Data-driven automated fault detection and diagnosis (AFDD) offers a significant advantage in optimizing AHU efficiency by enabling real-time fault detection. This study evaluates the field performance of a single-duct, dual-fan constant air volume AHU in a Mediterranean climate (southern Italy). The analysis considers both normal and faulty scenarios, including eleven commonly encountered faults (1) positive offset of the return air relative humidity sensor (+15%); 2) negative offset of the return air relative humidity sensor (– 15%); 3) negative offset of the return air temperature sensor (– 3 °C); 4) humidifier valve stuck at 0% (always closed); 5) positive offset of the return air temperature sensor (+3 °C); 6) cooling coil valve stuck at 100% (always open); 7) post-heating coil valve stuck at 0% (always closed); 8) post-heating coil valve stuck at 100% (always open); 9) cooling coil valve stuck at 0% (always closed); 10) complete failure of the return air fan; 11) complete failure of the supply air fan. The impact of these faults on AHU performance is assessed in terms of electrical power consumption, energy demand, operational costs, global equivalent CO2 emissions as well as operation time. Among the investigated faults, experimental results highlight that the post-heating coil valve stuck at 100% significantly affects system performance; specifically, this fault decreases daily indoor air temperature met ratio by about 69% during summer and winter tests, while increases total daily electrical energy consumption, operational costs, and global equivalent CO2 emissions by over than 60% during cooling period and by more than 80% during heating period. Through a comparative analysis between observed faulty data and normal performance under identical conditions, this study provides valuable insights for developing AFDD tools and assists building personnel in identifying symptoms of common AHU faults, thereby contributing to more efficient building management.
Energy, environmental and economic experimental assessment of a typical air-handling unit operating under single faults on valves, fans and sensors in south of Italy
Rosato Antonio
;El Youssef Mohammad;Mercuri Rita;
2026
Abstract
Data-driven automated fault detection and diagnosis (AFDD) offers a significant advantage in optimizing AHU efficiency by enabling real-time fault detection. This study evaluates the field performance of a single-duct, dual-fan constant air volume AHU in a Mediterranean climate (southern Italy). The analysis considers both normal and faulty scenarios, including eleven commonly encountered faults (1) positive offset of the return air relative humidity sensor (+15%); 2) negative offset of the return air relative humidity sensor (– 15%); 3) negative offset of the return air temperature sensor (– 3 °C); 4) humidifier valve stuck at 0% (always closed); 5) positive offset of the return air temperature sensor (+3 °C); 6) cooling coil valve stuck at 100% (always open); 7) post-heating coil valve stuck at 0% (always closed); 8) post-heating coil valve stuck at 100% (always open); 9) cooling coil valve stuck at 0% (always closed); 10) complete failure of the return air fan; 11) complete failure of the supply air fan. The impact of these faults on AHU performance is assessed in terms of electrical power consumption, energy demand, operational costs, global equivalent CO2 emissions as well as operation time. Among the investigated faults, experimental results highlight that the post-heating coil valve stuck at 100% significantly affects system performance; specifically, this fault decreases daily indoor air temperature met ratio by about 69% during summer and winter tests, while increases total daily electrical energy consumption, operational costs, and global equivalent CO2 emissions by over than 60% during cooling period and by more than 80% during heating period. Through a comparative analysis between observed faulty data and normal performance under identical conditions, this study provides valuable insights for developing AFDD tools and assists building personnel in identifying symptoms of common AHU faults, thereby contributing to more efficient building management.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
