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Documents authored by Koitz-Hristov, Roxane

Document
DOI: 10.4230/OASIcs.DX.2026.11
QSIM-Guided Weak Supervision for Fault Detection in Cyber-Physical Systems

Authors: Ankita Das, Roxane Koitz-Hristov, and Franz Wotawa

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

Abstract
This paper presents an initial investigation into the combination of qualitative simulation and machine learning for fault detection in cyber-physical systems. In recent years, the detection and diagnosis of faults in cyber-physical systems has become increasingly dependent on machine learning. However, supervised machine learning approaches rely on large labelled datasets, which are rarely available during the initial deployment phase. In systems such as smart buildings, faults occur rarely, and even the nominal behaviour of a system can vary across structurally identical installations. We therefore propose a framework that addresses this issue by generating weak supervision labels using qualitative reasoning. Given a qualitative model that captures the system’s nominal behaviour, we use a conformance check to determine whether the system traces are likely to be faulty or not. These binary pseudo-labels are then used to train a downstream classifier. Unlike statistical anomaly detection or self-supervised techniques, the generated supervision signal is based on physical consistency rather than deviations from a learned baseline. We evaluate our approach on four benchmarks relating to electrical, fluid, mechanical and thermal dynamics, comparing our methodology with unsupervised anomaly detection and supervised learning with limited labels. Our initial experiments suggest that, while qualitative conformance checks can provide usable training signals when labelled data is unavailable, their effectiveness depends on the discriminative structure of the qualitative model.
Cite as

Ankita Das, Roxane Koitz-Hristov, and Franz Wotawa. QSIM-Guided Weak Supervision for Fault Detection in Cyber-Physical Systems. In 37th International Conference on Principles of Diagnosis and Resilient Systems (DX 2026). Open Access Series in Informatics (OASIcs), Volume 148, pp. 11:1-11:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@InProceedings{das_et_al:OASIcs.DX.2026.11,
  author =	{Das, Ankita and Koitz-Hristov, Roxane and Wotawa, Franz},
  title =	{{QSIM-Guided Weak Supervision for Fault Detection in Cyber-Physical Systems}},
  booktitle =	{37th International Conference on Principles of Diagnosis and Resilient Systems (DX 2026)},
  pages =	{11:1--11:20},
  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.2026.11},
  URN =		{urn:nbn:de:0030-drops-278254},
  doi =		{10.4230/OASIcs.DX.2026.11},
  annote =	{Keywords: Qualitative Simulation, Fault Detection, Outlier Detection}
}
Document
DOI: 10.4230/OASIcs.DX.2025.1
Beyond Static Diagnosis: A Temporal ASP Framework for HVAC Fault Detection

Authors: Roxane Koitz-Hristov, Liliana Marie Prikler, and Franz Wotawa

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

Abstract
Improving sustainability in the building sector requires more efficient operation of energy-intensive systems such as Heating, Ventilation, and Air Conditioning (HVAC). We present a novel diagnostic framework for HVAC systems that integrates Answer Set Programming (ASP) with Functional Event Calculus (FEC). Our approach exploits the declarative nature of ASP for modeling and incorporates FEC to capture temporal system dynamics. We demonstrate the feasibility of our approach through a case study on a real-world heating system, where we model key components and system constraints. Our evaluation on nominal and faulty traces shows that exploiting ASP in combination with FEC can identify plausible diagnoses. Moreover, we explore the difference between static and rolling-window strategies and provide insights into runtime versus soundness on those variants. Our work provides a step toward the practical application of ASP-based temporal reasoning in building diagnostics.
Cite as

Roxane Koitz-Hristov, Liliana Marie Prikler, and Franz Wotawa. Beyond Static Diagnosis: A Temporal ASP Framework for HVAC Fault Detection. In 36th International Conference on Principles of Diagnosis and Resilient Systems (DX 2025). Open Access Series in Informatics (OASIcs), Volume 136, pp. 1:1-1:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{koitzhristov_et_al:OASIcs.DX.2025.1,
  author =	{Koitz-Hristov, Roxane and Prikler, Liliana Marie and Wotawa, Franz},
  title =	{{Beyond Static Diagnosis: A Temporal ASP Framework for HVAC Fault Detection}},
  booktitle =	{36th International Conference on Principles of Diagnosis and Resilient Systems (DX 2025)},
  pages =	{1:1--1:20},
  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.2025.1},
  URN =		{urn:nbn:de:0030-drops-247901},
  doi =		{10.4230/OASIcs.DX.2025.1},
  annote =	{Keywords: Model-based diagnosis, Answer set programming, HVAC, Modeling for diagnosis, Experimental evaluation}
}
Document
DOI: 10.4230/OASIcs.DX.2025.4
Using Qualitative Simulation Models for Monitoring and Diagnosis

Authors: Ankita Das, Roxane Koitz-Hristov, and Franz Wotawa

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

Abstract
Many systems in our daily lives control physical processes, which are parametrized and adapted, such as heating systems in buildings. Faults and non-optimized settings lead to a high energy demand and, therefore, need to be detected as early as possible. Unfortunately, due to specific adaptations, only the basic principles remain the same, but not the concrete implementations, making the use of techniques like machine learning difficult. Therefore, we suggest using abstract models that cover the basic behavior in a way that allows us to reuse the models in different installations. In particular, we discuss the application of qualitative simulation for fault detection and introduce a formal definition of conformance between the results of qualitative simulation and the monitored behavior. We discuss arising difficulties and provide a basis for further research and applications.
Cite as

Ankita Das, Roxane Koitz-Hristov, and Franz Wotawa. Using Qualitative Simulation Models for Monitoring and Diagnosis. In 36th International Conference on Principles of Diagnosis and Resilient Systems (DX 2025). Open Access Series in Informatics (OASIcs), Volume 136, pp. 4:1-4:14, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{das_et_al:OASIcs.DX.2025.4,
  author =	{Das, Ankita and Koitz-Hristov, Roxane and Wotawa, Franz},
  title =	{{Using Qualitative Simulation Models for Monitoring and Diagnosis}},
  booktitle =	{36th International Conference on Principles of Diagnosis and Resilient Systems (DX 2025)},
  pages =	{4:1--4:14},
  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.2025.4},
  URN =		{urn:nbn:de:0030-drops-247934},
  doi =		{10.4230/OASIcs.DX.2025.4},
  annote =	{Keywords: Qualitative Simulation, Fault Detection, Model-based Diagnosis, Monitoring, Application}
}

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