Labelled dataset for Ultra-Low Temperature Freezer to aid dynamic modelling & fault detection and diagnostics
Abstract Ultra-low temperature (ULT) freezers are used to store perishable biological contents and are among the most energy-intensive equipment in laboratory buildings, biobanks, and similar settings. To ensure reliable and efficient operation, it is essential to implement data-driven fault detecti...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2023-12-01
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| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-023-02808-6 |
| _version_ | 1797398247756529664 |
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| author | Tao Huang Silas Nøstvik Peder Bacher Jonas Kjær Jensen Wiebke Brix Markussen Jan Kloppenborg Møller |
| author_facet | Tao Huang Silas Nøstvik Peder Bacher Jonas Kjær Jensen Wiebke Brix Markussen Jan Kloppenborg Møller |
| author_sort | Tao Huang |
| collection | DOAJ |
| description | Abstract Ultra-low temperature (ULT) freezers are used to store perishable biological contents and are among the most energy-intensive equipment in laboratory buildings, biobanks, and similar settings. To ensure reliable and efficient operation, it is essential to implement data-driven fault detection and diagnostic algorithms, along with energy optimization techniques. This study presents labelled and long-term ULT-freezer performance dataset, the first of its kind, derived from 53 ULT freezers featuring two different control strategies. The dataset comprises high-resolution historical operation data spanning up to 10 years. More than 10 attributes are recorded from the freezing chamber and critical locations in the refrigeration systems. The dataset is labelled with regular events, such as door openings, as well as fault events obtained from 46 service reports. A scalable data pipeline, consisting of extraction, transformation, and loading processes, is developed to convert the raw data into a format ready for analysis. The dataset can be utilized to support the development of data-driven models and algorithms that advance the intelligent digital operation of ULT freezers. |
| first_indexed | 2024-03-09T01:21:52Z |
| format | Article |
| id | doaj.art-d22877636d674674be709edd86373b7e |
| institution | Directory Open Access Journal |
| issn | 2052-4463 |
| language | English |
| last_indexed | 2024-03-09T01:21:52Z |
| publishDate | 2023-12-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Data |
| spelling | doaj.art-d22877636d674674be709edd86373b7e2023-12-10T12:06:37ZengNature PortfolioScientific Data2052-44632023-12-0110111210.1038/s41597-023-02808-6Labelled dataset for Ultra-Low Temperature Freezer to aid dynamic modelling & fault detection and diagnosticsTao Huang0Silas Nøstvik1Peder Bacher2Jonas Kjær Jensen3Wiebke Brix Markussen4Jan Kloppenborg Møller5Section for Dynamical Systems, DTU ComputeSection of Thermal Energy, DTU ConstructSection for Dynamical Systems, DTU ComputeSection of Thermal Energy, DTU ConstructDanish Technological InstituteSection for Dynamical Systems, DTU ComputeAbstract Ultra-low temperature (ULT) freezers are used to store perishable biological contents and are among the most energy-intensive equipment in laboratory buildings, biobanks, and similar settings. To ensure reliable and efficient operation, it is essential to implement data-driven fault detection and diagnostic algorithms, along with energy optimization techniques. This study presents labelled and long-term ULT-freezer performance dataset, the first of its kind, derived from 53 ULT freezers featuring two different control strategies. The dataset comprises high-resolution historical operation data spanning up to 10 years. More than 10 attributes are recorded from the freezing chamber and critical locations in the refrigeration systems. The dataset is labelled with regular events, such as door openings, as well as fault events obtained from 46 service reports. A scalable data pipeline, consisting of extraction, transformation, and loading processes, is developed to convert the raw data into a format ready for analysis. The dataset can be utilized to support the development of data-driven models and algorithms that advance the intelligent digital operation of ULT freezers.https://doi.org/10.1038/s41597-023-02808-6 |
| spellingShingle | Tao Huang Silas Nøstvik Peder Bacher Jonas Kjær Jensen Wiebke Brix Markussen Jan Kloppenborg Møller Labelled dataset for Ultra-Low Temperature Freezer to aid dynamic modelling & fault detection and diagnostics Scientific Data |
| title | Labelled dataset for Ultra-Low Temperature Freezer to aid dynamic modelling & fault detection and diagnostics |
| title_full | Labelled dataset for Ultra-Low Temperature Freezer to aid dynamic modelling & fault detection and diagnostics |
| title_fullStr | Labelled dataset for Ultra-Low Temperature Freezer to aid dynamic modelling & fault detection and diagnostics |
| title_full_unstemmed | Labelled dataset for Ultra-Low Temperature Freezer to aid dynamic modelling & fault detection and diagnostics |
| title_short | Labelled dataset for Ultra-Low Temperature Freezer to aid dynamic modelling & fault detection and diagnostics |
| title_sort | labelled dataset for ultra low temperature freezer to aid dynamic modelling fault detection and diagnostics |
| url | https://doi.org/10.1038/s41597-023-02808-6 |
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