@inproceedings{18d1d96c37f9446db92c1423b6705376,
title = "Autoencoder-Based Features Extraction for the Health Monitoring in the Space domain",
abstract = "In the satellite operation domain, the accurate pre-diction of the Remaining Useful Life (RUL) of satellite subsystems and components is fundamental for an effective management of the mission. The accuracy of the RUL estimation depends not only on the predictive model but also on the quality and quantity of degradation features to monitor and predict. This paper proposes the use of an Autoencoder to extract time-domain features of a complex multi-sensor satellite sub-system. The Autoencoder is tested on a real-world satellite dataset for condition monitoring. The data is injected with increasing drifts to emulate degradation phenomena. The obtained features are compared with traditional time-domain features such as mean and standard deviation.",
keywords = "Autoencoder, Deep Learning, Feature extraction, Prognostics and health management (PHM), Remaining Useful Life (RUL), Telemetry data",
author = "Silvia Onofri and Andriy Enttsel and Livia Manovi and Alex Marchioni and Salvatore Cognetta and Francesco Corallo and Carlo Ciancarelli and Mauro Mangia and Riccardo Rovatti and Gianluca Setti",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 IEEE 66th International Midwest Symposium on Circuits and Systems, MWSCAS 2023 ; Conference date: 06-08-2023 Through 09-08-2023",
year = "2023",
doi = "10.1109/MWSCAS57524.2023.10406124",
language = "English (US)",
series = "Midwest Symposium on Circuits and Systems",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "458--462",
booktitle = "2023 IEEE 66th International Midwest Symposium on Circuits and Systems, MWSCAS 2023",
address = "United States",
}