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Dataset of low global warming potential refrigerant refrigeration system for fault detection and diagnostics
HVAC and refrigeration system fault detection and diagnostics (FDD) has attracted extensive studies for decades; however, FDD of supermarket refrigeration systems has not gained significant attention. Supermarkets consume around 50 kWh/ft(2) of electricity annually. The biggest consumer of energy in...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8159987/ https://www.ncbi.nlm.nih.gov/pubmed/34045474 http://dx.doi.org/10.1038/s41597-021-00927-6 |
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author | Sun, Jian Im, Piljae Bae, Yeonjin Munk, Jeff Kuruganti, Teja Fricke, Brian |
author_facet | Sun, Jian Im, Piljae Bae, Yeonjin Munk, Jeff Kuruganti, Teja Fricke, Brian |
author_sort | Sun, Jian |
collection | PubMed |
description | HVAC and refrigeration system fault detection and diagnostics (FDD) has attracted extensive studies for decades; however, FDD of supermarket refrigeration systems has not gained significant attention. Supermarkets consume around 50 kWh/ft(2) of electricity annually. The biggest consumer of energy in a supermarket is its refrigeration system, which accounts for 40%–60% of its total electricity usage and is equivalent to about 2%–3% of the total energy consumed by commercial buildings in the United States. Also, the supermarket refrigeration system is one of the biggest consumers of refrigerants. Reducing refrigerant usage or using environmentally friendly alternatives can result in significant climate benefits. A challenge is the lack of publicly available data sets to benchmark the system performance and record the faulted performance. This paper identifies common faults of supermarket refrigeration systems and conducts an experimental study to collect the faulted performance data and analyze these faults. This work provides a foundation for future research on the development of FDD methods and field automated FDD implementation. |
format | Online Article Text |
id | pubmed-8159987 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-81599872021-06-10 Dataset of low global warming potential refrigerant refrigeration system for fault detection and diagnostics Sun, Jian Im, Piljae Bae, Yeonjin Munk, Jeff Kuruganti, Teja Fricke, Brian Sci Data Data Descriptor HVAC and refrigeration system fault detection and diagnostics (FDD) has attracted extensive studies for decades; however, FDD of supermarket refrigeration systems has not gained significant attention. Supermarkets consume around 50 kWh/ft(2) of electricity annually. The biggest consumer of energy in a supermarket is its refrigeration system, which accounts for 40%–60% of its total electricity usage and is equivalent to about 2%–3% of the total energy consumed by commercial buildings in the United States. Also, the supermarket refrigeration system is one of the biggest consumers of refrigerants. Reducing refrigerant usage or using environmentally friendly alternatives can result in significant climate benefits. A challenge is the lack of publicly available data sets to benchmark the system performance and record the faulted performance. This paper identifies common faults of supermarket refrigeration systems and conducts an experimental study to collect the faulted performance data and analyze these faults. This work provides a foundation for future research on the development of FDD methods and field automated FDD implementation. Nature Publishing Group UK 2021-05-27 /pmc/articles/PMC8159987/ /pubmed/34045474 http://dx.doi.org/10.1038/s41597-021-00927-6 Text en © This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply 2021 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) applies to the metadata files associated with this article. |
spellingShingle | Data Descriptor Sun, Jian Im, Piljae Bae, Yeonjin Munk, Jeff Kuruganti, Teja Fricke, Brian Dataset of low global warming potential refrigerant refrigeration system for fault detection and diagnostics |
title | Dataset of low global warming potential refrigerant refrigeration system for fault detection and diagnostics |
title_full | Dataset of low global warming potential refrigerant refrigeration system for fault detection and diagnostics |
title_fullStr | Dataset of low global warming potential refrigerant refrigeration system for fault detection and diagnostics |
title_full_unstemmed | Dataset of low global warming potential refrigerant refrigeration system for fault detection and diagnostics |
title_short | Dataset of low global warming potential refrigerant refrigeration system for fault detection and diagnostics |
title_sort | dataset of low global warming potential refrigerant refrigeration system for fault detection and diagnostics |
topic | Data Descriptor |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8159987/ https://www.ncbi.nlm.nih.gov/pubmed/34045474 http://dx.doi.org/10.1038/s41597-021-00927-6 |
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