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            <a:FirstGiven>Sergio</a:FirstGiven>
            <a:LastFamily>Martin del Campo Barraza</a:LastFamily>
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              <r:String>Sergio Martin del Campo Barraza</r:String>
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              <r:String>Fredrik Sandin</r:String>
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            <a:FirstGiven>Daniel</a:FirstGiven>
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              <r:String>Daniel Strömbergsson</r:String>
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      <r:Title>
        <r:String xml:lang="sv">Dataset concerning the vibration signals from wind turbines in northern Sweden</r:String>
        <r:String xml:lang="en">Dataset concerning the vibration signals from wind turbines in northern Sweden</r:String>
      </r:Title>
      <r:Creator>
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          <r:URN>urn:ddi:se.researchdata:2024-248.Individual-0:2.0</r:URN>
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      <r:Publisher>
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          <r:String xml:lang="sv">Luleå tekniska universitet</r:String>
          <r:String xml:lang="en">Luleå University of Technology</r:String>
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      </r:Publisher>
      <r:Publisher>
        <r:PublisherName>
          <r:String xml:lang="sv">Luleå tekniska universitet</r:String>
          <r:String xml:lang="en">Luleå University of Technology</r:String>
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      <r:PublicationDate>
        <r:SimpleDate>2018-09-03</r:SimpleDate>
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        <r:IdentifierContent>10.5878/bcmv-wq08</r:IdentifierContent>
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      <r:Content xml:lang="sv">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.

Datasetet har ursprungligen publicerats i DiVA och flyttades över till SND 2024.</r:Content>
      <r:Content xml:lang="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).

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.</r:Content>
    </r:Abstract>
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        <r:URN>urn:ddi:se.researchdata:2024-248.TopicalCoverage:2.0</r:URN>
        <r:Subject xml:lang="en" controlledVocabularyID="20299" controlledVocabularyName="Standard för svensk indelning av forskningsämnen 2025">Other Electrical Engineering, Electronic Engineering, Information Engineering</r:Subject>
        <r:Subject xml:lang="sv" controlledVocabularyID="20299" controlledVocabularyName="Standard för svensk indelning av forskningsämnen 2025">Annan elektroteknik och elektronik</r:Subject>
        <r:Subject xml:lang="en" controlledVocabularyID="20399" controlledVocabularyName="Standard för svensk indelning av forskningsämnen 2025">Other Mechanical Engineering</r:Subject>
        <r:Subject xml:lang="sv" controlledVocabularyID="20399" controlledVocabularyName="Standard för svensk indelning av forskningsämnen 2025">Annan maskinteknik</r:Subject>
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        <r:URN>urn:ddi:se.researchdata:2024-248.SpatialCoverage:2.0</r:URN>
        <r:Description>
          <r:Content xml:lang="en">Wind farm in Northern Sweden.</r:Content>
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        <r:CountryCode>SE</r:CountryCode>
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