Data-driven invariant modelling patterns for digital twin design

The Digital Twin (DT) is one of the most promising technologies in the digital transformation market. A digital twin is a virtual copy of a physical system that emulates its behaviour to predict failures and opportunities for change, prescribe actions in real-time, and optimise and/or mitigate unexpected events. Modelling the virtual copy of a physical system is a rather complex task and requires the availability of a large amount of information and a set of accurate models that adequately represent the reality to model. At present, the modelling depends on the specific use case. Hence, the need to design a modelling solution suitable for virtual reality modelling in the context of a digital twin. The paper proposes a new approach to design a DT by endeavouring the concept of modelling patterns and their invariance property. Modelling patterns are here thought of as data-driven, as they can be derived autonomously from data using a specific approach devised to reach an invariance feature, to allow these to be used (and re-used) in modelling situations and/or problems with any given degree of similarity. The potentialities of invariance modelling patterns are proved here by the grace of a real industrial application, where a dedicated DT has been built using the approach proposed here.

Automated summary

The Digital Twin (DT) is a virtual copy of a physical system that predicts failures and opportunities for change, prescribes actions in real-time, and optimizes unexpected events. However, modeling the virtual copy is complex and requires accurate models. This paper proposes a new approach that uses modeling patterns and their invariance property to design a DT. The potential of invariance modeling patterns is demonstrated through a real industrial application.