# Supporting the Management of Rolling Stock Maintenance with an Ontology-Based Virtual Depot
## 3 Proposed Approach Overview  
The approach is centred on the development of a model of the data, processes, and activities for Rolling Stock maintenance, it is inspired by the area of enterprise modelling [14]. There has been a great deal of development in this sector on the collection of train operation and condition data; this work builds on that development by integrating the data within a model of the maintenance processes. Particularly in industrial and manufacturing applications, the benefits of this kind of modelling are seen as enhancing the inter-operability and knowledge-sharing of individual processes, presenting opportunities for optimisation of the whole process, and adding AI capabilities (e.g., the potential of adding automated planning) [15]. There has been significant research in developing conceptual models of enterprises in the Industry 4.0 movement [16] in a range of industrial domains. Such developments aim for:  
* Integration: the idea is to integrate the objects and processes of an enterprise so that the effect of different parts of a larger system can be modelled to explore their inter-operating behaviour. In rolling stock maintenance, it is envisaged that remote condition monitoring equipment, maintenance management (scheduled, routine servicing, and condition-based servicing) and maintenance activity can all be integrated based on an over-arching ontology. * Enhanced functionality: in this work, dynamic scheduling of activities is considered in detail, but other functions such as planning what maintenance actions are needed,or (in the longer term) autonomous enactment using robotic devices, are made feasible given the underlying ontological model. Capturing an industrial enterprise this way is analogous to the work in model-based, enterprise information technology architecture, where the components of the architecture are precisely and formally specified. The idea of developing a systems model of enterprises, or specific functions, through ontologies, has been around for decades (e.g., [PERSON]'s paper from 1997 [14]) and has been used for human communication, and to investigate optimisation and impact of change [17; 18]. In this study, an ontology is used to assist the development of automated Rolling Stock maintenance scheduling. In future work, the system model and associated withs could be expanded to fully optimise maintenance operations considering real-time condition monitoring and operations or to assist with the introduction of automation in train maintenance depots. The tool is developed using details of a specific (though anonymised) depot to illustrate the approach. The structure of this paper follows the steps in the approach shown in the quadrants in Figure 1. In the top left is the first step: the development of an abstract ontology capturing the activities (such as maintenance examinations), the objects and object assemblies (such as wheels and vehicles), and their attributes and associations, occurring in the rolling stock maintenance depot (see Section 4). The second step is to populate this abstract ontology with company and/or fleet-specific information so that the ontology can be used in specific processes within a particular depot (see Section 5). Step three involves using a systematic method to detail the process flow within fleet maintenance (see Section 6). This will specify the inputs and outputs of each of the major maintenance activities within the depot, such as scheduling and resource management. The fourth step is to utilise the framework created from the previous steps to plug-in supporting computer processes. In the fourth step, a short-term scheduling process schedules work to be carried out on all units with maintenance due in a three-week horizon. The process's inputs include predicted due dates for all outstanding fleet interventions, and knowledge acquired automatically (within step two) about required resources and tooling for this particular fleet.