# Supporting the Management of Rolling Stock Maintenance with an Ontology-Based Virtual Depot
## 9 Conclusions  
This paper introduced the idea of a Virtual Depot for rolling stock maintenance, which includes an abstract generic ontology populated with company- and/or fleet-specific information using a knowledge acquisition process. A four-step method was detailed for the creation of the Virtual Depot and illustrated and evaluated through the use of a real, though anonymised depot. Two particular aspects are discussed in detail--knowledge acquisition of fleet-specific information obtained from a manufacturer's Vehicle Maintenance Instruction manuals and the construction of a short-term scheduling process within the Virtual Depot. The integrative aspects of the method are evaluated, demonstrating how the ontological structure and its acquired specific information informs and benefits the scheduling process, in particular with respect to schedule optimisation. Previous work has not considered such a wide view as is captured in the Virtual Depot, such as resources, vehicle assemblies, and maintenance tasks. There also appears to be  
Figure 12: Scheduling without (**left**) vs. with (**right**) resource retrieval process. no existing published research where maintenance scheduling models are connected up within an enterprise-level conceptualisation, nor which has used VMIs within an automated process to populate the ontology with specific company/fleet knowledge. Initial results in Section 7 show that the scheduling optimisation tool has significant potential for improving the efficiency of rolling stock maintenance operations. Whilst results in Section 8 demonstrate the potential for resource-aware scheduling, by using the Virtual Depot model including, the fleet and depot ontology and knowledge extracted from maintenance instructions as part of the scheduling process. The current analysis uses assumed values of earliness and tardiness penalties for the optimisation process; further work is required to refine the earliness and tardiness parameters and link them to real costs and component costs in an overall maintenance optimisation algorithm. The manual short-term scheduling process is tedious, error-prone, and cost-independent. The planning team should consider many enterprise components and integrate inputs manually to produce this schedule. The virtual depot will enable an inter-operable way of data exchange and the short-term scheduling process will produce an optimal schedule based on the up-to-date data exchanged via the virtual depot. The data acquisition and retrieval process combined with the short-term scheduling model enables depot resource requirements forecast to avoid unwanted situations. In the future, the tool can be expanded to consider a broader range of inputs, and the tools developed to automatically extract data into machine-readable Knowledge Graphs from existing documents will help that process. In future work, further case studies will be investigated to improve and extend the Virtual Depot including the optimised scheduling process. Abstract ontology will be developed to include both spatial concepts and developed to assist with the implementation of Predictive Maintenance. A concrete connection with other depot management processes will be developed, such as train movements and spare part management processes. The scheduling process should include maintenance interventions' duration and multiple depot sites/fleets that are not yet covered by the current proposed model. Predictive maintenance using condition monitoring and historical data in addition to uncertainties and reactive (corrective) maintenance should be included to enable overall maintenance optimisation. The extraction of granular details from maintenance instruction manuals requires advanced methods that surpass the capabilities of the currently implemented XML transformations and simple natural language processing tasks.