Experimental calibration and validation of a simulation model for fault detection of HVAC systems and application to a case study

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.