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Dataset concerning the vibration signals from wind turbines in northern Sweden
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.ORCID iD: 0000-0001-6099-3882
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.ORCID iD: 0000-0001-5662-825X
Luleå University of Technology, Department of Engineering Sciences and Mathematics, Machine Elements.ORCID iD: 0000-0002-7970-8655
2018 (English)Other (Other academic)
Resource type
Text
Physical description [en]

There are six files, which contains the vibration data from each of the six wind turbines. Within each file, each row corresponds to a different measurement. Furthermore, the first column represents the time expressed in years since the vibration data started to be recorded. The second column is the speed expressed in cycles per minute. The remaining columns are the vibration signal time series expressed in Gs.

Abstract [en]

In the manuscript, we investigate condition monitoring methods based on unsupervised dictionary learning.

The dataset includes the raw time-domain vibration signals from six turbines within the same wind farm (near geographical location). All the wind turbines are of the same type and possess a three-stage gearbox. All measurement data corresponds to the axial direction of an accelerometer mounted on the housing of the output shaft bearing of each turbine. The sampling rate is 12.8 kilosamples/second and each signal segment is 1.28 seconds long (16384 samples).

Place, publisher, year, pages
2018.
Keywords [en]
dataset, wind turbine, condition monitoring
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering Other Mechanical Engineering
Research subject
Machine Elements; Machine Learning
Identifiers
URN: urn:nbn:se:ltu:diva-113477DOI: 10.5878/bcmv-wq08OAI: oai:DiVA.org:ltu-113477DiVA, id: diva2:1971118
Note

The dataset was originally published in DiVA and moved to SND in 2024.

Available from: 2018-09-03 Created: 2025-06-17 Last updated: 2025-10-21Bibliographically approved

Open Access in DiVA

No full text in DiVA

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Authority records

Martin del Campo Barraza, SergioSandin, FredrikStrömbergsson, Daniel

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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf