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.

中文翻译：

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数据驱动的优先级框架，可减轻地铁线路运营期间维护对乘客的影响

人工智能（AI）技术的应用可能会给铁路行业的各个方面带来显着改善。考虑到资产管理和维护，人工智能可以改进数据分析和资产状态预测和决策流程，促进预测性和规范性维护策略。规范性方法应该能够预测未来情景并提出一系列行动建议。然而，铁路资产管理的决策通常基于经典的资产导向方法，集中于资产本身的功能作为主要关键绩效指标（KPI），而以用户为导向的方法可以带来绩效的提高在服务水平方面。本文旨在将乘客的观点纳入资产管理的决策过程中，以减轻服务中断可能对最终用户造成的影响。开发了数据驱动的优先级框架，以考虑资产状态和重要性来确定维护干预措施的优先级。特别提出了三步法，重点是分析乘客数据以评估故障对服务的影响，分析警报和异常以评估资产状态，以及提出维护干预建议。所提出的方法适用于意大利米兰市地铁 M5 线的维护。结果表明，所提出的方法有助于支持基础设施管理者和维护操作员做出有关维护活动优先级的决策，从而降低严重故障和服务中断的风险。