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CREAM, a component level coffeemaker electrical activity measurement dataset
Monitoring the internal conditions of a machine is essential to increase its production efficiency and to reduce energy waste. Non-intrusive condition monitoring techniques, such as analysing electrical signals, provide insights by disaggregating a composite signal of a machine as a whole into the i...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7747629/ https://www.ncbi.nlm.nih.gov/pubmed/33335093 http://dx.doi.org/10.1038/s41597-020-00767-w |
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author | Jorde, Daniel Kriechbaumer, Thomas Berger, Tim Zitzlsperger, Stefan Jacobsen, Hans-Arno |
author_facet | Jorde, Daniel Kriechbaumer, Thomas Berger, Tim Zitzlsperger, Stefan Jacobsen, Hans-Arno |
author_sort | Jorde, Daniel |
collection | PubMed |
description | Monitoring the internal conditions of a machine is essential to increase its production efficiency and to reduce energy waste. Non-intrusive condition monitoring techniques, such as analysing electrical signals, provide insights by disaggregating a composite signal of a machine as a whole into the individual components to determine their states. Developing and evaluating new algorithms for condition monitoring and maintenance-related analysis tasks require a fully-labelled dataset for a machine, which comprises standard industrial components that are triggered following a typical manufacturing process to produce goods. For this purpose, we introduce CREAM, a component level electrical measurement dataset for two industrial-grade coffeemakers, simulating industrial processes. The dataset contains continuous voltage and current measurements provided at 6400 samples per second, as well as the product and maintenance-related event labels, such as 370600 expert-labelled component-level electrical events, 1734 product ones and 3646 maintenance ones. CREAM provides fully-labelled ground-truth to establish a benchmark and comparative studies of manufacturing-related analysis in a controlled and transparent environment. |
format | Online Article Text |
id | pubmed-7747629 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-77476292020-12-21 CREAM, a component level coffeemaker electrical activity measurement dataset Jorde, Daniel Kriechbaumer, Thomas Berger, Tim Zitzlsperger, Stefan Jacobsen, Hans-Arno Sci Data Data Descriptor Monitoring the internal conditions of a machine is essential to increase its production efficiency and to reduce energy waste. Non-intrusive condition monitoring techniques, such as analysing electrical signals, provide insights by disaggregating a composite signal of a machine as a whole into the individual components to determine their states. Developing and evaluating new algorithms for condition monitoring and maintenance-related analysis tasks require a fully-labelled dataset for a machine, which comprises standard industrial components that are triggered following a typical manufacturing process to produce goods. For this purpose, we introduce CREAM, a component level electrical measurement dataset for two industrial-grade coffeemakers, simulating industrial processes. The dataset contains continuous voltage and current measurements provided at 6400 samples per second, as well as the product and maintenance-related event labels, such as 370600 expert-labelled component-level electrical events, 1734 product ones and 3646 maintenance ones. CREAM provides fully-labelled ground-truth to establish a benchmark and comparative studies of manufacturing-related analysis in a controlled and transparent environment. Nature Publishing Group UK 2020-12-17 /pmc/articles/PMC7747629/ /pubmed/33335093 http://dx.doi.org/10.1038/s41597-020-00767-w Text en © The Author(s) 2020 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/. The Creative Commons Public Domain Dedication waiver http://creativecommons.org/publicdomain/zero/1.0/ applies to the metadata files associated with this article. |
spellingShingle | Data Descriptor Jorde, Daniel Kriechbaumer, Thomas Berger, Tim Zitzlsperger, Stefan Jacobsen, Hans-Arno CREAM, a component level coffeemaker electrical activity measurement dataset |
title | CREAM, a component level coffeemaker electrical activity measurement dataset |
title_full | CREAM, a component level coffeemaker electrical activity measurement dataset |
title_fullStr | CREAM, a component level coffeemaker electrical activity measurement dataset |
title_full_unstemmed | CREAM, a component level coffeemaker electrical activity measurement dataset |
title_short | CREAM, a component level coffeemaker electrical activity measurement dataset |
title_sort | cream, a component level coffeemaker electrical activity measurement dataset |
topic | Data Descriptor |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7747629/ https://www.ncbi.nlm.nih.gov/pubmed/33335093 http://dx.doi.org/10.1038/s41597-020-00767-w |
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