Search Results

Documents authored by Dresia, Kai

Document
DOI: 10.4230/OASIcs.DX.2026.8
Hybrid Fault Detection and Isolation for the Fuel Turbopump Subsystem of the LOX/LNG Expander-Bleed Rocket Engine LUMEN

Authors: Eldin Kurudzija, Marcos Quinones-Grueiro, Kai Dresia, Austin Coursey, Gautam Biswas, Jan Deeken, and Günther Waxenegger-Wilfing

Published in: OASIcs, Volume 148, 37th International Conference on Principles of Diagnosis and Resilient Systems (DX 2026)

Abstract
The transition toward reusable and cost-efficient launch systems increases the need for advanced fault detection and isolation (FDI) capabilities to support the reliable and safe operation of liquid rocket engines. Development of these systems typically relies on physics-based simulation models and experimental data. LUMEN (Liquid Upper stage deMonstrator ENgine) is a modular, pump-fed LOX/LNG rocket engine with a nominal thrust of 25 kN, developed and operated by the Institute of Space Propulsion of the German Aerospace Center (DLR). The Rocket Engine Control and Diagnosis Benchmark provides a high-fidelity, experimentally validated EcosimPro/ESPSS simulation model of LUMEN for FDI system development. In this work, we use the fuel turbopump subsystem of this model as a case study and integrate data-driven components into a consistency-based diagnosis (CBD) framework to improve diagnostic performance. The results demonstrate that the hybrid architecture improves isolation accuracy, increasing it from 0.46 to 0.76 compared to the CBD baseline. In addition, we propose a sensor validation network (SVN) that uses a grey-box neural ordinary differential equation surrogate driven by valve-command inputs to decouple sensor faults from component faults. This further increases the isolation accuracy to 0.92, while retaining the ability to detect unknown component faults. These results indicate that incorporating data-driven components helps to compensate for structural non-isolability and improve diagnostic performance while preserving physical interpretability.
Cite as

Eldin Kurudzija, Marcos Quinones-Grueiro, Kai Dresia, Austin Coursey, Gautam Biswas, Jan Deeken, and Günther Waxenegger-Wilfing. Hybrid Fault Detection and Isolation for the Fuel Turbopump Subsystem of the LOX/LNG Expander-Bleed Rocket Engine LUMEN. In 37th International Conference on Principles of Diagnosis and Resilient Systems (DX 2026). Open Access Series in Informatics (OASIcs), Volume 148, pp. 8:1-8:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

Copy BibTex To Clipboard

@InProceedings{kurudzija_et_al:OASIcs.DX.2026.8,
  author =	{Kurudzija, Eldin and Quinones-Grueiro, Marcos and Dresia, Kai and Coursey, Austin and Biswas, Gautam and Deeken, Jan and Waxenegger-Wilfing, G\"{u}nther},
  title =	{{Hybrid Fault Detection and Isolation for the Fuel Turbopump Subsystem of the LOX/LNG Expander-Bleed Rocket Engine LUMEN}},
  booktitle =	{37th International Conference on Principles of Diagnosis and Resilient Systems (DX 2026)},
  pages =	{8:1--8:20},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-455-0},
  ISSN =	{2190-6807},
  year =	{2026},
  volume =	{148},
  editor =	{Pill, Ingo and Zanella, Marina and Provan, Gregory},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.DX.2026.8},
  URN =		{urn:nbn:de:0030-drops-278223},
  doi =		{10.4230/OASIcs.DX.2026.8},
  annote =	{Keywords: Hybrid, Fault Detection and Isolation, Liquid Rocket Engines, LUMEN}
}
Document
DX Competition
DOI: 10.4230/OASIcs.DX.2025.14
The DX Competition 2025 and Its Benchmarks (DX Competition)

Authors: Ingo Pill, Daniel Jung, Eldin Kurudzija, Anna Sztyber-Betley, Michał Syfert, Kai Dresia, Günther Waxenegger-Wilfing, and Johan de Kleer

Published in: OASIcs, Volume 136, 36th International Conference on Principles of Diagnosis and Resilient Systems (DX 2025)

Abstract
Fault diagnosis has been addressed in many research communities, leading to a variety of available fault diagnosis techniques. Deciding as a user which fault diagnosis methods are suitable for a specific application is thus a nontrivial task. Benchmarks can provide the community with a holistic understanding of the landscape of newly developed and available fault diagnosis methods when making this decision. After a long hiatus, we revived the DX Competition with three fault diagnosis benchmarks: SLIDe, LUMEN, and LiU-ICE. The purpose of the benchmarks is to inspire fault diagnosis research with challenging problems in cyber-physical systems relevant for industry. The benchmarks share a common code structure and we used similar performance metrics in order to simplify the adaptation of diagnosis system solutions to the different case studies.
Cite as

Ingo Pill, Daniel Jung, Eldin Kurudzija, Anna Sztyber-Betley, Michał Syfert, Kai Dresia, Günther Waxenegger-Wilfing, and Johan de Kleer. The DX Competition 2025 and Its Benchmarks (DX Competition). In 36th International Conference on Principles of Diagnosis and Resilient Systems (DX 2025). Open Access Series in Informatics (OASIcs), Volume 136, pp. 14:1-14:19, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

Copy BibTex To Clipboard

@InProceedings{pill_et_al:OASIcs.DX.2025.14,
  author =	{Pill, Ingo and Jung, Daniel and Kurudzija, Eldin and Sztyber-Betley, Anna and Syfert, Micha{\l} and Dresia, Kai and Waxenegger-Wilfing, G\"{u}nther and de Kleer, Johan},
  title =	{{The DX Competition 2025 and Its Benchmarks}},
  booktitle =	{36th International Conference on Principles of Diagnosis and Resilient Systems (DX 2025)},
  pages =	{14:1--14:19},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-394-2},
  ISSN =	{2190-6807},
  year =	{2025},
  volume =	{136},
  editor =	{Quinones-Grueiro, Marcos and Biswas, Gautam and Pill, Ingo},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.DX.2025.14},
  URN =		{urn:nbn:de:0030-drops-248030},
  doi =		{10.4230/OASIcs.DX.2025.14},
  annote =	{Keywords: Diagnosis, Algorithms, Evaluation}
}

Any Issues?
X

Feedback on the Current Page

CAPTCHA

Thanks for your feedback!

Feedback submitted to Dagstuhl Publishing

Could not send message

Please try again later or send an E-mail
© 2023-2026 Schloss Dagstuhl – LZI GmbH Schloss Dagstuhl – LZI GmbH About DROPS Imprint Privacy Contact