users' unmet demand due to traffic disturbances caused by asset faults. In the paper, after a general framework description, the relevant literature is analyzed. Then, the formal problem description is given, and some experimental results are discussed, together with some indications about the future model developments
Risk-based optimal scheduling of maintenance activities in a railway network
No full textIn a railway system, maintenance activities need to be continuously performed to ensure safety and continued rail operations. In this framework, while on one hand unplanned corrective maintenance activities performed when a fault is occurred are expensive and would cause low service quality, on the other hand preventive maintenance that does not consider the actual asset condition is often unnecessary and turns out to generate avoidable costs. To deal with this issue, in this paper, a risk-based decision support system to schedule the predictive maintenance activities is proposed. In such a framework, the interventions are planned by taking into account the forecast degradation state of railway assets and performed when a given threshold is reached, thus minimizing the probability of both sudden and unnecessary operations. With the end of finding the optimal scheduling of predictive maintenance, in this paper also the space-distributed aspect of railway infrastructure is considered, defining the best path and the activities assignment for each maintenance team. The scheduling model is formulated as a Mixed Integer Linear Programming (MILP) problem aiming at based on the risk minimization, according to the ISO 55000 guidelines. A matheuristic solution approach is proposed and applied to a real rail network. The relevant results show how the proposed scheduling model can use the outputs of predictive tools and degradation models, based on data from field, to mitigate the sudden failure risk by means of a costeffective maintenance plan at a network level
Multimodal, sustainable and resilient solutions for mobility and transport – SIDT 2022
Get PDFTransport policy is a multidisciplinary field where engineering, economics, sociology and law must come together in well-articulated and effective solutions. Despite being a field of effective intervention, most scientific publications address transport policy with a theoretical and often abstract approach, making its understanding difficult for non-senior academics and even more opaque for practitioners. While the merits of case study methods both for undergraduate and graduate teaching are recognised, academics struggle to find empirical material that provides objective and operational illustration of the theories and approaches lectured. This is a major barrier not only in the teaching context but also for practitioners.
Case Studies on Transport Policy covers this gap by providing a repository of relevant material to support teaching and transferability of experiences. Observation of field experience highlighting the details and drawbacks of implementation is invaluable to show how Transport Policy can be applied in the operational field, maintaining consistency with strategic options. Teaching with case studies introduces students to challenges they may face in the real world, and provides a very rich learning method for executive training at every institutional level. For practitioners, and specially governments, case studies are a powerful tool to show the potential benefits from policy measures and packages.
Case Studies on Transport Policy and its sister journal Transport Policy provide a valuable reference for the specialised study of transport policy offering in-depth theoretical analysis and detailed case study description and analysis, and in this way providing very complete material for decision makers planners and practitioners to undertake transferability of experiences
Towards an intelligent and automated platform for railway Asset Management
No full textThis paper presents the objectives and the main expected results from IN2SMART Project, funded by the SHIFT2RAIL Joint Undertaking and the European Commission, within the SHIFT2RAIL Research Programme. This project contributes to the development of an intelligent and automated platform for Asset Management decision-making, focused on the planning of predictive, condition and risk-based Asset Management activities. Based on a framework for Asset Management aligned with international standards, the platform receives inputs from tools and models for predictive analytics that are able to extract information on current and future asset condition, using heterogeneous data from the field. In particular, nowcasting and forecasting methodologies, diagnostics and anomaly detection techniques and indicators derived from Risk, RAMS and LCC analysis are used to support decision-making. Finally, real-world business cases are presented to show the expected applicability of the proposed automated platform and the usefulness of the relevant methodology
A Bayesian Network approach for the reliability analysis of complex railway systems
No full textRailway system is a typical large-scale complex system with interconnected sub-systems, each containing several components. In this framework, cost-effective asset management and innovative smart maintenance strategies require an accurate estimation of the reliability at different levels, according to the system configuration. Moreover, in order to apply risk-based maintenance approaches, techniques for the evaluation of assets criticality, that take into account the causal-effect relation between system components, are necessary. This paper presents a Bayesian Network modeling approach for the reliability evaluation of a complex rail system, which is applied to a real world case study consisting of a railway signaling system, with the aim of showing the usefulness of the approach in achieving a good understanding of the behavior of such a complex system
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