In-flight Novelty Detection with Convolutional Neural Networks

TL;DR

This paper introduces a real-time, data-driven anomaly detection system using convolutional neural networks for gas turbine engines, enabling efficient prioritization of valuable data for preventative maintenance in aerospace applications.

Contribution

It presents a novel online anomaly detection and prioritization system that manages uncertainty and operates on low-power hardware, demonstrated on real engine flight data.

Findings

Effective detection of real and synthetic faults

Operates in real-time on embedded hardware

Deployed on Rolls-Royce Pearl 15 engine trials

Abstract

Gas turbine engines are complex machines that typically generate a vast amount of data, and require careful monitoring to allow for cost-effective preventative maintenance. In aerospace applications, returning all measured data to ground is prohibitively expensive, often causing useful, high value, data to be discarded. The ability to detect, prioritise, and return useful data in real-time is therefore vital. This paper proposes that system output measurements, described by a convolutional neural network model of normality, are prioritised in real-time for the attention of preventative maintenance decision makers. Due to the complexity of gas turbine engine time-varying behaviours, deriving accurate physical models is difficult, and often leads to models with low prediction accuracy and incompatibility with real-time execution. Data-driven modelling is a desirable alternative producing high accuracy, asset specific models without the need for derivation from first principles. We present a data-driven system for online detection and prioritisation of anomalous data. Biased data assessment deriving from novel operating conditions is avoided by uncertainty management integrated into the deep neural predictive model. Testing is performed on real and synthetic data, showing sensitivity to both real and synthetic faults. The system is capable of running in real-time on low-power embedded hardware and is currently in deployment on the Rolls-Royce Pearl 15 engine flight trials.