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  <doc>
    <id>63157</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1</pageFirst>
    <pageLast>19</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>article</type>
    <publisherName>Sage Publications</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Machine learning strategies with ensemble voting for ultrasonic damage detection in composite structures under varying temperature or load conditions</title>
    <abstract language="eng">In recent years, the development of machine learning (ML) techniques has led to significant progress in the field of structural health monitoring with ultrasonic-guided waves. However, a number of challenges still need to be resolved for reliable operation in realistic settings. In this work, we consider the complex problem of experimental damage detection under varying temperature or load conditions where damage locations are not included in the training set. The ML techniques proposed here include supervised and unsupervised methods originally developed for image and time series classification combined with ensemble voting. A performance demonstration of the ML techniques is presented using benchmark datasets from the open-guided waves platform. The unsupervised approach is then applied to a new dataset from an experimental campaign carried out on a composite over-wrapped pressure vessel used for hydrogen storage with real defects. Results show that ensemble voting enables the effective combination of the predictions of multiple transducer pairs, even with a limited number of strong individual classifiers. When applied to unsupervised learning, this returns high accuracy also when real damage over the structure is considered.</abstract>
    <parentTitle language="eng">Structural Health Monitoring</parentTitle>
    <identifier type="doi">10.1177/14759217251333066</identifier>
    <identifier type="issn">1741-3168</identifier>
    <identifier type="urn">urn:nbn:de:kobv:b43-631571</identifier>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="date_peer_review">22.05.2025</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
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    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Oliver Schackmann</author>
    <author>Octavio A. Márquez Reyes</author>
    <author>Vittorio Memmolo</author>
    <author>Daniel Lozano</author>
    <author>Jens Prager</author>
    <author>Jochen Moll</author>
    <author>Peter Kraemer</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Structural-Health-Monitoring</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Ultrasonic guided waves</value>
    </subject>
    <collection role="ddc" number="624">Ingenieurbau</collection>
    <collection role="institutes" number="">8 Zerstörungsfreie Prüfung</collection>
    <collection role="institutes" number="">8.4 Akustische und elektromagnetische Verfahren</collection>
    <collection role="themenfelder" number="">Infrastruktur</collection>
    <collection role="literaturgattung" number="">Verlagsliteratur</collection>
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    <collection role="unnumberedseries" number="">Wissenschaftliche Artikel der BAM</collection>
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    <thesisPublisher>Bundesanstalt für Materialforschung und -prüfung (BAM)</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-bam/files/63157/schackmann-et-al-2025.pdf</file>
  </doc>
  <doc>
    <id>64894</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>608</pageFirst>
    <pageLast>613</pageLast>
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    <edition/>
    <issue/>
    <volume/>
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    <publisherName/>
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    <creatingCorporation>IEEE</creatingCorporation>
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    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Intelligent damage detection in composite pressure vessels under varying environmental and operational conditions</title>
    <abstract language="eng">Despite proven approaches available in the literature, structural health monitoring by ultrasonic guided waves under varying environmental and operational conditions is still challenging. The use of machine learning approaches is discussed in this work, considering the complex problem of experimental damage detection under varying load conditions in a composite overwrapped pressure vessel for hydrogen storage. Specifically, unsupervised methods originally developed for image and time series classification are combined with ensemble voting to conceive reliable damage detection technique. This enables the effective combination of the predictions of multiple transducer pairs, even with a limited number of strong individual classifiers. A performance demonstration of the technique is presented using a real damage scenario dataset.</abstract>
    <parentTitle language="eng">Proceedings of 2025 IEEE 12th International Workshop on Metrology for AeroSpace (MetroAeroSpace)</parentTitle>
    <identifier type="issn">2575-7490</identifier>
    <identifier type="isbn">979-8-3315-0152-5</identifier>
    <identifier type="doi">10.1109/MetroAeroSpace64938.2025.11114628</identifier>
    <enrichment key="eventName">IEEE 12th International Workshop on Metrology for AeroSpace (MetroAeroSpace)</enrichment>
    <enrichment key="eventPlace">Napoli, Italy</enrichment>
    <enrichment key="eventStart">18.06.2025</enrichment>
    <enrichment key="eventEnd">20.06.2025</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="date_peer_review">01.12.2025</enrichment>
    <author>Oliver Schackmann</author>
    <author>Octavio Márquez Reyes</author>
    <author>Vittorio Memmolo</author>
    <author>Daniel Lozano</author>
    <author>Jens Prager</author>
    <author>Jochen Moll</author>
    <author>Peter Kraemer</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Guided ultrasonic waves</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Structural health monitoring</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Artificial intelligence</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Hydrogen storage</value>
    </subject>
    <collection role="ddc" number="621">Angewandte Physik</collection>
    <collection role="institutes" number="">8 Zerstörungsfreie Prüfung</collection>
    <collection role="institutes" number="">8.4 Akustische und elektromagnetische Verfahren</collection>
    <collection role="themenfelder" number="">Energie</collection>
    <collection role="fulltextaccess" number="">Datei im Netzwerk der BAM verfügbar ("Closed Access")</collection>
    <collection role="literaturgattung" number="">Graue Literatur</collection>
    <collection role="themenfelder" number="">Wasserstoff</collection>
  </doc>
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