Data-driven Automated Fault Detection and Diagnosis (AFDD) methods rep-resent one of the most promising options for improving energy, environmen-tal 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.

Experimental assessment of a preliminary rule-based data-driven method for fault detection and diagnosis of coils, fans and sensors in air-handling units

Mohammad El Youssef
;
Francesco Guarino;Sergio Sibilio;Antonio Rosato
2023

Abstract

Data-driven Automated Fault Detection and Diagnosis (AFDD) methods rep-resent one of the most promising options for improving energy, environmen-tal 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.
978-981-19-8769-4
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11591/480750
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