# Supporting the Management of Rolling Stock Maintenance with an Ontology-Based Virtual Depot
## 1 Introduction  
The railway industry is under continued pressure to improve the cost efficiency of its operations. The cost of maintaining rail vehicles makes up 40% of the lifecycle cost of those rail vehicles [1]; hence, identifying methods to improve the efficiency and effectiveness of rolling stock maintenance could have a significant benefit to the overall cost of running the railways. Fleets of trains (i.e., Rolling Stock) are typically maintained at one or more maintenance depots. Management of the depot operations can be a complex task, including the movement of trains into and around the depot, management of depot resources (i.e., staff, space, equipment, consumables and spare parts, fixed plant, tools), and integration with train operations. New fleets of trains have a defined maintenance manual, including all necessary tasks to be carried out and the maximum permitted periodicity (in time or mileage) between each task. Regular Preventative Maintenance tasks are grouped together in 'Preventative Maintenance Exams', which are a selection of tasks that can all be typically completed within one shift. This provides a framework for depot planners towork within. The aim of this work is to carry out research into the improvement of the efficiency and effectiveness of rolling stock maintenance to meet the challenges of the near future of rail. To this end, a digital platform to support _smart_ rolling stock maintenance is proposed. Current practices use a mix of manual and often isolated digital processes; the proposed method is to develop integrated automated processes for maintenance scheduling utilising a connected digital backbone or '_Virtual Depot_'. Rolling stock maintenance can be divided into several categories, including inspection (checking the condition of components), servicing (such as cleaning, fueling, or topping up fluids), replacing worn or damaged components, and heavy maintenance or subsystem overhauls. Maintenance can be either 'Corrective', i.e., fixing a fault that has occurred during service, or 'Preventative', i.e., a long-term maintenance system to manage the condition of a system in good working order, replacing components before they reach the end of their life and avoiding the need for Corrective Maintenance. This paper focuses on a system to manage Preventative Maintenance. The vision for the railway of the future is to schedule maintenance interventions based on Predictive Maintenance (i.e., use of degradation models to predict when in future a component or system will reach the end of its useful life, based on a measure of the current condition) [2]; whilst this could significantly reduce the potential for removing components which have not reached the end of their useful life and the risk of components failing in service, it would make planning maintenance operations significantly more complicated as workloads could vary throughout the year. In the future, a 'Virtual Depot' tool will be essential to successfully implement a fully Predictive Maintenance regime, whilst also efficiently utilising depot resources. The 'Virtual Depot' is centred around an ontology which contains a digital model of aspects of the train fleet, depot maintenance facilities, and resources. It forms the 'glue' for the integration of the structures, relationships, and attributes of the main objects (including rolling stock, sensors, depot assets, and resources) in the depot. It connects up computational components of maintenance activities, encodes explicit actionable knowledge about the depot, and provides a route to utilise recent advances in AI tools for smart rolling stock maintenance. In particular, the ontological structure can be populated with company and fleet-specific information using an automated knowledge acquisition process, meaning that the abstract structural ontology can efficiently be applied to a particular depot.