Healthy and Faulty Experimental Performance of a Typical HVAC System under Italian Climatic Conditions: Artificial Neural Network-Based Model and Fault Impact Assessment

Sergio Sibilio,Luigi Maffei,Massimiliano Masullo,Antonio Rosato,Francesco Guarino,Evgueniy Entchev

doi:10.3390/en14175362

Abstract

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.

Highlights

The building sector contributes to approximately 40% of overall energy demand in industrialized countries, with Heating, Ventilation, and Air Conditioning (HVAC) systems accounting for a large part of this energy consumption [1,2]
This paper addresses several research gaps highlighted by the literature review focusing on AFDD applications to HVAC systems
A database consisting of experimental measurements of key operating parameters during transient and steady-state operation of a typical HVAC system under both normal and faulty conditions has been obtained with reference to a wide range of summer and winter scenarios

Summary

Introduction

The building sector contributes to approximately 40% of overall energy demand in industrialized countries, with Heating, Ventilation, and Air Conditioning (HVAC) systems accounting for a large part of this energy consumption [1,2]. Au-Yong et al [8] highlighted a relevant impact of poor maintenance of HVAC systems on indoor thermo-hygrometric comfort, identifying several maintenance factors significantly correlated with occupants’ satisfaction. This means that adopting a proper maintenance strategy is fundamental. In the case of a preventive maintenance, systems are examined and maintained at given periods (whatever their state is); this approach requires identifying a proper maintenance schedule in order to not waste component life that is still profitable as well as avoid safety problems

Results

Discussion

Conclusion