Data-driven Automated Fault Detection and Diagnosis (AFDD) are recognized as one of the most promising options to improve the efficiency of Air-Handling Units (AHUs). In this study, the field operation of a typical single-duct dual-fan constant air volume AHU is investigated through a series of experiments carried out under Mediterranean (southern Italy) climatic conditions considering both fault-free and faulty scenarios. The AHU performances are analyzed while artificially introducing the following five different typical faults: (1) post-heating coil valve stuck at 100% (always open); (2) post-heating coil valve stuck at 0% (always closed); (3) cooling coil valve stuck at 100% (always open); (4) cooling coil valve stuck at 0% (always closed); (5) humidifier valve stuck at 0% (always closed). The measured faulty data are compared against the corresponding fault-free performance measured under the same boundary conditions with the aim of assessing the faults’ impact on both thermal/hygrometric indoor conditions, as well as patterns of 16 different key operating parameters. The results of this study can help building operators and facility engineers in identifying faults’ symptoms in typical AHUs and facilitate the related development of new AFDD tools.

Faults of heating, ventilation, and air-conditioning (HVAC) systems can cause significant consequences, such as negatively affecting thermal comfort of occupants, energy demand, indoor air quality, etc. Several methods of fault detection and diagnosis (FDD) in building energy systems have been proposed since the late 1980s in order to reduce the consequences of faults in heating, ventilation, and air-conditioning (HVAC) systems. All the proposed FDD methods require laboratory data, or simulated data, or field data. Furthermore, the majority of the recently proposed FDD methods require labelled faulty and normal data to be developed. Thus, providing reliable ground truth data of HVAC systems with different technical characteristics is of great importance for advances in FDD methods for HVAC units. The primary objective of this study is to examine the operational behaviour of a typical single-duct dual-fan constant air volume air-handling unit (AHU) in both faulty and fault-free conditions. The investigation encompasses a series of experiments conducted under Mediterranean climatic conditions in southern Italy during summer and winter. This study investigates the performance of the AHU by artificially introducing seven distinct typical faults: (1) return air damper kept always closed (stuck at 0%); (2) fresh air damper kept always closed (stuck at 0%); (3) fresh air damper kept always opened (stuck at 100%); (4) exhaust air damper kept always closed (stuck at 0%); (5) supply air filter partially clogged at 50%; (6) fresh air filter partially clogged at 50%; and (7) return air filter partially clogged at 50%. The collected data from the faulty scenarios are compared to the corresponding data obtained from fault-free performance measurements conducted under similar boundary conditions. Indoor thermo-hygrometric conditions, electrical power and energy consumption, operation time of AHU components, and all key operating parameters are measured for all the aforementioned faulty tests and their corresponding normal tests. In particular, the experimental results demonstrated that the exhaust air damper stuck at 0% significantly reduces the percentage of time with indoor air relative humidity kept within the defined deadbands by about 29% (together with a reduction in the percentage of time with indoor air temperature kept within the defined deadbands by 7.2%) and increases electric energy consumption by about 13% during winter. Moreover, the measured data underlined that the effects on electrical energy demand and indoor thermo-hygrometric conditions are minimal (with deviations not exceeding 5.6% during both summer and winter) in the cases of 50% clogging of supply air filter, fresh air filter, and return air filter. The results of this study can be exploited by researchers, facility managers, and building operators to better recognize root causes of faulty evidences in AHUs and also to develop and test new FDD tools.