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Automation of Testing and Fault Detection for Rocket Engine Test Facilities with Machine Learning

Dresia, Kai and Kurudzija, Eldin and Waxenegger-Wilfing, Günther and Behler, Hendrik and Auer, Daniel and Fröhlke, Karsten and Neumann, Heike and Frank, Anja and Laurent, Jérôme and Fabreguettes, Luce (2023) Automation of Testing and Fault Detection for Rocket Engine Test Facilities with Machine Learning. International Journal of Energetic Materials and Chemical Propulsion, 6 (22). Begell House. doi: 10.1615/IntJEnergeticMaterialsChemProp.2023047195. ISSN 2150-766X.

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Abstract

The German Aerospace Center (DLR) Institute of Space Propulsion has unique expertise in operating test facilities for rocket engine testing and development in Europe since 1959. However, essential elements of the test site were designed up to half a century ago. In order to ensure a futureproof and intelligent digital test infrastructure, the potential of test automation, advanced control, and monitoring systems is investigated based on machine learning. Such intelligent control systems are expected to reduce engine development and test preparation times, thereby lowering the associated costs. Additionally, advanced monitoring systems are anticipated to increase the safety and reliability of the test infrastructure. This paper presents the results of two pilot projects: the first project uses reinforcement learning to automatically generate test sequences based on test requirements, while the second project develops a feed-forward forecasting model to predict deviations from expected behavior in the feed-line of a rocket engine test facility.

Item URL in elib:https://elib.dlr.de/200338/
Document Type:Article
Title:Automation of Testing and Fault Detection for Rocket Engine Test Facilities with Machine Learning
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Dresia, KaiUNSPECIFIEDhttps://orcid.org/0000-0003-3229-5184UNSPECIFIED
Kurudzija, EldinUNSPECIFIEDhttps://orcid.org/0000-0001-5409-3845UNSPECIFIED
Waxenegger-Wilfing, GüntherUNSPECIFIEDhttps://orcid.org/0000-0001-5381-6431UNSPECIFIED
Behler, HendrikUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Auer, DanielUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Fröhlke, KarstenUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Neumann, HeikeUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Frank, AnjaUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Laurent, JérômeEuropean Space Agency (ESA), Paris, FranceUNSPECIFIEDUNSPECIFIED
Fabreguettes, LuceEuropean Space Agency (ESA), Paris, FranceUNSPECIFIEDUNSPECIFIED
Date:16 August 2023
Journal or Publication Title:International Journal of Energetic Materials and Chemical Propulsion
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:Yes
Volume:6
DOI:10.1615/IntJEnergeticMaterialsChemProp.2023047195
Publisher:Begell House
ISSN:2150-766X
Status:Published
Keywords:rocket engine test facilites; machine learning; digital twin; anomaly detection; intelligent control; reinforcement learning
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Space Transportation
DLR - Research area:Raumfahrt
DLR - Program:R RP - Space Transportation
DLR - Research theme (Project):R - Reusable Space Systems and Propulsion Technology
Location: Lampoldshausen
Institutes and Institutions:Institute of Space Propulsion > Rocket Engine Systems
Institute of Space Propulsion > Test Facilities
Deposited By: Dresia, Kai
Deposited On:04 Dec 2023 08:39
Last Modified:29 Jan 2024 12:27

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