In-flight Novelty Detection with Convolutional Neural Networks
- URL: http://arxiv.org/abs/2112.03765v1
- Date: Tue, 7 Dec 2021 15:19:41 GMT
- Title: In-flight Novelty Detection with Convolutional Neural Networks
- Authors: Adam Hartwell, Felipe Montana, Will Jacobs, Visakan Kadirkamanathan,
Andrew R Mills, Tom Clark
- Abstract summary: This paper proposes that system output measurements are prioritised in real-time for the attention of preventative maintenance decision makers.
We present a data-driven system for online detection and prioritisation of anomalous data.
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.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- 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.
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