Unsupervised Anomaly Detection for Space Gardening
Bioregenerative Life Support Systems (BLSS) will be used within extra-terrestrial habitats to produce food, close material cycles (respiratory air, water, biomass, waste), and enhance well-being. The EDEN NEXT GEN project aims at designing an integrated BLSS ground demonstrator including all critica...
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Format: | Conference Object |
Language: | English |
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2023
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Online Access: | https://elib.dlr.de/201529/ https://elib.dlr.de/201529/1/Rewicki_Unsupervised_Anomaly_Detection_for_Space_Gardening__Data_Science_Day_2023.pdf |
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author | Rewicki, Ferdinand Denzler, Joachim Niebling, Julia |
author_facet | Rewicki, Ferdinand Denzler, Joachim Niebling, Julia |
author_sort | Rewicki, Ferdinand |
collection | Unknown |
description | Bioregenerative Life Support Systems (BLSS) will be used within extra-terrestrial habitats to produce food, close material cycles (respiratory air, water, biomass, waste), and enhance well-being. The EDEN NEXT GEN project aims at designing an integrated BLSS ground demonstrator including all critical subsystems. Therefore, it builds on the results gained at the research greenhouse EDEN ISS in Antarctica between 2018 and 2021. To ensure safe and stable operation, we are researching unsupervised anomaly detection (USAD) methods to identify unhealthy system states. While the abundance of available methods makes it difficult to choose the most appropriate method for a specific application, each method has its strengths in detecting anomalies of different types. We validate our previous findings in the BLSS domain and apply the best-performing methods to telemetry data collected from the EDEN ISS research greenhouse. |
format | Conference Object |
genre | Antarc* Antarctica |
genre_facet | Antarc* Antarctica |
id | ftdlr:oai:elib.dlr.de:201529 |
institution | Open Polar |
language | English |
op_collection_id | ftdlr |
op_relation | https://elib.dlr.de/201529/1/Rewicki_Unsupervised_Anomaly_Detection_for_Space_Gardening__Data_Science_Day_2023.pdf Rewicki, Ferdinand und Denzler, Joachim und Niebling, Julia (2023) Unsupervised Anomaly Detection for Space Gardening. Data Science Day Jena 2023, 2023-05-10, Jena, Germany. |
op_rights | cc_by |
publishDate | 2023 |
record_format | openpolar |
spelling | ftdlr:oai:elib.dlr.de:201529 2025-06-15T14:11:40+00:00 Unsupervised Anomaly Detection for Space Gardening Rewicki, Ferdinand Denzler, Joachim Niebling, Julia 2023-05-10 application/pdf https://elib.dlr.de/201529/ https://elib.dlr.de/201529/1/Rewicki_Unsupervised_Anomaly_Detection_for_Space_Gardening__Data_Science_Day_2023.pdf en eng https://elib.dlr.de/201529/1/Rewicki_Unsupervised_Anomaly_Detection_for_Space_Gardening__Data_Science_Day_2023.pdf Rewicki, Ferdinand und Denzler, Joachim und Niebling, Julia (2023) Unsupervised Anomaly Detection for Space Gardening. Data Science Day Jena 2023, 2023-05-10, Jena, Germany. cc_by Datenanalyse und -intelligenz Konferenzbeitrag NonPeerReviewed 2023 ftdlr 2025-06-04T04:58:04Z Bioregenerative Life Support Systems (BLSS) will be used within extra-terrestrial habitats to produce food, close material cycles (respiratory air, water, biomass, waste), and enhance well-being. The EDEN NEXT GEN project aims at designing an integrated BLSS ground demonstrator including all critical subsystems. Therefore, it builds on the results gained at the research greenhouse EDEN ISS in Antarctica between 2018 and 2021. To ensure safe and stable operation, we are researching unsupervised anomaly detection (USAD) methods to identify unhealthy system states. While the abundance of available methods makes it difficult to choose the most appropriate method for a specific application, each method has its strengths in detecting anomalies of different types. We validate our previous findings in the BLSS domain and apply the best-performing methods to telemetry data collected from the EDEN ISS research greenhouse. Conference Object Antarc* Antarctica Unknown |
spellingShingle | Datenanalyse und -intelligenz Rewicki, Ferdinand Denzler, Joachim Niebling, Julia Unsupervised Anomaly Detection for Space Gardening |
title | Unsupervised Anomaly Detection for Space Gardening |
title_full | Unsupervised Anomaly Detection for Space Gardening |
title_fullStr | Unsupervised Anomaly Detection for Space Gardening |
title_full_unstemmed | Unsupervised Anomaly Detection for Space Gardening |
title_short | Unsupervised Anomaly Detection for Space Gardening |
title_sort | unsupervised anomaly detection for space gardening |
topic | Datenanalyse und -intelligenz |
topic_facet | Datenanalyse und -intelligenz |
url | https://elib.dlr.de/201529/ https://elib.dlr.de/201529/1/Rewicki_Unsupervised_Anomaly_Detection_for_Space_Gardening__Data_Science_Day_2023.pdf |