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.File in questo prodotto:
Non ci sono file associati a questo prodotto.
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
