Publicly Available Datasets for Predictive Maintenance in the Energy Sector: A Review
Predictive maintenance (PdM) uses statistical and machine learning methods to detect and predict the onset of faults. PdM is often used in industrial IoT settings in the energy sector, where research works usually consider specific types of faults depending on the application. However, since PdM is...
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10182232/ |
| _version_ | 1797772620352978944 |
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| author | Eda Jovicic Daria Primorac Marko Cupic Alan Jovic |
| author_facet | Eda Jovicic Daria Primorac Marko Cupic Alan Jovic |
| author_sort | Eda Jovicic |
| collection | DOAJ |
| description | Predictive maintenance (PdM) uses statistical and machine learning methods to detect and predict the onset of faults. PdM is often used in industrial IoT settings in the energy sector, where research works usually consider specific types of faults depending on the application. However, since PdM is mainly data-driven and needs to work in real time, the public availability of datasets is required in order to build efficient and effective models applicable across multiple domains. Unlike methods, the publicly available datasets obtained from sensors in the energy sector have not been properly reviewed or categorized. In this work, we consider five subsectors of the energy sector: wind, solar, oil & gas, diesel & thermal, and electrical power grid. We provide a detailed description of the properties of the publicly available PdM datasets in these subsectors. The review of the datasets is conducted on a number of scientific and commercial repositories: IEEE DataPort, UCI Machine Learning Repository, Kaggle, EDP, and Mendeley Data. The datasets are graded into three categories according to objective criteria. We also provide references to significant related research work that uses the considered datasets. The observed challenges in using the datasets in this field are thoroughly discussed. We find that there is a troublesome scarcity of publicly available datasets in the energy sector, more so of data coming from real, non-simulated sources. Three datasets, 3W (oil & gas), EDP-WT (wind), and OREC (wind) stand out as highly valuable for researchers in this field. |
| first_indexed | 2024-03-12T21:54:34Z |
| format | Article |
| id | doaj.art-db074f05ffa441ccbdf2c8e7107b237e |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-12T21:54:34Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-db074f05ffa441ccbdf2c8e7107b237e2023-07-25T23:00:13ZengIEEEIEEE Access2169-35362023-01-0111735057352010.1109/ACCESS.2023.329511310182232Publicly Available Datasets for Predictive Maintenance in the Energy Sector: A ReviewEda Jovicic0https://orcid.org/0000-0001-6166-2225Daria Primorac1https://orcid.org/0000-0003-0389-8664Marko Cupic2Alan Jovic3https://orcid.org/0000-0003-3821-8091Faculty of Electrical Engineering and Computing, University of Zagreb, Zagreb, CroatiaIndependent Researcher, Zurich, SwitzerlandFaculty of Electrical Engineering and Computing, University of Zagreb, Zagreb, CroatiaFaculty of Electrical Engineering and Computing, University of Zagreb, Zagreb, CroatiaPredictive maintenance (PdM) uses statistical and machine learning methods to detect and predict the onset of faults. PdM is often used in industrial IoT settings in the energy sector, where research works usually consider specific types of faults depending on the application. However, since PdM is mainly data-driven and needs to work in real time, the public availability of datasets is required in order to build efficient and effective models applicable across multiple domains. Unlike methods, the publicly available datasets obtained from sensors in the energy sector have not been properly reviewed or categorized. In this work, we consider five subsectors of the energy sector: wind, solar, oil & gas, diesel & thermal, and electrical power grid. We provide a detailed description of the properties of the publicly available PdM datasets in these subsectors. The review of the datasets is conducted on a number of scientific and commercial repositories: IEEE DataPort, UCI Machine Learning Repository, Kaggle, EDP, and Mendeley Data. The datasets are graded into three categories according to objective criteria. We also provide references to significant related research work that uses the considered datasets. The observed challenges in using the datasets in this field are thoroughly discussed. We find that there is a troublesome scarcity of publicly available datasets in the energy sector, more so of data coming from real, non-simulated sources. Three datasets, 3W (oil & gas), EDP-WT (wind), and OREC (wind) stand out as highly valuable for researchers in this field.https://ieeexplore.ieee.org/document/10182232/Datasetsdeep learningmachine learningpredictive maintenance (PdM)energy sector |
| spellingShingle | Eda Jovicic Daria Primorac Marko Cupic Alan Jovic Publicly Available Datasets for Predictive Maintenance in the Energy Sector: A Review IEEE Access Datasets deep learning machine learning predictive maintenance (PdM) energy sector |
| title | Publicly Available Datasets for Predictive Maintenance in the Energy Sector: A Review |
| title_full | Publicly Available Datasets for Predictive Maintenance in the Energy Sector: A Review |
| title_fullStr | Publicly Available Datasets for Predictive Maintenance in the Energy Sector: A Review |
| title_full_unstemmed | Publicly Available Datasets for Predictive Maintenance in the Energy Sector: A Review |
| title_short | Publicly Available Datasets for Predictive Maintenance in the Energy Sector: A Review |
| title_sort | publicly available datasets for predictive maintenance in the energy sector a review |
| topic | Datasets deep learning machine learning predictive maintenance (PdM) energy sector |
| url | https://ieeexplore.ieee.org/document/10182232/ |
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