Abstract
Data-driven Automated Fault Detection and Diagnosis (AFDD) methods represent one of the most promising options for improving energy, environmental and economic performance of Air-Handling Units (AHUs). In this paper, a curated experimental faulted and unfaulted dataset associated to the field operation of a typical real AHU is firstly presented; a new rule-based data-driven AFDD method for fault detection and diagnosis of coils, fans and sensors is developed and its accuracy has been assessed in contrast with measured data.
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El Youssef, M., Guarino, F., Sibilio, S., Rosato, A. (2023). Experimental Assessment of a Preliminary Rule-Based Data-Driven Method for Fault Detection and Diagnosis of Coils, Fans and Sensors in Air-Handling Units. In: Littlewood, J., Howlett, R.J., Jain, L.C. (eds) Sustainability in Energy and Buildings 2022 . SEB 2022. Smart Innovation, Systems and Technologies, vol 336. Springer, Singapore. https://doi.org/10.1007/978-981-19-8769-4_34
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DOI: https://doi.org/10.1007/978-981-19-8769-4_34
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