Property Learning-Based Fault Detection for Liquid Propellant Rocket Engine Control Systems

Andrea Urgolo*, Ingo Pill, Günther Waxenegger-Wilfing, Manuel Freiberger

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review

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.

Original languageEnglish
Title of host publication35th International Conference on Principles of Diagnosis and Resilient Systems, DX 2024
EditorsIngo Pill, Avraham Natan, Franz Wotawa
PublisherSchloss Dagstuhl - Leibniz-Zentrum für Informatik
ISBN (Electronic)9783959773560
DOIs
Publication statusPublished - 26 Nov 2024
Event35th International Conference on Principles of Diagnosis and Resilient Systems, DX 2024 - Vienna, Austria
Duration: 4 Nov 20247 Nov 2024

Publication series

NameOpenAccess Series in Informatics
Volume125
ISSN (Print)2190-6807

Conference

Conference35th International Conference on Principles of Diagnosis and Resilient Systems, DX 2024
Country/TerritoryAustria
CityVienna
Period4/11/247/11/24

Keywords

  • Diagnosis
  • Explainable AI
  • Fault detection
  • Genetic programming
  • Machine learning
  • Monitoring
  • Property learning
  • Runtime verification

ASJC Scopus subject areas

  • Geography, Planning and Development
  • Modelling and Simulation

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