Autoencoder-Based Features Extraction for the Health Monitoring in the Space domain

Silvia Onofri; Andriy Enttsel; Livia Manovi; Alex Marchioni; Salvatore Cognetta; Francesco Corallo; Carlo Ciancarelli; Mauro Mangia; Riccardo Rovatti; Gianluca Setti

doi:10.1109/MWSCAS57524.2023.10406124

Autoencoder-Based Features Extraction for the Health Monitoring in the Space domain

Silvia Onofri, Andriy Enttsel, Livia Manovi, Alex Marchioni, Salvatore Cognetta, Francesco Corallo, Carlo Ciancarelli, Mauro Mangia, Riccardo Rovatti, Gianluca Setti

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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.

Original language	English (US)
Title of host publication	2023 IEEE 66th International Midwest Symposium on Circuits and Systems, MWSCAS 2023
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	458-462
Number of pages	5
ISBN (Electronic)	9798350302103
DOIs	https://doi.org/10.1109/MWSCAS57524.2023.10406124
State	Published - 2023
Event	2023 IEEE 66th International Midwest Symposium on Circuits and Systems, MWSCAS 2023 - Tempe, United States Duration: Aug 6 2023 → Aug 9 2023

Publication series

Name	Midwest Symposium on Circuits and Systems
ISSN (Print)	1548-3746

Conference

Conference	2023 IEEE 66th International Midwest Symposium on Circuits and Systems, MWSCAS 2023
Country/Territory	United States
City	Tempe
Period	08/6/23 → 08/9/23

Bibliographical note

Publisher Copyright:
© 2023 IEEE.

Keywords

Autoencoder
Deep Learning
Feature extraction
Prognostics and health management (PHM)
Remaining Useful Life (RUL)
Telemetry data

ASJC Scopus subject areas

Electronic, Optical and Magnetic Materials
Electrical and Electronic Engineering

Access to Document

10.1109/MWSCAS57524.2023.10406124

Cite this

Onofri, S., Enttsel, A., Manovi, L., Marchioni, A., Cognetta, S., Corallo, F., Ciancarelli, C., Mangia, M., Rovatti, R., & Setti, G. (2023). Autoencoder-Based Features Extraction for the Health Monitoring in the Space domain. In 2023 IEEE 66th International Midwest Symposium on Circuits and Systems, MWSCAS 2023 (pp. 458-462). (Midwest Symposium on Circuits and Systems). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/MWSCAS57524.2023.10406124

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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.",

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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",

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year = "2023",

doi = "10.1109/MWSCAS57524.2023.10406124",

language = "English (US)",

series = "Midwest Symposium on Circuits and Systems",

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Onofri, S, Enttsel, A, Manovi, L, Marchioni, A, Cognetta, S, Corallo, F, Ciancarelli, C, Mangia, M, Rovatti, R & Setti, G 2023, Autoencoder-Based Features Extraction for the Health Monitoring in the Space domain. in 2023 IEEE 66th International Midwest Symposium on Circuits and Systems, MWSCAS 2023. Midwest Symposium on Circuits and Systems, Institute of Electrical and Electronics Engineers Inc., pp. 458-462, 2023 IEEE 66th International Midwest Symposium on Circuits and Systems, MWSCAS 2023, Tempe, United States, 08/6/23. https://doi.org/10.1109/MWSCAS57524.2023.10406124

Autoencoder-Based Features Extraction for the Health Monitoring in the Space domain. / Onofri, Silvia; Enttsel, Andriy; Manovi, Livia et al.
2023 IEEE 66th International Midwest Symposium on Circuits and Systems, MWSCAS 2023. Institute of Electrical and Electronics Engineers Inc., 2023. p. 458-462 (Midwest Symposium on Circuits and Systems).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Enttsel, Andriy

AU - Manovi, Livia

AU - Marchioni, Alex

AU - Cognetta, Salvatore

AU - Corallo, Francesco

AU - Ciancarelli, Carlo

AU - Mangia, Mauro

AU - Rovatti, Riccardo

AU - Setti, Gianluca

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AB - 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.

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Onofri S, Enttsel A, Manovi L, Marchioni A, Cognetta S, Corallo F et al. Autoencoder-Based Features Extraction for the Health Monitoring in the Space domain. In 2023 IEEE 66th International Midwest Symposium on Circuits and Systems, MWSCAS 2023. Institute of Electrical and Electronics Engineers Inc. 2023. p. 458-462. (Midwest Symposium on Circuits and Systems). doi: 10.1109/MWSCAS57524.2023.10406124

Autoencoder-Based Features Extraction for the Health Monitoring in the Space domain

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this