Distributed digital twins for health monitoring: resource constrained aero-engine fleet management

Abstract

Abstract Sensed data from high-value engineering systems is being increasingly exploited to optimise their operation and maintenance. In aerospace, returning all measured data to a central repository is prohibitively expensive, often causing useful, high-value data to be discarded. The ability to detect, prioritise and return useful data on asset and in real-time is vital to move toward more sustainable maintenance activities. We present a data-driven solution for on-line detection and prioritisation of anomalous data that is centrally processed and used to update individualised digital twins (DT) distributed onto remote machines. The DT is embodied as a convolutional neural network (CNN) optimised for real-time execution on a resource constrained gas turbine monitoring computer. The CNN generates a state prediction with uncertainty, which is used as a metric to select informative data for transfer to a remote fleet monitoring system. The received data is screened for faults before updating the weights on the CNN, which are synchronised between real and virtual asset. Results show the successful detection of a known in-flight engine fault and the collection of data related to high novelty pre-cursor events that were previously unrecognised. We demonstrate that data related to novel operation are also identified for transfer to the fleet monitoring system, allowing model improvement by retraining. In addition to these industrial dataset results, reproducible examples are provided for a public domain NASA dataset. The data prioritisation solution is capable of running in real-time on production-standard low-power embedded hardware and is deployed on the Rolls-Royce Pearl 15 engines.