Automated fault detection and diagnosis of airflow and refrigerant charge faults in residential HVAC systems using IoT-enabled measurements
| Author(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) |
|---|---|
| Medium: | journal article |
| Language(s): | English |
| Published in: | Science and Technology for the Built Environment, October 2023, n. 9, v. 29 |
| Page(s): | 1-18 |
| DOI: | 10.1080/23744731.2023.2234231 |
- About this
data sheet - Reference-ID
10738866 - Published on:
03/09/2023 - Last updated on:
14/01/2024
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