FLASH-FAULT. Fast learning algorithm for a single sensor based heating system fault detection
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DOI der Originalpublikation
Projekttyp
angewandte Forschung
Projektbeginn
01.2025
Projektende
12.2025
Projektstatus
laufend
Projektkontakt
Projektmanager:in
Beteiligte
Beschreibung
Zusammenfassung
The potential of machine learning (ML) algorithms for fault detection in residential building heating systems is well established in scientific literature including our recent works. We developed a preliminary ML-pipeline, that utilizes time series forecasting to identify faults in a solar thermal system using minimal initial data unlike existing data-intensive approaches. Although the ML algorithm achieves similar accuracies as the rule-based algorithm integrated into the industrial partner’s single-sensor IoT framework, it lacks theoretical testing and conceptual evaluation.
We aim to advance this ML approach to industrial maturity, necessitating a critical evaluation of our ML pipeline and support for robust deployment.
This project has significant potential for fault detection in other heating systems, such as heat pumps and district heating, projected to play a major role in Switzerland’s future energy mix. Collaboration with SDSC is essential for ensuring the successful translation of our research prototype into a robust, production-ready application, significantly impacting energy efficiency and sustainability in building heating systems.
Während FHNW Zugehörigkeit erstellt
Ja
Zukunftsfelder FHNW
Zero Emission
Hochschule
Hochschule für Architektur, Bau und Geomatik FHNW
Institut
Institut Nachhaltigkeit und Energie am Bau
Finanziert durch
Swiss Data Science Center
Projektpartner
Swiss Data Science Center