Applications of Ontologies for Assembling Simulation Models of Industrial Systems

Simulation models are important parts of industrial system analysis and control system design. Despite the wide range of possible usage, their formalization, integration and design have not been satisfactorily solved. This paper contributes to the design phase of simulation models for large-scale industrial systems, consisting of a large number of heterogeneous components. Nowadays, it is the simulation expert who assembles the simulation model for a particular industrial plant manually, which is time-consuming and error-prone. We propose to use a semantic engine performing SPARQL queries, which assembles the structure of a simulation model semi-automatically, using formalized knowledge about real industrial plant and available simulation blocks represented in appropriate ontologies. As each real plant device can be represented by more than one simulation blocks, the selection of suitable simulation candidates is based on matching interfaces of neighboring blocks. Evaluation on a real-life industrial use-case shows improvements in reducing development time and avoiding design errors. Major results of this paper are the proposed structures of the ontologies and the SPARQL query realizing the selection of the appropriate simulation blocks.