Contextualisation of information in digital twin processes
Matthew S. Bonney, Marco de Angelis, Mattia Dal Borgo, David Wagg
Abstract
Digital twins are required to process a large amount of data during operation, in order to achieve specific tasks, over the lifetime of the physical twin that they relate to. One important feature of processing data is the identification of trust in both the underlying data and processed information that arises from the data. Trust, as it is defined here, will typically be built from several contributory sources. While there are both quantitative and qualitative sources of trust, this paper focuses on the qualitative aspects of trust via the transparency of the algorithmic process that is available in the crystal-box modelling. The crystal-box idea is also extended to include the concept of a ‘crystal-box workflow’. The key idea is that in order to assist the user of the digital twin to interpret the results they are presented with, via the digital twin interface, the information needs to be contextualised. This work shows an example of how this can be done for a vibration testing (specifically modal testing) example on a scaled three-storey structure. The information is contextualised for the user via ‘profiles’, which collate and augment the processed information together. In particular, synthetic results are generated in order to augment a limited set of physically recorded data, and these synthetic results are then used to assist the user in contextualising the physically recorded data. Implementation results are shown using an open-source digital twin code called DTOP-Cristallo.