M. A. Fremmelev, P. Ladpli, E. Orlowitz, L. Bernhammer, M. McGugan, K. Branner
{"title":"Structural health monitoring of 52-meter wind turbine blade: Detection of damage propagation during fatigue testing","authors":"M. A. Fremmelev, P. Ladpli, E. Orlowitz, L. Bernhammer, M. McGugan, K. Branner","doi":"10.1017/dce.2022.20","DOIUrl":null,"url":null,"abstract":"Abstract This work is concerned with damage detection in a commercial 52-meter wind turbine blade during fatigue testing. Different artificial damages are introduced in the blade in the form of laminate cracks. The lengths of the damages are increased manually, and they all eventually propagate and develop into delaminations during fatigue loading. Strain gauges, acoustic emission sensors, distributed accelerometers, and an active vibration monitoring system are used to track different physical responses in healthy and damaged states of the blade. Based on the recorded data, opportunities and limitations of the different sensing systems for blade structural health monitoring are investigated.","PeriodicalId":34169,"journal":{"name":"DataCentric Engineering","volume":" ","pages":""},"PeriodicalIF":2.4000,"publicationDate":"2022-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"DataCentric Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1017/dce.2022.20","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 5
Abstract
Abstract This work is concerned with damage detection in a commercial 52-meter wind turbine blade during fatigue testing. Different artificial damages are introduced in the blade in the form of laminate cracks. The lengths of the damages are increased manually, and they all eventually propagate and develop into delaminations during fatigue loading. Strain gauges, acoustic emission sensors, distributed accelerometers, and an active vibration monitoring system are used to track different physical responses in healthy and damaged states of the blade. Based on the recorded data, opportunities and limitations of the different sensing systems for blade structural health monitoring are investigated.
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