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DOI: 10.4230/OASIcs.DX.2024.15
Property Learning-Based Fault Detection for Liquid Propellant Rocket Engine Control Systems

Authors: Andrea Urgolo, Ingo Pill, Günther Waxenegger-Wilfing, and Manuel Freiberger

Published in: OASIcs, Volume 125, 35th International Conference on Principles of Diagnosis and Resilient Systems (DX 2024)

Abstract
Accommodating the dynamic and uncertain operational environments that are typical for aerospace applications, our work focuses on robust fault detection and accurate diagnosis in the context of Liquid Propellant Rocket Engines. To this end, we employ techniques based on learning temporal properties which are then dynamically adapted and refined based on observed behavior. Leveraging the capabilities of genetic programming, our methodology evolves and optimizes temporal properties that are validated through formal methods in order to ensure precise, interpretable real-time fault monitoring and diagnosis. Our integrated strategy enables us to enhance resilience, safety and reliability when operating rocket engines - due to the proactive detection and systematic analysis of operational deviations before they would escalate into critical failures. We demonstrate the effectiveness of our method via a rigorous evaluation across varied simulated fault conditions, in order to showcase its potential to significantly mitigate the fault-related risks in aerospace systems.
Cite as

Andrea Urgolo, Ingo Pill, Günther Waxenegger-Wilfing, and Manuel Freiberger. Property Learning-Based Fault Detection for Liquid Propellant Rocket Engine Control Systems. In 35th International Conference on Principles of Diagnosis and Resilient Systems (DX 2024). Open Access Series in Informatics (OASIcs), Volume 125, pp. 15:1-15:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{urgolo_et_al:OASIcs.DX.2024.15,
  author =	{Urgolo, Andrea and Pill, Ingo and Waxenegger-Wilfing, G\"{u}nther and Freiberger, Manuel},
  title =	{{Property Learning-Based Fault Detection for Liquid Propellant Rocket Engine Control Systems}},
  booktitle =	{35th International Conference on Principles of Diagnosis and Resilient Systems (DX 2024)},
  pages =	{15:1--15:20},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-356-0},
  ISSN =	{2190-6807},
  year =	{2024},
  volume =	{125},
  editor =	{Pill, Ingo and Natan, Avraham and Wotawa, Franz},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.DX.2024.15},
  URN =		{urn:nbn:de:0030-drops-221074},
  doi =		{10.4230/OASIcs.DX.2024.15},
  annote =	{Keywords: Machine learning, Runtime verification, Property learning, Monitoring, Fault detection, Diagnosis, Genetic programming, Explainable AI}
}
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