AUTOMATION OF TESTING AND FAULT DETECTION FOR ROCKET ENGINE TEST FACILITIES WITH MACHINE LEARNING

被引：0

作者：

Dresia, Kai ^{[1
]}

Kurudzija, Eldin ^{[1
]}

Waxenegger-Wilfing, Guenther ^{[1
]}

Behler, Hendrik ^{[1
]}

Auer, Daniel ^{[1
]}

Froehlke, Karsten ^{[1
]}

Neumann, Heike ^{[1
]}

Frank, Anja ^{[1
]}

Laurent, Jerome ^{[2
]}

Fabreguettes, Luce ^{[2
]}

机构：

[1] German Aerosp Ctr DLR, D-51147 Cologne, Germany

[2] European Space Agcy ESA, Paris, France

来源：

INTERNATIONAL JOURNAL OF ENERGETIC MATERIALS AND CHEMICAL PROPULSION | 2023年 / 22卷 / 06期

关键词：

rocket engine test facilities; machine learning; digital twin; anomaly de tection; intelligent control; reinforcement learning;

D O I：

10.1615/IntJEnergeticMaterialsChemProp.2023047195

中图分类号：

V [航空、航天];

学科分类号：

08 ; 0825 ;

摘要：

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.

引用

页码：17 / 33

页数：17

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