A dataset for fault detection and diagnosis of an air handling unit from a real industrial facility
This dataset was collected for the purpose of applying fault detection and diagnosis (FDD) techniques to real data from an industrial facility. The data for an air handling unit (AHU) is extracted from a building management system (BMS) and aligned with the Project Haystack naming convention. This d...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-06-01
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| Series: | Data in Brief |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S235234092300327X |
| _version_ | 1797798032710828032 |
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| author | Michael Ahern Dominic T.J. O'Sullivan Ken Bruton |
| author_facet | Michael Ahern Dominic T.J. O'Sullivan Ken Bruton |
| author_sort | Michael Ahern |
| collection | DOAJ |
| description | This dataset was collected for the purpose of applying fault detection and diagnosis (FDD) techniques to real data from an industrial facility. The data for an air handling unit (AHU) is extracted from a building management system (BMS) and aligned with the Project Haystack naming convention. This dataset differs from other publicly available datasets in three main ways. Firstly, the dataset does not contain fault detection ground truth. The lack of labelled datasets in the industrial setting is a significant limitation to the application of FDD techniques found in the literature. Secondly, unlike other publicly available datasets that typically record values every 1 min or 5 min, this dataset captures measurements at a lower frequency of every 15 min, which is due to data storage constraints. Thirdly, the dataset contains a myriad of data issues. For example, there are missing features, missing time intervals, and inaccurate data. Therefore, we hope this dataset will encourage the development of robust FDD techniques that are more suitable for real world applications. |
| first_indexed | 2024-03-13T03:58:26Z |
| format | Article |
| id | doaj.art-cf26858be4734842a31904455736aed5 |
| institution | Directory Open Access Journal |
| issn | 2352-3409 |
| language | English |
| last_indexed | 2024-03-13T03:58:26Z |
| publishDate | 2023-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Data in Brief |
| spelling | doaj.art-cf26858be4734842a31904455736aed52023-06-22T05:03:59ZengElsevierData in Brief2352-34092023-06-0148109208A dataset for fault detection and diagnosis of an air handling unit from a real industrial facilityMichael Ahern0Dominic T.J. O'Sullivan1Ken Bruton2Intelligent Efficiency Research Group (IERG), Department of Civil and Environmental Engineering, University College Cork, T12 CY82 Cork, Ireland; MaREI Centre, Environmental Research Institute, University College Cork, T12 CY82 Cork, Ireland; Corresponding author.Intelligent Efficiency Research Group (IERG), Department of Civil and Environmental Engineering, University College Cork, T12 CY82 Cork, Ireland; MaREI Centre, Environmental Research Institute, University College Cork, T12 CY82 Cork, IrelandIntelligent Efficiency Research Group (IERG), Department of Civil and Environmental Engineering, University College Cork, T12 CY82 Cork, Ireland; MaREI Centre, Environmental Research Institute, University College Cork, T12 CY82 Cork, IrelandThis dataset was collected for the purpose of applying fault detection and diagnosis (FDD) techniques to real data from an industrial facility. The data for an air handling unit (AHU) is extracted from a building management system (BMS) and aligned with the Project Haystack naming convention. This dataset differs from other publicly available datasets in three main ways. Firstly, the dataset does not contain fault detection ground truth. The lack of labelled datasets in the industrial setting is a significant limitation to the application of FDD techniques found in the literature. Secondly, unlike other publicly available datasets that typically record values every 1 min or 5 min, this dataset captures measurements at a lower frequency of every 15 min, which is due to data storage constraints. Thirdly, the dataset contains a myriad of data issues. For example, there are missing features, missing time intervals, and inaccurate data. Therefore, we hope this dataset will encourage the development of robust FDD techniques that are more suitable for real world applications.http://www.sciencedirect.com/science/article/pii/S235234092300327XReal dataHVAC dataDetectionTime series |
| spellingShingle | Michael Ahern Dominic T.J. O'Sullivan Ken Bruton A dataset for fault detection and diagnosis of an air handling unit from a real industrial facility Data in Brief Real data HVAC data Detection Time series |
| title | A dataset for fault detection and diagnosis of an air handling unit from a real industrial facility |
| title_full | A dataset for fault detection and diagnosis of an air handling unit from a real industrial facility |
| title_fullStr | A dataset for fault detection and diagnosis of an air handling unit from a real industrial facility |
| title_full_unstemmed | A dataset for fault detection and diagnosis of an air handling unit from a real industrial facility |
| title_short | A dataset for fault detection and diagnosis of an air handling unit from a real industrial facility |
| title_sort | dataset for fault detection and diagnosis of an air handling unit from a real industrial facility |
| topic | Real data HVAC data Detection Time series |
| url | http://www.sciencedirect.com/science/article/pii/S235234092300327X |
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