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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)Data set, Primary dataAlternative title
Dataset of A dictionary learning approach to monitoring of wind turbine drivetrain bearings (English)
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
2018.
Version
1.0
Keywords [en]
dataset, wind turbine, condition monitoring
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering Tribology (Interacting Surfaces including Friction, Lubrication and Wear)
Research subject
Industrial Electronics; Machine Elements
Identifiers
URN: urn:nbn:se:ltu:diva-70730OAI: oai:DiVA.org:ltu-70730DiVA, id: diva2:1244889
Available from: 2018-09-03 Created: 2018-09-03 Last updated: 2019-05-14Bibliographically approved

Open Access in DiVA

Turbine1.csv(872485 kB)249 downloads
File information
File name DATASET02.csvFile size 872485 kBChecksum SHA-512
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Type datasetMimetype text/csv
Turbine2.csv(880493 kB)136 downloads
File information
File name DATASET03.csvFile size 880493 kBChecksum SHA-512
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Type datasetMimetype text/csv
Turbine3.csv(791568 kB)129 downloads
File information
File name DATASET04.csvFile size 791568 kBChecksum SHA-512
c479341f553c0eb1cfb49d13aecbeb81934a3da18df15ef6dae28ca53c52905154bfbe7a6fa04d81689ce3ea7e351a719f296fb6278148e0072149a43aa68a73
Type datasetMimetype text/csv
Turbine4.csv(787720 kB)126 downloads
File information
File name DATASET05.csvFile size 787720 kBChecksum SHA-512
4c0d7813ddd08a65ac14c7deb8590e4ace90de403be147086f479c7e1a8326ed6ff145b0a9bd33b3ec8beb83dc805a6bab9fa50fc94f67f0d2b49fe93d49cba5
Type datasetMimetype text/csv
Turbine5.csv(873751 kB)123 downloads
File information
File name DATASET06.csvFile size 873751 kBChecksum SHA-512
77454dc616151f2c48e5cbfb201bb15daf264302e48f7a5db83abf468fe0670e074cf0c3d239ada8aa5afb6fbe29b3679ef893e967e961d1b710e86b5de81a1b
Type datasetMimetype text/csv
Turbine6.csv(870916 kB)146 downloads
File information
File name DATASET07.csvFile size 870916 kBChecksum SHA-512
32b5d864e63fa24f66e0c80f4dc82863e2a8b77d15689986487ff7a173c9353f5bab0d9fcce7344b1c99a295464fb424db622b50d3c63863e1daee56ae4d8b10
Type datasetMimetype text/csv

Authority records BETA

Martin del Campo Barraza, SergioSandin, FredrikStrömbergsson, Daniel

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Martin del Campo Barraza, SergioSandin, FredrikStrömbergsson, Daniel
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Embedded Internet Systems LabMachine Elements
Other Electrical Engineering, Electronic Engineering, Information EngineeringTribology (Interacting Surfaces including Friction, Lubrication and Wear)
Martin del Campo Barraza, S., Sandin, F. & Strömbergsson, D. (2017). A dictionary learning approach to monitoring of wind turbine drivetrain bearings. Mechanical systems and signal processing

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CiteExportLink to record
Permanent link

Direct link
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Citation style
  • apa
  • harvard1
  • 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