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Using Qualitative Simulation Models for Monitoring and Diagnosis

Authors Ankita Das , Roxane Koitz-Hristov , Franz Wotawa

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OASIcs.DX.2025.4.pdf
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Subject Classification

ACM Subject Classification
  • Computing methodologies → Causal reasoning and diagnostics
  • Computing methodologies → Spatial and physical reasoning
  • Computing methodologies → Modeling methodologies
Keywords
  • Qualitative Simulation
  • Fault Detection
  • Model-based Diagnosis
  • Monitoring
  • Application

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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 Get BibTex

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) https://doi.org/10.4230/OASIcs.DX.2025.4

Author Details

Ankita Das
  • Institute of Software Engineering and Artificial Intelligence, Graz University of Technology, Austria
Roxane Koitz-Hristov
  • Institute of Software Engineering and Artificial Intelligence, Graz University of Technology, Austria
Franz Wotawa
  • Institute of Software Engineering and Artificial Intelligence, Graz University of Technology, Austria

Funding

The work presented in this paper has been supported by the FFG project Artificial Intelligence for Smart Diagnosis in Building Automation (ALFA) under grant FO999914932.
  • Das, Ankita: Supported by by the Christian-Doppler Forschungsgesellschaft (CDG) project AI-based Diagnosis for Energy Transition and the Circular Economy (AID4ETCE).

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