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Detecting Soft Faults in Heat Pumps (Short Paper)

Authors Birgit Hofer , Franz Wotawa

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Author Details

Birgit Hofer
  • Graz University of Technology, Austria
Franz Wotawa
  • Graz University of Technology, Austria

Funding

The research described in this paper has been carried out within the FFG AI for Green program as part of the project FO999899931 GreenHeat – The next generation of AI-based services for heat pumps funded by the Federal Ministry for Climate Action, Environment, Energy, Mobility, Innovation and Technology (BMK).

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Birgit Hofer and Franz Wotawa. Detecting Soft Faults in Heat Pumps (Short Paper). In 35th International Conference on Principles of Diagnosis and Resilient Systems (DX 2024). Open Access Series in Informatics (OASIcs), Volume 125, pp. 22:1-22:10, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024) https://doi.org/10.4230/OASIcs.DX.2024.22

Abstract

Heat pumps are critical for energy-efficient heating and cooling, but their performance can be compromised by soft faults like condenser silting. It is vital to detect and fix such faults early in order to ensure optimal performance and longevity of heat pump systems, and consequently optimize the positive effect of heat pumps to our environment. In this paper, we tackle the problem of early fault detection and propose a supervised machine learning approach that detects soft faults. In particular, we used a random forest approach to learn the regular behavior of heat pumps. We detect faults via comparing the expected behavior obtained from the learned model with the current behavior. In addition to the description of the used methodology, we provide and discuss the results obtained from an experimental study that is based on synthetic data of two different heat pumps.

Subject Classification

ACM Subject Classification
  • Hardware → Error detection and error correction
Keywords
  • Fault detection
  • heat pumps
  • supervised machine learning

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