Automated fault detection and diagnostics (FDD) could provide a cornerstone for predictive maintenance of heating, ventilation and air-conditioning (HVAC) systems based on the development of simulation models able to accurately compare the faulty operation with respect to nominal conditions. In this paper, several experiments have been carried out for assessing the performance of the HVAC unit (nominal cooling/heating capacity of 5.0/5.0 kW) controlling the thermo-hygrometric comfort inside a 4.0 × 4.0 × 3.6 m test room at the Department of Architecture and Industrial Design of the University of Campania Luigi Vanvitelli (Italy); then, a detailed dynamic simulation model has been developed and validated by contrasting the predictions with the measured data. The model has also been used to analyze the dynamic variations of key parameters associated to faulty operation in comparison to normal performance, in order to identify simplified rules for detection of any non-optimal states of HVAC devices. Finally, the simulated performance of the HVAC unit has also been investigated while serving a typical Italian building office with and without the occurrence of typical faults with the main aim of assessing the impact of the faults on thermo-hygrometric comfort conditions as well as electric energy consumption.

The heating, ventilation, and air conditioning (HVAC) system serving the test room of the SENS i-Lab of the Department of Architecture and Industrial Design of the University of Campania Luigi Vanvitelli (Aversa, south of Italy) has been experimentally investigated through a series of tests performed during both summer and winter under both normal and faulty scenarios. In particular, five distinct typical faults have been artificially implemented in the HVAC system and analyzed during transient and steady-state operation. An optimal artificial neural network-based system model has been created in the MATLAB platform and verified by contrasting the experimental data with the predictions of twenty-two different neural network architectures. The selected artificial neural network architecture has been coupled with a dynamic simulation model developed by using the TRaNsient SYStems (TRNSYS) software platform with the main aims of (i) making available an experimental dataset characterized by labeled normal and faulty data covering a wide range of operating and climatic conditions; (ii) providing an accurate simulation tool able to generate operation data for assisting further research in fault detection and diagnosis of HVAC units; and (iii) evaluating the impact of selected faults on occupant indoor thermo-hygrometric comfort, temporal trends of key operating system parameters, and electric energy consumptions.

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.

The symptoms associated to the occurrence of typical faults in a heating, ventilation and air-conditioning (HVAC) system, including a single duct dual fan constant air volume air-handling unit, have been experimentally characterized. The operation of the HVAC unit with 3 artificially forced faults ((1) reduced velocity of the supply air fan, (2) reduced velocity of the return air fan, (3) the valve supplying the humidifier kept always closed) has been analysed and compared with that of healthy operation of the same plant under very similar boundary conditions (outside air temperature and initial indoor air temperature) during Italian summer and winter in order to preliminarily assess (i) the effects on the main operating parameters, and (ii) generate preliminary operation data to assist further research in fault detection and diagnosis of HVAC systems.