# Supporting the Management of Rolling Stock Maintenance with an Ontology-Based Virtual Depot
## 2 Related Work  
There has been little research and development of enterprise-model-type frameworks performed in the railway maintenance sector. Exceptions include [PERSON] et al., who proposed an architecture to automate railway infrastructure maintenance. Three components are defined (degradation estimation, planning and scheduling, and cost analysis), with a fourth component integrating the first three components and providing a graphical user interface [3]. Although restricted to railway infrastructure only, the overall thrust of this work appears very relevant. In contrast to their work, this paper proposes a formal domain conceptualisation of the depot and rolling stock which is called a virtual depot. [PERSON] et al. defined a domain 'Dysfunctional Analysis Ontology' (DAO) of common concepts related to dysfunctional analysis aspects [4]. The proposed DAO aims to provide an interoperable view of safety analysis methods such as the Preliminary Hazard Analysis (PHA), the Failure Modes and Effects Analysis (FMEA), and the Failure Tree Analysis (FTA). The goal-oriented perspective is also considered to establish consistent safety reasoning and help with the safety decision-making process. The proposed ontology is an upper ontology which requires domain knowledge to be instantiated and used. The idea of going from virtualisation to performing dysfunctional analysis is very important but requires more modelling. Our virtualisation in addition to failure data is required to instantiate their proposed ontology to use it in the context of rolling stock maintenance.  
[PERSON] et al. discussed the benefits of using ontologies to assist with the development of a predictive maintenance tool for rolling stock [5]. They used a fragment of possible railway ontology to illustrate the idea. Details on how the ontology will be linked to the scheduling tool are left for future work. [PERSON] et al. proposed a hierarchical architecture for capturing the domain ontology of the train system [6], with high-level ontologies created for each level in the hierarchy. [PERSON] et al. proposed a fleet-wide approach based on ontologies to ease diagnosis activities [7]. Their approach aims to capitalise on fleet knowledge and data to help decision-makers identify the causes of abnormal operations. Even if this approach was proposed for marine fleets, it falls within the aims of this paper. However, the authors have restricted the idea to the diagnostic process, but the ontology might be used for more than that such as maintenance scheduling, as explained in this paper.  
[PERSON] et al. proposed an ontology for maintenance where failure modes and effects criticality analysis can be performed [8]. This ontology is suitable for reliability analysis and might be reused, but no wide use or links to maintenance scheduling or any other services is considered in the actual version of the ontology. [PERSON] et al. proposed an integrated approach combining existing domain ontologies of various interacting systems for manufacturing systems [9; 10]. Although the domain is different, the idea is similar, to some extent, to some of this paper's aims. Likewise, [PERSON] and [PERSON] have proposed an ontology in the context of manufacturing maintenance that they called a digital twin [11]. In the same direction, [PERSON] et al. proposed an ontology for maintenance which looks more advanced with some decision-making support outputs such as actions that should be taken when a failure and/or threshold is reached [12]. [PERSON] et al. added a more advanced action layer, to the maintenance ontology, for maintenance action recommendation [13]. These papers propose interesting ideas but have not yet moved to real applications as presented in this paper. The following section provides an overview of this paper's proposed approach.