ArXiv Preprint
A Digital Twin (DT) is a simulation of a physical system that provides
information to make decisions that add economic, social or commercial value.
The behaviour of a physical system changes over time, a DT must therefore be
continually updated with data from the physical systems to reflect its changing
behaviour. For resource-constrained systems, updating a DT is non-trivial
because of challenges such as on-board learning and the off-board data
transfer. This paper presents a framework for updating data-driven DTs of
resource-constrained systems geared towards system health monitoring. The
proposed solution consists of: (1) an on-board system running a light-weight DT
allowing the prioritisation and parsimonious transfer of data generated by the
physical system; and (2) off-board robust updating of the DT and detection of
anomalous behaviours. Two case studies are considered using a production gas
turbine engine system to demonstrate the digital representation accuracy for
real-world, time-varying physical systems.
Felipe Montana, Adam Hartwell, Will Jacobs, Visakan Kadirkamanathan, Andrew R Mills, Tom Clark
2023-01-03
