A dataset for fault detection and diagnosis of an air handling unit from a real industrial facility. Ahern, M., O'Sullivan, D. T. J., & Bruton, K. Data in Brief, 48:109208, June, 2023. ![A dataset for fault detection and diagnosis of an air handling unit from a real industrial facility [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex 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.
@article{ahern_dataset_2023,
title = {A dataset for fault detection and diagnosis of an air handling unit from a real industrial facility},
volume = {48},
issn = {2352-3409},
url = {https://www.sciencedirect.com/science/article/pii/S235234092300327X},
doi = {10.1016/j.dib.2023.109208},
abstract = {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.},
urldate = {2023-10-04},
journal = {Data in Brief},
author = {Ahern, Michael and O'Sullivan, Dominic T. J. and Bruton, Ken},
month = jun,
year = {2023},
keywords = {Detection, HVAC data, Real data, Time series},
pages = {109208},
}
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