Different Faults’ Severities of Fans in a Typical Air-Handling Unit in Southern Italy: Experimental Assessment of Indoor Conditions, Electric Demands, CO2 Emissions and Costs

Data-driven Automated Fault Detection and Diagnosis (AFDD) has emerged as an innovative solution for optimizing the performance of heating, ventilation, and air-conditioning (HVAC) systems. This study presents a comprehensive dataset generated through a series of experiments conducted on a typical single-duct dual-fan constant air volume air-handling unit (AHU) serving a test room in southern Italy. The AHU performance has been analyzed under both faulty and normal scenarios during winter and summer. Eight artificially induced fan faults have been investigated: supply air fan (SAF) stuck at (i) 0%, (ii) 25%, (iii) 75% and (iv) 100% of its maximum velocity as well as return air fan (RAF) stuck at (v) 0%, (vi) 25%, (vii) 75% and (viii) 100% of its maximum velocity. The faulty AHU performance has been compared to the fault-free scenarios (with both fans operating at 50% of their maximum velocity) under identical boundary conditions to quantify deviations in terms of indoor thermo-hygrometric conditions, electric energy consumption, equivalent global CO2 emissions, and operational costs. With respect to normal operation, fans’ faults can cause an increase of daily total electricity demand, equivalent global CO2 emissions, and operating costs from a minimum of about 3% up to a maximum of approximately 110%. Moreover, they can reduce the time percentage during which indoor air relative humidity or temperature remains within the desired range from a minimum of about 0.2% up to a maximum of approximately 62.0%.