Ontologies for prognostics and health management of production systems: overview and research challenges

Prognostics and Health Management (PHM) approaches aim to intervene in the equipment of production systems before faults occur. To properly implement a PHM system, data-centric steps must be taken, including data acquisition and manipulation, detection of machine states, health assessment, prognosis of future failures, and advisory generation. The data generated by different data sources, such as maintenance management systems, equipment manufacturer manuals, design documentation, and process monitoring and control systems, are fundamental for PHM steps. Discovering and using the knowledge embedded in this data is relevant because, for example, data-driven techniques require knowledge, maintenance data often contain tacit knowledge that can facilitate knowledge transfer and collaboration between maintenance personnel with different levels of experience and expertise, and the knowledge related to the same types of systems could be context-dependent. However, the heterogeneity of data sources, the variety of data types, and the possibility of context-dependent data pose challenges in revealing the real value of data and discovering the useful, yet hidden, patterns embedded in maintenance data that can lead to explicit knowledge. Ontologies can effectively contribute to this issue through the organization of data, semantic annotation, integration, and checking of consistency. Several ontologies contributing to the PHM process have been proposed in the scientific literature. However, to the best of our knowledge, no overview of the available ontologies contributing to the PHM steps of production systems is present in the literature. Therefore, this paper aimed to investigate the ontologies and knowledge graphs proposed in the literature for the PHM of production systems. A systematic analysis and mapping of the literature was performed, and the main information was extracted and discussed according to (i) the type and year of the publication, (ii) the ontological and non-ontological resources adopted for designing the ontology/knowledge graph, (iii) the method adopted for implementing the approach, (iv) the type of application, (v) the step(s) of the PHM process on which the article is focused, and (vi) the type of decisions (strategical, tactical, or operational) to which the ontology/knowledge graph is adopted. Subsequently, the conducted analysis led to the definition of a research agenda in the domain, including the following challenges to address: (1) alignment of the ontologies in the maintenance field with respect to top-level ontologies, (2) connection among the different PHM steps at the operational level, (3) major exploitation of the combination of data-driven AI, ontologies, and reasoning for predictive maintenance, and (4) supporting sustainability-related challenges through the connection between the production system, maintenance system, and product.