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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Bibliographic Details
Published in:IEEE access 2023-01, Vol.11, p.1-1
Main Authors: Jovicic, Eda, Primorac, Daria, Cupic, Marko, Jovic, Alan
Format: Article
Language:English
Subjects:
Availability
Data models
Datasets
Deep learning
deep learning (DL)
Energy industry
Energy management
energy sector
Fault detection
Induction motors
Industrial Internet of Things
Industries
Machine learning
machine learning (ML)
Predictive maintenance
predictive maintenance (PdM)
Repositories
Wind turbines
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Summary: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 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 available 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.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2023.3295113