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    <pageLast>9</pageLast>
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    <edition/>
    <issue>e2958</issue>
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    <title language="eng">Infrared Thermography of Turbulence Patterns of Operational Wind Turbine Rotor Blades Supported With High‐Resolution Photography: KI‐VISIR Dataset</title>
    <abstract language="eng">With increasing wind energy capacity and installation of wind turbines, new inspection techniques are being explored to examine wind turbine rotor blades, especially during operation. A common result of surface damage phenomena (such as leading edge erosion) is the premature transition of laminar to turbulent flow on the surface of rotor blades. In the KI-VISIR (Künstliche Intelligenz Visuell und Infrarot Thermografie—Artificial Intelligence-Visual and Infrared Thermography) project, infrared thermography is used as an inspection tool to capture so-called thermal turbulence patterns (TTPs) that result from such surface contamination or damage. To complement the thermographic inspections, high-resolution photography is performed to visualise, in detail, the sites where these turbulence patterns initiate. A convolutional neural network (CNN) was developed and used to detect and localise turbulence patterns. A unique dataset combining the thermograms and visual images of operational wind turbine rotor blades has been provided, along with the simplified annotations for the turbulence patterns. Additional tools are available to allow users to use the data requiring only basic Python programming skills.</abstract>
    <parentTitle language="eng">Wind Energy</parentTitle>
    <identifier type="doi">10.1002/we.2958</identifier>
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    <author>Somsubhro Chaudhuri</author>
    <author>Michael Stamm</author>
    <author>Ivana Lapšanská</author>
    <author>Thibault Lançon</author>
    <author>Lars Osterbrink</author>
    <author>Thomas Driebe</author>
    <author>Daniel Hein</author>
    <author>René Harendt</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Thermography</value>
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    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Thermografie</value>
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    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Wind energy</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Leading edge erosion</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>KI</value>
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    <collection role="ddc" number="621">Angewandte Physik</collection>
    <collection role="institutes" number="">8 Zerstörungsfreie Prüfung</collection>
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    <title language="deu">Passive Thermografie als Inspektionsinstrument für Rotorblätter von Windkraftanlagen im Betrieb</title>
    <abstract language="deu">Der „European Green Deal“ und der globale Kampf gegen den Klimawandel erfordern mehr und größere Windkraftanlagen. Um dem steigenden Bedarf an Wartung und Inspektion zu bedienen, werden neben der „klassischen“ Inspektion durch Industriekletterer zunehmend auch halb autonome Methoden der Ferninspektion entwickelt. &#13;
Die BAM arbeitet in mehreren Projekten an der Detektion sowohl inneren als auch oberflächlichen Schäden mittels bodenbasierter passiver Thermografie. Passive Thermografie hat den Vorteil, dass die Sonne als Wärmequelle genutzt wird und so sehr große Objekte ohne direkten Zugriff untersucht werden können. Dies erlaubt eine Inspektion von Rotorblättern von Windenergieanlagen im laufenden Betrieb. Das verhindert Ausfallzeiten und Umsatzeinbußen im Vergleich zu herkömmlichen Blattinspektionen.&#13;
Neben der Automatisierung der Aufnahmetechnik stehen zwei wissenschaftliche Fragestellungen im Fokus der aktuellen Arbeiten. Auf der einen Seite sind Inspektionen mit passiver Thermografie und deren Ergebnisse stark wetterabhängig. Deswegen bedarf es der einem guten Verständnis dieser Abhängigkeit und einer Kopplung von Simulationen und experimentellen Daten. So kann gezeigt werden, welche Defekte in Rotorblätter passive Thermografie bei welchen Wetterbedingungen sichtbar macht. Auf der anderen Seite sind die Auswertung und Interpretation der schnell erfassten Inspektionsergebnisse zeitaufwendig und verlangen gute Kenntnisse der Thermografie. Um hier praxistauglicher zu werden, arbeitet die BAM an der Erstellung von Algorithmen, die eine Vorauswahl und -interpretation der Inspektionsdaten mit künstlicher Intelligenz ermöglichen. &#13;
In dem Beitrag wird der messtechnische Aufbau zur bodenbasierten passiven Thermografie an Rotorblättern, der Vergleich von Simulationsdaten und Feldmessungen sowie die Arbeiten bezüglich der KI-basierten Auswertung von thermografischen Aufnahmen gezeigt.</abstract>
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    <author>Ivana Lapšanská</author>
    <subject>
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      <value>Wind</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Windrad</value>
    </subject>
    <subject>
      <language>deu</language>
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      <value>Rotorblatt</value>
    </subject>
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      <value>Thermography</value>
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    <subject>
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      <value>Thermografie</value>
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    </subject>
    <collection role="ddc" number="620">Ingenieurwissenschaften und zugeordnete Tätigkeiten</collection>
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