Document Open Access Logo

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

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

PDF
Thumbnail PDF

File

OASIcs.DX.2024.15.pdf
  • Filesize: 1.76 MB
  • 20 pages

Document Identifiers

Author Details

Andrea Urgolo
  • Silicon Austria Labs GmbH (SAL), Graz, Austria
Ingo Pill
  • Institute of Software Technology, Graz University of Technology, Austria
Günther Waxenegger-Wilfing
  • University of Würzburg, Germany
  • German Aerospace Center (DLR), Lampoldshausen, Germany
Manuel Freiberger
  • Silicon Austria Labs GmbH (SAL), Graz, Austria

Funding

This project has been financially supported by the Austrian Research Promotion Agency (FFG) under grant no. 897989.

Cite As Get BibTex

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) https://doi.org/10.4230/OASIcs.DX.2024.15

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.

Subject Classification

ACM Subject Classification
  • Computing methodologies → Machine learning
  • Computer systems organization → Embedded and cyber-physical systems
Keywords
  • Machine learning
  • Runtime verification
  • Property learning
  • Monitoring
  • Fault detection
  • Diagnosis
  • Genetic programming
  • Explainable AI

Metrics

  • Access Statistics
  • Total Accesses (updated on a weekly basis)
    0
    PDF Downloads
    0
    Metadata Views

References

  1. A. Abid, M. T. Khan, and J. Iqbal. A review on fault detection and diagnosis techniques: basics and beyond. Artificial Intelligence Review, 54(5):3639-3664, 2021. URL: https://doi.org/10.1007/S10462-020-09934-2.
  2. K. Aggarwal, O. Atan, A. K. Farahat, C. Zhang, K. Ristovski, and C. Gupta. Two birds with one network: Unifying failure event prediction and time-to-failure modeling. In Proc. 6th Big Data, pages 1308-1317. IEEE, 2018. URL: https://doi.org/10.1109/BIGDATA.2018.8622431.
  3. B. Alpern and F. B. Schneider. Recognizing safety and liveness. Distributed Computing, 2(3):117-126, 1987. URL: https://doi.org/10.1007/BF01782772.
  4. E. Bartocci, C. Mateis, E. Nesterini, and D. Nickovic. Survey on mining signal temporal logic specifications. Information and Computation, 289:104957, 2022. URL: https://doi.org/10.1016/J.IC.2022.104957.
  5. A. Bauer, M. Leucker, and C. Schallhart. Comparing LTL semantics for runtime verification. Journal of Logic and Computation, 20(3):651-674, 2010. URL: https://doi.org/10.1093/LOGCOM/EXN075.
  6. G. Bombara and C. Belta. Offline and online learning of signal temporal logic formulae using decision trees. ACM Transactions on Cyber-Physical Systems, 5(3):1-23, 2021. URL: https://doi.org/10.1145/3433994.
  7. A. Brunello, D. Della Monica, A. Montanari, N. Saccomanno, and A. Urgolo. Monitors that learn from failures: Pairing STL and genetic programming. IEEE Access, 11:57349-57364, 2023. URL: https://doi.org/10.1109/ACCESS.2023.3277620.
  8. Y. Cao, B. J. Smucker, and T. J. Robinson. On using the hypervolume indicator to compare Pareto fronts: Applications to multi-criteria optimal experimental design. Journal of Statistical Planning and Inference, 160:60-74, 2015. Google Scholar
  9. G. Chen, M. Liu, and Z. Kong. Temporal-logic-based semantic fault diagnosis with time-series data from industrial internet of things. IEEE Transactions on Industrial Electronics, 68(5):4393-4403, 2020. URL: https://doi.org/10.1109/TIE.2020.2984976.
  10. T. Chen and C. Guestrin. XGBoost: A scalable tree boosting system. In Proc. 22nd SIGKDD, pages 785-794. ACM, 2016. Google Scholar
  11. A. Cimatti, C. Pecheur, and R. Cavada. Formal verification of diagnosability via symbolic model checking. In Proc. 18th IJCAI, pages 363-369, 2003. Google Scholar
  12. E. M. Clarke, O. Grumberg, D. Kroening, D. A. Peled, and H. Veith. Model checking. MIT Press, 2nd edition, 2018. Google Scholar
  13. K. Deb and H. Jain. An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part I: Solving problems with box constraints. IEEE Transactions on Evolutionary Computation, 18(4):577-601, 2013. URL: https://doi.org/10.1109/TEVC.2013.2281535.
  14. A. Donzé, T. Ferrère, and O. Maler. Efficient robust monitoring for STL. In Computer Aided Verification, pages 264-279. Springer, 2013. URL: https://doi.org/10.1007/978-3-642-39799-8_19.
  15. K. Dresia, M. Boerner, W. Armbruster, S. Klein, T. Traudt, D. Suslov, J. Hardi, G. Waxenegger-Wilfing, and J. Deeken. Design and Control Challenges for the LUMEN LOX/LNG Expander-Bleed Rocket Engine. In Proc. 34th ISTS, 2023. Google Scholar
  16. A. E. Eiben and J. E. Smith. Introduction to evolutionary computing. Springer, 2003. Google Scholar
  17. European Commission. Communication from the commission to the european parliament, the european council, the council, the european economic and social committee and the committee of the regions- the european green deal. Technical report, European Commission, 2019. URL: https://eur-lex.europa.eu/resource.html?uri=cellar:b828d165-1c22-11ea-8c1f-01aa75ed71a1.0002.02/DOC_1&format=PDF.
  18. F. A. Fortin, F. M. De Rainville, M. A. Gardner, M. Parizeau, and C. Gagné. DEAP: Evolutionary algorithms made easy. Journal of Machine Learning Research, 13(70):2171-2175, 2012. URL: https://doi.org/10.5555/2503308.2503311.
  19. J. Gao, H. Wang, and H. Shen. Task failure prediction in cloud data centers using deep learning. IEEE Transactions on Services Computing, 15(3):1411-1422, May 2022. URL: https://doi.org/10.1109/TSC.2020.2993728.
  20. J. R. Koza. Genetic programming as a means for programming computers by natural selection. Statistics and Computing, 4(2):87-112, 1994. Google Scholar
  21. M. Leucker and C. Schallhart. A brief account of runtime verification. The Journal of Logic and Algebraic Programming, 78(5):293-303, 2009. URL: https://doi.org/10.1016/J.JLAP.2008.08.004.
  22. S. Lu, B. Luo, T. Patel, Y. Yao, D. Tiwari, and W. Shi. Making disk failure predictions smarter! In Proc. 18th FAST, pages 151-167. USENIX, 2020. Google Scholar
  23. O. Maler and D. Nickovic. Monitoring temporal properties of continuous signals. In Formal Techniques, Modelling and Analysis of Timed and Fault-Tolerant Systems, pages 152-166. Springer, 2004. URL: https://doi.org/10.1007/978-3-540-30206-3_12.
  24. I. Matei, J. de Kleer, A. Feldman, M. Zhenirovskyy, and R. Rai. Classification based diagnosis: Integrating partial knowledge of the physical system. In Proc. 11th PHM, 2019. Google Scholar
  25. L. Nenzi, S. Silvetti, E. Bartocci, and L. Bortolussi. A robust genetic algorithm for learning temporal specifications from data. In Proc. 15th QEST, pages 323-338. Springer, 2018. URL: https://doi.org/10.1007/978-3-319-99154-2_20.
  26. D. Ničković and T. Yamaguchi. RTAMT: Online robustness monitors from STL. In Proc. 18th ATVA, volume 12302, pages 564-571. Springer, 2020. URL: https://doi.org/10.1007/978-3-030-59152-6_34.
  27. O. Niggemann, G. Biswas, J. S. Kinnebrew, H. Khorasgani, S. Volgmann, and A. Bunte. Data-driven monitoring of cyber-physical systems leveraging on big data and the internet-of-things for diagnosis and control. In Proc. 26th DX, pages 185-192, 2015. Google Scholar
  28. G. Petmezas, K. Haris, L. Stefanopoulos, V. Kilintzis, A. Tzavelis, J. A. Rogers, A. K. Katsaggelos, and N. Maglaveras. Automated atrial fibrillation detection using a hybrid CNN-LSTM network on imbalanced ECG datasets. Biomedical Signal Processing and Control, 63:102194, 2021. URL: https://doi.org/10.1016/J.BSPC.2020.102194.
  29. I. Pill and F. Wotawa. Automated generation of (F)LTL oracles for testing and debugging. Journal of Systems and Software, 139:124-141, 2018. URL: https://doi.org/10.1016/J.JSS.2018.02.002.
  30. A. Pnueli. The temporal logic of programs. In Proc. 18th SFCS, pages 46-57. IEEE, 1977. Google Scholar
  31. S. Pérez-Roca, J. Marzat, H. Piet-Lahanier, N. Langlois, F. Farago, M. Galeotta, and S. Le Gonidec. A survey of automatic control methods for liquid-propellant rocket engines. Progress in Aerospace Sciences, 107:63-84, 2019. Google Scholar
  32. V. S. Reddy. The SpaceX effect. New Space, 6(2):125-134, 2018. Google Scholar
  33. T. Traudt, W. Armbruster, C. Groll, R. Hahn, K. Dresia, M. Börner, S. Klein, D. I. Suslov, J. Haemisch, M. A. Müller, J. Deeken, J. Hardi, and S. Schlechtriem. LUMEN, the Test Bed for Rocket Engine Components: Results of the Acceptance Tests and Overview on the Engine Test Preparation. In Proc. 9th 3AF Space Propulsion, 2024. Google Scholar
  34. A. Urgolo, C. Gei, K. Dresia, M. Freiberger, E. Kurudzija, H. Neumann, I. Pill, F. Pittino, G. Radchenko, and G. Waxenegger-Wilfing. Feature selection and virtual sensing based mixture ratio estimation for liquid propellant rocket engine control systems. In Proc. 9th 3AF Space Propulsion, 2024. Google Scholar
Questions / Remarks / Feedback
X

Feedback for Dagstuhl Publishing


Thanks for your feedback!

Feedback submitted

Could not send message

Please try again later or send an E-mail
© 2023-2024 Schloss Dagstuhl – LZI GmbH About DROPS Imprint Privacy Contact