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Cardoni M, Pau D, Falaschetti L, et al. Synthetic image dataset of shaft junctions inside wind turbines in presence or absence of oil leaks. Data Brief. 2021;39:107538doi: 10.1016/j.dib.2021.107538.
Cardoni, M., Pau, D., Falaschetti, L., Turchetti, C., & Lattuada, M. (2021). Synthetic image dataset of shaft junctions inside wind turbines in presence or absence of oil leaks. Data in brief, 39107538. https://doi.org/10.1016/j.dib.2021.107538
Cardoni, Matteo, et al. "Synthetic image dataset of shaft junctions inside wind turbines in presence or absence of oil leaks." Data in brief vol. 39 (2021): 107538. doi: https://doi.org/10.1016/j.dib.2021.107538
Cardoni M, Pau D, Falaschetti L, Turchetti C, Lattuada M. Synthetic image dataset of shaft junctions inside wind turbines in presence or absence of oil leaks. Data Brief. 2021 Nov 03;39:107538. doi: 10.1016/j.dib.2021.107538. eCollection 2021 Dec. PMID: 34815989; PMCID: PMC8591346.
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Data Brief. 2021 Nov 03;39:107538. doi: 10.1016/j.dib.2021.107538. eCollection 2021 Dec.

Synthetic image dataset of shaft junctions inside wind turbines in presence or absence of oil leaks.

Data in brief

Matteo Cardoni, Danilo Pau, Laura Falaschetti, Claudio Turchetti, Marco Lattuada

Affiliations

  1. STMicroelectronics, via C. Olivetti 2, Agrate Brianza, I-20864, Italy.
  2. Department of Information Engineering, Università Politecnica delle Marche, Ancona, Via Brecce Bianche 12, Ancona I-60131, Italy.
  3. STMicroelectronics, via Tolomeo 1, Cornaredo, I-20010, Italy.

PMID: 34815989 PMCID: PMC8591346 DOI: 10.1016/j.dib.2021.107538

Abstract

This paper presents a dataset of images generated via 3D graphics rendering. The dataset is composed by pictures of the junction between the high-speed shaft and the external bracket of the power generator inside a wind turbine cabin, in presence and absence of oil leaks. Oil leak occurrence is an anomaly that can verify in a zone of interest of the junction. Since the wind turbines industry is becoming more and more important, turbines maintenance is growing in importance accordingly. In this context a dataset, as we propose, can be used, for example, to design machine learning algorithms for predictive maintenance. The renderings have been produced, from various framings and various leaks shapes and colors, using the rendering engine Keyshot9. Subsequent preprocessing has been performed with Matlab, including images grayscale conversion and image binarization. Finally, data augmentation has been implemented in Python, and it can be easily extended/customized for realizing any further processing. The Matlab and Python source codes are also provided. To the authors' knowledge, there are no other public available datasets on this topic.

© 2021 Published by Elsevier Inc.

Keywords: Anomaly detection; Image classification; Image dataset; Machine learning; Oil leaks; Wind turbines

Conflict of interest statement

The authors declare that they have no known competing financial interests or personal relationships which have, or could be perceived to have, influenced the work reported in this article.

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