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Faulty Operation of Coils’ and Humidifier Valves in a Typical Air-Handling Unit: Experimental Impact Assessment of Indoor Comfort and Patterns of Operating Parameters under Mediterranean Climatic Conditions

Author

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  • Antonio Rosato

    (Department of Architecture and Industrial Design, University of Campania Luigi Vanvitelli, Via San Lorenzo 4, 81031 Aversa, Italy)

  • Francesco Guarino

    (Department of Architecture and Industrial Design, University of Campania Luigi Vanvitelli, Via San Lorenzo 4, 81031 Aversa, Italy)

  • Mohammad El Youssef

    (Department of Architecture and Industrial Design, University of Campania Luigi Vanvitelli, Via San Lorenzo 4, 81031 Aversa, Italy)

  • Alfonso Capozzoli

    (Department of Architecture and Industrial Design, University of Campania Luigi Vanvitelli, Via San Lorenzo 4, 81031 Aversa, Italy)

  • Massimiliano Masullo

    (Department of Architecture and Industrial Design, University of Campania Luigi Vanvitelli, Via San Lorenzo 4, 81031 Aversa, Italy)

  • Luigi Maffei

    (Department of Architecture and Industrial Design, University of Campania Luigi Vanvitelli, Via San Lorenzo 4, 81031 Aversa, Italy)

Abstract

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.

Suggested Citation

  • Antonio Rosato & Francesco Guarino & Mohammad El Youssef & Alfonso Capozzoli & Massimiliano Masullo & Luigi Maffei, 2022. "Faulty Operation of Coils’ and Humidifier Valves in a Typical Air-Handling Unit: Experimental Impact Assessment of Indoor Comfort and Patterns of Operating Parameters under Mediterranean Climatic Cond," Energies, MDPI, vol. 15(18), pages 1-38, September.
  • Handle: RePEc:gam:jeners:v:15:y:2022:i:18:p:6781-:d:917106
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    References listed on IDEAS

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    1. Antonio Rosato & Francesco Guarino & Vincenzo Filomena & Sergio Sibilio & Luigi Maffei, 2020. "Experimental Calibration and Validation of a Simulation Model for Fault Detection of HVAC Systems and Application to a Case Study," Energies, MDPI, vol. 13(15), pages 1-27, August.
    2. Hu, R.L. & Granderson, J. & Auslander, D.M. & Agogino, A., 2019. "Design of machine learning models with domain experts for automated sensor selection for energy fault detection," Applied Energy, Elsevier, vol. 235(C), pages 117-128.
    3. Zhao, Yang & Li, Tingting & Zhang, Xuejun & Zhang, Chaobo, 2019. "Artificial intelligence-based fault detection and diagnosis methods for building energy systems: Advantages, challenges and the future," Renewable and Sustainable Energy Reviews, Elsevier, vol. 109(C), pages 85-101.
    4. Antonio Rosato & Francesco Guarino & Sergio Sibilio & Evgueniy Entchev & Massimiliano Masullo & Luigi Maffei, 2021. "Healthy and Faulty Experimental Performance of a Typical HVAC System under Italian Climatic Conditions: Artificial Neural Network-Based Model and Fault Impact Assessment," Energies, MDPI, vol. 14(17), pages 1-41, August.
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