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Applications of Deep Learning Neural Networks to Satellite Telemetry Monitoring

O'Meara, Corey and Schlag, Leonard and Wickler, Martin (2018) Applications of Deep Learning Neural Networks to Satellite Telemetry Monitoring. In: 15th International Conference on Space Operations, SpaceOps 2018. 15th International Conference on Space Operations (SpaceOps 2018), 2018-05-28 - 2018-06-01, Marseille, France. doi: 10.2514/6.2018-2558. ISBN 978-162410562-3.

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Official URL: https://arc.aiaa.org/doi/10.2514/6.2018-2558

Abstract

As part of the Automated Telemetry Health Monitoring System (ATHMoS) being developed at GSOC, we performed an investigation into potential applications of artificial neural networks to our existing health monitoring system. In the end, we have created an experimental module which uses several deep learning neural networks to augment our existing data analysis algorithms. The module accomplishes three things; automatic feature extraction, anomaly detection, and telemetry prediction. Automatic feature extraction is used to determine numerical values which represent a time window of a single parameters telemetry data, then, using these abstract numeric values we augment our existing so-called feature vectors used in our ATHMoS anomaly detection system to provide additional information in the anomaly detection. Additionally, we use the same type of neural network to perform anomaly detection on each telemetry parameter in order to take an ensemble machine learning approach to detecting anomalies in telemetry. By combining the results of the neural network with our existing (non-neural network) anomaly detection algorithms, we can provide a more robust classification of whether or not new data should be flagged as anomalous. Lastly, we've created a neural network which given the most recent orbit data, can predict the general behaviour of a telemetry parameter over the next four and a half hours. Thus, if the prediction we obtain is off from the usual nominal behaviour of the telemetry parameter, we flag it and label it as a potential future anomaly - thereby performing anomaly prediction. Adding these experimental neural network capabilities to work concomitantly with the already existing anomaly detection modules which ATHMoS is comprised of, has already shown to be beneficial by providing new insights into the data as well as offering a more robust approach to anomaly detection via ensemble machine learning. Here we present an overview of a year long study into applying neural networks to telemetry monitoring and discuss in detail the way in which the three applications described above are being added to the current anomaly detection system at GSOC.

Item URL in elib:https://elib.dlr.de/121211/
Document Type:Conference or Workshop Item (Lecture)
Title:Applications of Deep Learning Neural Networks to Satellite Telemetry Monitoring
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
O'Meara, CoreyUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Schlag, LeonardUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Wickler, MartinUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Date:31 May 2018
Journal or Publication Title:15th International Conference on Space Operations, SpaceOps 2018
Refereed publication:No
Open Access:Yes
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:No
DOI:10.2514/6.2018-2558
ISBN:978-162410562-3
Status:Published
Keywords:neural networks, anomaly detection, feature engineering, forecasting, telemetry, machine learning, artificial intelligence
Event Title:15th International Conference on Space Operations (SpaceOps 2018)
Event Location:Marseille, France
Event Type:international Conference
Event Start Date:28 May 2018
Event End Date:1 June 2018
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Space System Technology
DLR - Research area:Raumfahrt
DLR - Program:R SY - Space System Technology
DLR - Research theme (Project):R - Raumflugbetrieb / Kontrollzentrums-Technologie (old)
Location: Oberpfaffenhofen
Institutes and Institutions:Space Operations and Astronaut Training > Mission Operations
Deposited By: O'Meara, Corey
Deposited On:07 Aug 2018 08:51
Last Modified:24 Apr 2024 20:25

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