Automated Reasoning and Knowledge Inference on OPC UA Information Models

Abstract

The fourth industrial revolution demands flexibility, adaptability, transparency and semantic interoperability. Within the German Industry 4.0 initiative, the Reference Architecture Model Industrie 4.0 (RAMI4.0) has recently been standardized and OPC Unified Architecture (OPC UA) is listed as the sole recommendation for implementation of a communication layer. Even though OPC UA helps bridge the interoperability gap at the automation level, its semantic has not yet been formally defined and an efficient automated reasoning and knowledge inference on the OPC UA address space is therefore not yet possible. This paper addresses this issue by presenting a solution to infer knowledge from OPC UA information models. By analyzing and comparing the semantic expressiveness of the OPC UA address space with semantic knowledge representation formalisms such as RDF and OWL, we derived and implemented a solution to transform an OPC UA information model into an RDF-Graph expressing an OWL Ontology. Using the generated ontology, we were able to run a true reasoning task directly on the OPC UA address space. In this paper, the developed approach is conveniently validated on various case studies involving online factory reconfiguration, intelligent energy management, and human-machine interfaces.