Through-life Monitoring of Resource-constrained Systems and Fleets

TL;DR

This paper proposes a framework for maintaining accurate digital twins of resource-constrained systems through lightweight on-board updates and robust off-board data integration, demonstrated on a gas turbine engine.

Contribution

It introduces a novel framework combining on-board lightweight digital twins with off-board updates for resource-limited systems, addressing a key challenge in digital twin deployment.

Findings

Effective digital representation of a gas turbine engine over time

Lightweight on-board DT enables prioritized data transfer

Robust off-board updates improve anomaly detection

Abstract

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.