Through-life Monitoring of Resource-constrained Systems and Fleets
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 16 of its 57 references that are in the map. First posted on arXiv 3 Jan 2023.
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References · 16
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4 more
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- 2021In-flight Novelty Detection with Convolutional Neural Networkscited by 1
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