Dataset concerning the vibration signals from wind turbines in northern Sweden

SND-ID: 2024-248. Version: 1. DOI:


Alternative title

Dataset of A dictionary learning approach to monitoring of wind turbine drivetrain bearings

Creator/Principal investigator(s)

Sergio Martin del Campo Barraza - Luleå University of Technology, Institutionen för system- och rymdteknik orcid

Fredrik Sandin - Luleå University of Technology, Institutionen för system- och rymdteknik orcid

Daniel Strömbergsson - Luleå University of Technology, Institutionen för teknikvetenskap och matematik orcid

Research principal

Luleå University of Technology - Institutionen för system- och rymdteknik rorId


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).

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.

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

Data contains personal data



Method and outcome

Data format / data structure

Data collection
Geographic coverage
Administrative information

Responsible department/unit

Institutionen för system- och rymdteknik


Topic and keywords

Research area

Other electrical engineering, electronic engineering, information engineering (Standard för svensk indelning av forskningsämnen 2011)

Tribology (Standard för svensk indelning av forskningsämnen 2011)


Martin-del-Campo, S., Sandin, F., & Strömbergsson, D. (2021). Dictionary Learning Approach to Monitoring of Wind Turbine Drivetrain Bearings. In International Journal of Computational Intelligence Systems (Vol. 14, Issue 1, pp. 106–121).
URN: urn:nbn:se:ltu:diva-63111

If you have published anything based on these data, please notify us with a reference to your publication(s). If you are responsible for the catalogue entry, you can update the metadata/data description in DORIS.

Published: 2024-07-01