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    <id>62501</id>
    <completedYear/>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>poster</type>
    <publisherName/>
    <publisherPlace/>
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    <title language="eng">Thermographic detection and AI evaluation of leading-edge erosion</title>
    <abstract language="eng">The European Green Deal and the global fight against climate change call for more and larger wind turbines. To meet the increasing demand for maintenance and inspection, semi-autonomous methods of remote inspection are increasingly being developed in addition to industrial climbers performing the inspection [1, 2]. &#13;
Rotor blades are exposed to extreme weather conditions throughout their lifetime causing leading edge erosion which changed the aerodynamic features of blades and can cause structural damages. The estimated annual energy production (AEP) losses caused by erosion damages are between 0.5% and 2% per year. The classification of the severity of the damage and the quantification of the resulting AEP losses are subject of scientific research. With such information, cost efficient repairs and maintenance efforts can improve the power production of wind turbines.&#13;
Passive thermography presents a viable method for inspecting rotor blades, capable of identifying internal damage [3, 4] and surface erosion [5, 6]. An advantage of this method is that it not only detects rain erosion damage but also reveals temperature variations caused by resulting turbulence on the blade's surface. These turbulent effects reduce rotor blade efficiency, leading to losses in AEP. The inspection process takes approximately 10 minutes per turbine and is carried out while the turbine is fully operational, eliminating downtime and revenue losses typically associated with traditional blade inspections. This efficient inspection procedure is further enhanced by an automatic data analysis system, resulting in a substantial number of wind turbines being inspected within a specified timeframe. The automated evaluation of thermal images is executed using a fully convolutional network (FCN).&#13;
The initial phase involved training an FCN using over 1500 thermal images. The primary objective was to accurately identify the thermal patterns associated with erosion damage at the leading edge. In the ongoing second phase, the emphasis is on integrating thermographic and visual images to assess the extent of the damage using an FCN. Additionally, this phase involves leveraging human factors in the FCN training process and providing a precise estimation of Annual Energy Production losses attributed to the identified rain erosion damages.</abstract>
    <identifier type="url">https://www.sintef.no/globalassets/project/eera-deepwind-2024/posters/operation-and-maintenance_ivana_lapsanska_thermographic-detection.pdf</identifier>
    <enrichment key="eventName">EERA Deep Wind 2024</enrichment>
    <enrichment key="eventPlace">Trondheim , Norway</enrichment>
    <enrichment key="eventStart">17.01.2024</enrichment>
    <enrichment key="eventEnd">19.01.2024</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <author>Ivana Lapšanská</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Wind</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Turbulent flow</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Thermography</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Wind rotor blade</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Inspection</value>
    </subject>
    <collection role="ddc" number="620">Ingenieurwissenschaften und zugeordnete Tätigkeiten</collection>
    <collection role="institutes" number="">8 Zerstörungsfreie Prüfung</collection>
    <collection role="institutes" number="">8.3 Thermografische 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="">Präsentation</collection>
    <collection role="themenfelder" number="">Windenergie</collection>
  </doc>
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