**6. Conclusions**

In this study, 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. In particular, five different typical faults (affecting the supply air fan, the return air fan, the post-heating coil valve, the cooling coil valve, and the humidifier valve) have been artificially implemented in the HVAC system and analyzed.

An artificial neural network-based model of the HVAC system has also been developed in the MATLAB environment [32] and contrasted with measured data, highlighting that it is able to provide a rigorous characterization of the HVAC system's steady-state and transient performance under both normal and faulty scenarios. In more detail, the model is characterized by average values of coefficient of determination R2 in predicting supply air temperature, supply air relative humidity, opening percentage of the post-heating coil valve, opening percentage of the cooling coil valve, and opening percentage of the humidifier valve very close to the maximum values and, respectively, equal to 0.95 ◦C, 0.93%, 0.95%, 0.97%, and 0.96%.

The ANN-based model has also been coupled with a dynamic simulation model developed in TRNSYS environment [33] and then the experimental operation of the HVAC unit without faults has been compared with the predicted performance of the same system while operating with one of the five above-mentioned faults under the same boundary conditions. The results of this analysis highlighted that:


In addition, the following results in terms of standard deviation σ and arithmetic mean μ of return air temperature (TRA) and relative humidity (RHRA) have been obtained:


• The fault 5 significantly affects only the values of σ associated to RHRA under winter conditions.

Both the labeled measured data as well as the developed simulation models (together with their learning/validation datasets) will be uploaded in a public data repository and utilization will be permitted to readers for institutional and research purposes. This will allow AFDD developers, AFDD users, and research organizations to (i) explore the symptoms associated to the selected faults on the performance of a typical HVAC system, (ii) exploit the experimentally validated simulation model in order to generate operational data for assisting further research for AFDD of HVAC units, (iii) compare accuracy among AFDD methods, and (iv) identify research gaps to be addressed and future AFDD developments.

The presented experimental database will be extended over time with the aim of investigating a broader range of boundary conditions as well as different fault types. In particular, in the future the authors would like to perform additional tests with the aim of analyzing the effects associated to new faults regarding sensors (e.g., positive and negative offsets in measuring return air relative humidity and temperature), devices (e.g., blockage of air dampers and coil/humidifier valves at different levels), equipment (e.g., complete failure of fans), or controllers (e.g., frozen control signal for coils, dampers, or fans). A measurement time step equal to 1 s will be used during future experiments in order to more carefully take into account the response time of some HVAC components. In addition, the authors will extend the present analysis (where the faults have been introduced at the beginning of the faulty tests and maintained during the entire duration of the experiments) by also considering (i) faults arising suddenly during HVAC operation and remaining at a constant level after occurrence as well as (ii) shorter faulty scenarios where a component is 'sticky' and takes more time to be moved/operated with respect to normal operation. Finally, the authors in the future would like to (i) compare experimental fault free operation against experimental faulty performance of the HVAC system working under same boundary conditions, (ii) refine and improve the simulation model, and (iii) develop and test an innovative method for performing AFDD analyses based on supervised data-driven methods customized on experimental results.

**Author Contributions:** Conceptualization, A.R., F.G., E.E. and L.M.; methodology, A.R., F.G., M.M., E.E. and L.M.; software, A.R., F.G., S.S., M.M. and E.E.; validation, A.R. and F.G.; formal analysis, A.R., F.G., S.S., M.M., E.E. and L.M.; investigation, A.R., F.G. and M.M.; resources, A.R., S.S., M.M. and L.M.; data curation, A.R., F.G. and M.M.; writing—original draft preparation, A.R., F.G., S.S., M.M., E.E. and L.M.; writing—review and editing, A.R., F.G., S.S., M.M., E.E. and L.M.; visualization, A.R., F.G., S.S., M.M., E.E. and L.M.; supervision, A.R., S.S., M.M., E.E. and L.M.; project administration, A.R. and L.M.; funding acquisition, A.R., M.M. and L.M. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Data Availability Statement:** The data presented in this study are available on request from the corresponding author.

**Acknowledgments:** This work was undertaken as part of the program "PON FSE-FESR Ricerca e Innovazione 2014–2020" of the Italian Ministry of Education, University and Research, Action I.1 "Dottorati Innovativi con caratterizzazione industriale".

**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.



