An autonomous system for maintenance scheduling data-rich complex infrastructure: Fusing the railways' condition, planning and cost
An autonomous system for maintenance scheduling data-rich complex infrastructure: Fusing the railways' condition, planning and cost
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DOI：
10.1016/j.trc.2018.02.010
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发表时间：
2018-04-01
影响因子：
8.3
通讯作者：
Emmanouilidis, Christos
中科院分区：
文献类型：
--
作者：
Durazo-Cardenas, Isidro;Starr, Andrew;Emmanouilidis, Christos
国家铁路通常是庞大而复杂的系统。他们的网络基础设施通常包括延长的轨道区段、桥梁、车站和其他支持资产。近年来，铁路也成为一个数据丰富的环境。铁路基础设施资产的寿命很长，但本质上会退化。干预是必要的，但它们可能造成延误、损害和危险。每天，成千上万的离散维护工作根据时间和紧急程度进行调度。服务中断会产生直接的经济影响。维护计划可能是复杂的、昂贵的和不确定的，维护工作的自主调度是必不可少的。提出了一种新的集成化自动作业调度系统的设计策略，从概念的形成到数据的检查，再到信息过渡层接口和决策层。底层架构配置了技术和业务驱动因素的高级融合;调度优化的干预计划，将成本影响和附加值考虑在内。开发了概念验证演示器，以验证系统原理并测试算法功能。它采用仪表板来可视化系统响应并呈现关键信息。分析了真实的轨道事故和检查数据集，以发出降级警报，启动维护任务的自动调度。通过数据分析和作业排序启发式算法和遗传算法实现了最佳调度，同时考虑到综合任务成本建模的具体成本和价值输入。与铁路基础设施专家和利益相关者进行了正式的面部验证。验证器结构被发现符合逻辑组件关系的目的，提供了进一步的研究和商业开发的范围。
National railways are typically large and complex systems. Their network infrastructure usually includes extended track sections, bridges, stations and other supporting assets. In recent years, railways have also become a data-rich environment.Railway infrastructure assets have a very long life, but inherently degrade. Interventions are necessary but they can cause lateness, damage and hazards. Every day, thousands of discrete maintenance jobs are scheduled according to time and urgency. Service disruption has a direct economic impact. Planning for maintenance can be complex, expensive and uncertain.Autonomous scheduling of maintenance jobs is essential. The design strategy of a novel integrated system for automatic job scheduling is presented; from concept formulation to the examination of the data to information transitional level interface, and at the decision making level. The underlying architecture configures high-level fusion of technical and business drivers; scheduling optimized intervention plans that factor-in cost impact and added value.A proof of concept demonstrator was developed to validate the system principle and to test algorithm functionality. It employs a dashboard for visualization of the system response and to present key information. Real track incident and inspection datasets were analyzed to raise degradation alarms that initiate the automatic scheduling of maintenance tasks. Optimum scheduling was realized through data analytics and job sequencing heuristic and genetic algorithms, taking into account specific cost & value inputs from comprehensive task cost modelling. Formal face validation was conducted with railway infrastructure specialists and stakeholders. The demonstrator structure was found fit for purpose with logical component relationships, offering further scope for research and commercial exploitation.