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  <doc>
    <id>54504</id>
    <completedYear/>
    <publishedYear>2021</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>researchdata</type>
    <publisherName>Zenodo</publisherName>
    <publisherPlace>Geneva</publisherPlace>
    <creatingCorporation>Bundesanstalt für Materialforschung und -prüfung (BAM)</creatingCorporation>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Dataset on full ultrasonic guided wavefield measurements of a CFRP plate with fully bonded and partially debonded omega stringer</title>
    <abstract language="eng">The fourth dataset dedicated to the Open Guided Waves platform presented in this work aims at a carbon fiber composite plate with an additional omega stringer at constant temperature conditions. The dataset provides full ultrasonic guided wavefields. &#13;
&#13;
A chirp signal in the frequency range 20-500 kHz and Hann windowed tone-burst signal with 5 cycles and carrier frequencies of 16.5 kHz, 50 kHz, 100 kHz, 200 kHz and 300kHz are used to excite the wave. The piezoceramic actuator used for this purpose is attached to the center of the stringer side surface of the core plate.&#13;
&#13;
&#13;
Three scenarios are provided with this setup: (1) wavefield measurements without damage, (2) wavefield measurements with a local stringer debond and (3) wavefield measurements with a large stringer debond. The defects were caused by impacts performed from the backside of the plate. As result, the stringer feet debonds locally which was verified with conventional ultrasound measurements.&#13;
&#13;
The dataset can be used for benchmarking purposes of various signal processing methods for damage imaging.&#13;
&#13;
The detailed description of the dataset is published in Data in Brief Journal.</abstract>
    <identifier type="doi">10.5281/zenodo.5105861</identifier>
    <enrichment key="ScientificResourceTypeGeneral">Datensatz</enrichment>
    <enrichment key="ScientificDateCreatedStart">2021</enrichment>
    <enrichment key="ScientificGeolocation">Institute of Fluid-Flow Machinery Polish Academy of Sciences, Generała Józefa Fiszera 14, 80-231 Gdańsk, Poland</enrichment>
    <enrichment key="ScientificNote">The detailed description of the dataset is published in the journal "Data in Brief".</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="RelatedIdentifier">https://opus4.kobv.de/opus4-bam/frontdoor/index/index/docId/54524</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">false</enrichment>
    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>P. Kudela</author>
    <author>M. Radzienski</author>
    <author>M. Moix-Bonet</author>
    <author>C. Willberg</author>
    <author>Yevgeniya Lugovtsova</author>
    <author>Jannis Bulling</author>
    <author>K. Tschöke</author>
    <author>J. Moll</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Open data</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Ultrasonic guided waves</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Experimental data</value>
    </subject>
    <collection role="ddc" number="543">Analytische Chemie</collection>
    <collection role="institutes" number="">8 Zerstörungsfreie Prüfung</collection>
    <collection role="institutes" number="">8.4 Akustische und elektromagnetische Verfahren</collection>
    <collection role="themenfelder" number="">Chemie und Prozesstechnik</collection>
    <collection role="fulltextaccess" number="">Datei für die Öffentlichkeit verfügbar ("Open Access")</collection>
    <collection role="literaturgattung" number="">Graue Literatur</collection>
    <collection role="unnumberedseries" number="">Forschungsdatensätze der BAM</collection>
  </doc>
  <doc>
    <id>54524</id>
    <completedYear/>
    <publishedYear>2022</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1</pageFirst>
    <pageLast>13</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>42</volume>
    <type>article</type>
    <publisherName>Elsevier</publisherName>
    <publisherPlace>Amsterdam</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Dataset on full ultrasonic guided wavefield measurements of a CFRP plate with fully bonded and partially debonded omega stringer</title>
    <abstract language="eng">The fourth dataset dedicated to the Open Guided Waves platform presented in this work aims at a carbon fiber composite plate with an additional omega stringer at constant temperature conditions. The dataset provides full ultrasonic guided wavefields. Two types of signals were used for guided wave excitation, namely chirp signal and tone-burst signal. The chirp signal had a frequency range of 20-500kHz. The tone-burst signals had a form of sine modulated by Hann window with 5 cycles and carrier frequencies 16.5kHz, 50kHz, 100kHz, 200kHz, 300kHz. The piezoceramic actuator used for this purpose was attached to the center of the stringer side surface of the core plate. Three scenarios are provided with this setup: (1) wavefield measurements without damage, (2) wavefield measurements with a local stringer debond and (3) wavefield measurements with a large stringer debond. The defects were caused by impacts performed from the backside of the plate. As result, the stringer feet debonds locally which was verified with conventional ultrasound measurements.</abstract>
    <parentTitle language="eng">Data in brief</parentTitle>
    <identifier type="doi">10.1016/j.dib.2022.108078</identifier>
    <identifier type="issn">2352-3409</identifier>
    <identifier type="urn">urn:nbn:de:kobv:b43-545242</identifier>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="RelatedIdentifier">https://opus4.kobv.de/opus4-bam/frontdoor/index/index/docId/54504</enrichment>
    <enrichment key="date_peer_review">20.04.2022</enrichment>
    <licence>Creative Commons - CC BY-NC-ND - Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International</licence>
    <author>P. Kudela</author>
    <author>M. Radzienski</author>
    <author>M. Moix-Bonet</author>
    <author>C. Willberg</author>
    <author>Yevgeniya Lugovtsova</author>
    <author>Jannis Bulling</author>
    <author>K. Tschöke</author>
    <author>J. Moll</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Lamb waves</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Composite panel</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Impact damage</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Damage detection</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Scanning laser Doppler vibrometry</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Structural health monitoring</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Non-destructive evaluation</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Open data</value>
    </subject>
    <collection role="ddc" number="543">Analytische Chemie</collection>
    <collection role="institutes" number="">8 Zerstörungsfreie Prüfung</collection>
    <collection role="institutes" number="">8.4 Akustische und elektromagnetische Verfahren</collection>
    <collection role="themenfelder" number="">Chemie und Prozesstechnik</collection>
    <collection role="literaturgattung" number="">Verlagsliteratur</collection>
    <collection role="fulltextaccess" number="">Datei für die Öffentlichkeit verfügbar ("Open Access")</collection>
    <collection role="unnumberedseries" number="">Wissenschaftliche Artikel der BAM</collection>
    <thesisPublisher>Bundesanstalt für Materialforschung und -prüfung (BAM)</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-bam/files/54524/1-s2.0-S235234092200289X-main.pdf</file>
  </doc>
  <doc>
    <id>54206</id>
    <completedYear/>
    <publishedYear>2022</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1</pageFirst>
    <pageLast>19</pageLast>
    <pageNumber/>
    <edition/>
    <issue>1</issue>
    <volume>22</volume>
    <type>article</type>
    <publisherName>MDPI</publisherName>
    <publisherPlace>Basel</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Towards interpretable machine learning for automated damage detection based on ultrasonic guided waves</title>
    <abstract language="eng">Data-driven analysis for damage assessment has a large potential in structural health monitoring (SHM) systems, where sensors are permanently attached to the structure, enabling continuous and frequent measurements. In this contribution, we propose a machine learning (ML) approach for automated damage detection, based on an ML toolbox for industrial condition monitoring. The toolbox combines multiple complementary algorithms for feature extraction and selection and automatically chooses the best combination of methods for the dataset at hand. Here, this toolbox is applied to a guided wave-based SHM dataset for varying temperatures and damage locations, which is freely available on the Open Guided Waves platform. A classification rate of 96.2% is achieved, demonstrating reliable and automated damage detection. Moreover, the ability of the ML model to identify a damaged structure at untrained damage locations and temperatures is demonstrated.</abstract>
    <parentTitle language="eng">Sensors</parentTitle>
    <identifier type="doi">10.3390/s22010406</identifier>
    <identifier type="issn">1424-8220</identifier>
    <identifier type="urn">urn:nbn:de:kobv:b43-542060</identifier>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="date_peer_review">21.01.2022</enrichment>
    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>C. Schnur</author>
    <author>P. Goodarzi</author>
    <author>Yevgeniya Lugovtsova</author>
    <author>Jannis Bulling</author>
    <author>Jens Prager</author>
    <author>K. Tschöke</author>
    <author>J. Moll</author>
    <author>A. Schütze</author>
    <author>T. Schneider</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Composite structures</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Structural health monitoring</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Carbon fibre-reinforced plastic</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Interpretable machine learning</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Automotive industry</value>
    </subject>
    <collection role="ddc" number="543">Analytische Chemie</collection>
    <collection role="institutes" number="">8 Zerstörungsfreie Prüfung</collection>
    <collection role="institutes" number="">8.4 Akustische und elektromagnetische Verfahren</collection>
    <collection role="themenfelder" number="">Chemie und Prozesstechnik</collection>
    <collection role="literaturgattung" number="">Verlagsliteratur</collection>
    <collection role="fulltextaccess" number="">Datei für die Öffentlichkeit verfügbar ("Open Access")</collection>
    <collection role="unnumberedseries" number="">Wissenschaftliche Artikel der BAM</collection>
    <thesisPublisher>Bundesanstalt für Materialforschung und -prüfung (BAM)</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-bam/files/54206/Schnur - 2022 - Machine Learning on OGW Dataset (MDPI Sensors).pdf</file>
  </doc>
</export-example>
