Automated fault detection and diagnosis of airflow and refrigerant charge faults in residential HVAC systems using IoT-enabled measurements

Auteur(s):	Kevwe Andrew Ejenakevwe (School of Aerospace and Mechanical Engineering, University of Oklahoma, Norman, Oklahoma, USA) Junke Wang (School of Aerospace and Mechanical Engineering, University of Oklahoma, Norman, Oklahoma, USA) Yilin Jiang (School of Aerospace and Mechanical Engineering, University of Oklahoma, Norman, Oklahoma, USA) Li Song (School of Aerospace and Mechanical Engineering, University of Oklahoma, Norman, Oklahoma, USA) Roshan L. Kini (Energy and Environment Directorate, Pacific Northwest National Laboratory, Richland, Washington, USA)
Médium:	article de revue
Langue(s):	anglais
Publié dans:	Science and Technology for the Built Environment, octobre 2023, n. 9, v. 29
Page(s):	1-18
DOI:	10.1080/23744731.2023.2234231

Structurae ne peut pas vous offrir cette publication en texte intégral pour l'instant. Le texte intégral est accessible chez l'éditeur. DOI: 10.1080/23744731.2023.2234231.

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