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Beyond Static Diagnosis: A Temporal ASP Framework for HVAC Fault Detection

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

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ACM Subject Classification
  • Computing methodologies → Causal reasoning and diagnostics
  • Computing methodologies → Logic programming and answer set programming
Keywords
  • Model-based diagnosis
  • Answer set programming
  • HVAC
  • Modeling for diagnosis
  • Experimental evaluation

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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 Get BibTex

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) https://doi.org/10.4230/OASIcs.DX.2025.1

Author Details

Roxane Koitz-Hristov
  • Institute of Software Engineering and Artificial Intelligence, Graz University of Technology, Austria
Liliana Marie Prikler
  • 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.

Acknowledgements

We would like to express our gratitude to our industrial partner, DiLT Analytics FlexCo.

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