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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: 2023-09-05Bibliographically approved

Open Access in DiVA

Turbine1.csv(872485 kB)1684 downloads
File information
File name DATASET02.csvFile size 872485 kBChecksum SHA-512
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Type datasetMimetype text/csv
Turbine2.csv(880493 kB)1991 downloads
File information
File name DATASET03.csvFile size 880493 kBChecksum SHA-512
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Type datasetMimetype text/csv
Turbine3.csv(791568 kB)744 downloads
File information
File name DATASET04.csvFile size 791568 kBChecksum SHA-512
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Type datasetMimetype text/csv
Turbine4.csv(787720 kB)732 downloads
File information
File name DATASET05.csvFile size 787720 kBChecksum SHA-512
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Type datasetMimetype text/csv
Turbine5.csv(873751 kB)1414 downloads
File information
File name DATASET06.csvFile size 873751 kBChecksum SHA-512
77454dc616151f2c48e5cbfb201bb15daf264302e48f7a5db83abf468fe0670e074cf0c3d239ada8aa5afb6fbe29b3679ef893e967e961d1b710e86b5de81a1b
Type datasetMimetype text/csv
Turbine6.csv(870916 kB)824 downloads
File information
File name DATASET07.csvFile size 870916 kBChecksum SHA-512
32b5d864e63fa24f66e0c80f4dc82863e2a8b77d15689986487ff7a173c9353f5bab0d9fcce7344b1c99a295464fb424db622b50d3c63863e1daee56ae4d8b10
Type datasetMimetype text/csv

Authority records

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, S., Sandin, F. & Strömbergsson, D. (2021). Dictionary Learning Approach to Monitoring of Wind Turbine Drivetrain Bearings. International Journal of Computational Intelligence Systems, 14(1), 106-121

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CiteExportLink to record
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Citation style
  • apa
  • ieee
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  • de-DE
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Output format
  • html
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