Abstract
This paper aims to provide a summarized classification of fault detection and diagnosis (FDD) methods in Heating Ventilation and Air Conditioning (HVAC) systems by dividing them into knowledge-driven-based, data-driven and hybrid approaches, and then subdividing each category to more detailed categories. Considering the advantages and disadvantages of each method, it is concluded that knowledge-driven approaches require noticeable expertise, high number of input variables and consequently sensors to be installed, also having scalability issues. On the other hand, data-driven methods provide more precise results, while they require reliable labeled fault free and/or faulty data which is hard to access especially in real-world Building Automation System (BAS) data. Considering the disadvantages of knowledge-based and data-driven approaches and following a brief explanation of current studies based on hybrid methods, this paper highlights the necessity of hybrid FDD approach expansion in the future studies specifically in fault diagnosis.
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The work of Marco Savino Piscitelli was carried out within the Ministerial Decree no. 1062/2021 and received funding from the FSE REACT-EU—PON Ricerca e Innovazione 2014–2020.
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Piscitelli, M.S., Hooman, A., Rosato, A., Capozzoli, A. (2024). Overview on Fault Detection and Diagnosis Methods in Building HVAC Systems: Toward a Hybrid Approach. In: Littlewood, J.R., Jain, L., Howlett, R.J. (eds) Sustainability in Energy and Buildings 2023. Smart Innovation, Systems and Technologies, vol 378. Springer, Singapore. https://doi.org/10.1007/978-981-99-8501-2_61
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DOI: https://doi.org/10.1007/978-981-99-8501-2_61
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