In 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 cost-effective maintenance plan at a network level.

One of the main benefits of the railways digital transformation is the possibility of increasing the efficiency of the Asset Management process through the combination of data-driven models and decision support systems, paving the road towards an Intelligent Asset Management System (IAMS). The paper describes the whole IAMS decisional process based on a real railway signaling use case: from field data acquisition to decision support. The process includes data collection, preparation and analytics to extract knowledge on current and future assets’ status. Then, the extracted knowledge is used within the decision support system to prioritize asset management interventions in a fully-automated way, by applying optimization logics and operational constraints.The target is to optimize the scheduling of maintenance activities, to maximize the service reliability and optimize both usage of resources and possession times, avoiding (or minimizing) contractual penalties and delays.In this context, a real use case related to signaling system and, in particular, to track circuits, is presented, applying the proposed methodology to an Italian urban rail network and showing the usefulness of the approach and its possible further developments.

This 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.

The objective of this study is to show the applicability of machine learning and simulative approaches to the development of decision support systems for railway asset management. These techniques are applied within the generic framework developed and tested within the In2Smart project. The framework is composed by different building blocks, in order to show the complete process from data collection and knowledge extraction to the real-world decisions. The application of the framework to two different real-world case studies is described: the first case study deals with strategic earthworks asset management, while the second case study considers the tactical and operational planning of track circuits’ maintenance. Although different methodologies are applied and different planning levels are considered, both the case studies follow the same general framework, demonstrating the generality of the approach. The potentiality of combining machine learning techniques with simulative approaches to replicate real processes is shown, evaluating the key performance indicators employed within the considered asset management process. Finally, the results of the validation are reported as well as the developed human–machine interfaces for output visualization.

The application of artificial intelligence (AI) techniques may lead to significant improvements in different aspects of rail sector. Considering asset management and maintenance, AI can improve data analysis and asset status forecasting and decision-making processes, fostering predictive and prescriptive maintenance strategies. A prescriptive approach should be able to predict future scenarios as well as to suggest a course of actions. Nevertheless, the decision-making in rail asset management is often based on the classical asset-oriented approach, concentrating on the function of the asset itself as a main key performance indicator (KPI), whereas a user-oriented approach could lead to improved performance in terms of level of service. This paper is aimed at integrating the passengers’ perspective in the decision-making process for asset management to mitigate the impact that service interruptions may have on the final users. A data-driven prioritisation framework is developed to prioritise maintenance interventions taking into account asset status and criticality. In particular, a three-step approach is proposed, which focuses on the analysis of passenger data to evaluate the failure impact on the service, the analysis of alarms and anomalies to evaluate the asset status, and the suggestion of maintenance interventions. The proposed approach is applied to the maintenance of the metro line M5 in the Italian city of Milan. Results show the usefulness of the proposed approach to support infrastructure managers and maintenance operators in making decisions regarding the priority of maintenance activities, reducing the risk of critical failures and service interruptions.

This 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.