Automated Fault Detection and Diagnostics (FDD) could provide a cornerstone for predictive maintenance of Air-Handling Units (AHUs). An innovative multi-sensorial laboratory, called SENS-i Lab, has been set-up at the Department of Architecture and Industrial Design of the University of Campania Luigi Vanvitelli (Italy). The laboratory is equipped with an AHU (nominal cooling/heating capacity of 5.0/5.0 kW) aiming to control the thermo-hygrometric comfort inside a 4.0x4.0x3.6 m test room; the AHU is fully instrumented in order to monitor and control its operation. Several experiments for assessing the performance of the AHU have been carried out; a detailed dynamic simulation model has been developed by means of the software TRNSYS and then calibrated and validated with respect to the measured data. The results demonstrated a very good agreement between the predicted outputs and the experimental observations, highlighting the suitability of the developed model to be used in combination with FFD methods for implementing HVAC predictive maintenance programs.

PRELIMINARY EXPERIMENTAL CALIBRATION AND VALIDATION OF A DYNAMIC SIMULATION MODEL FOR FAULT DETECTION AND DIAGNOSIS OF AIR-HANDLING UNITS

Antonio Rosato;Francesco Guarino
;
Sergio Sibilio;Luigi Maffei
2020

Abstract

Automated Fault Detection and Diagnostics (FDD) could provide a cornerstone for predictive maintenance of Air-Handling Units (AHUs). An innovative multi-sensorial laboratory, called SENS-i Lab, has been set-up at the Department of Architecture and Industrial Design of the University of Campania Luigi Vanvitelli (Italy). The laboratory is equipped with an AHU (nominal cooling/heating capacity of 5.0/5.0 kW) aiming to control the thermo-hygrometric comfort inside a 4.0x4.0x3.6 m test room; the AHU is fully instrumented in order to monitor and control its operation. Several experiments for assessing the performance of the AHU have been carried out; a detailed dynamic simulation model has been developed by means of the software TRNSYS and then calibrated and validated with respect to the measured data. The results demonstrated a very good agreement between the predicted outputs and the experimental observations, highlighting the suitability of the developed model to be used in combination with FFD methods for implementing HVAC predictive maintenance programs.
978-605-86911-8-6
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11591/433485
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