Towards Semantically Enhanced Digital Twins

Abstract

Digital twins (DTs) are a powerful mechanism for representing complex industrial assets such as oil platforms as digital models. These models can facilitate temporal analyses and computer simulations of assets. In order to enable this, DTs should be able to capture characteristics of an asset as specified by the manufacturer, its state during the run time, as well as how the asset interacts with other assets in a complex system. We argue that semantic technologies and in particular semantic models or ontologies is promising modelling paradigm for DTs. Semantic models allow to capture complex systems in an intuitive fashion, can be written in standardised ontology languages, and come with a wide range of off-the-shelf systems to design, maintain, query, and navigate semantic models. In this work we report our preliminary results on developing a system that would support semantic-based DTs. In particular, we plan to augment the PI System developed by OSIsoft with ontologies and show how the resulting solution can help in simplifying analytical and machine learning routines for DTs.