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- W2746207856 abstract "As the wind energy sector evolves, blade manufacturers have started to producelarger blades in an effort to maximise power production. As the blades are amongstthe most important and most expensive constituents of the wind turbines, it is benefi-cial to monitor their structural integrity, which would aid in minimising downtime dueto periodic maintenance, and/or damage. The work carried out in this paper is basedon real data obtained from a Siemens Wind Power wind turbine using sensors on theblades. During normal operation, the blade sensors capture a range of frequency contentaside from the blade frequency modes. These include the vibration contents of theturbine rotation speeds (which are the most dominant), the harmonics, and that of thetower. To add to the complexity, since wind speed fluctuations affect the rotor speed,wind turbine operations are nonstationary, making structural health monitoring (SHM)a difficult task. Therefore, this present work aims to develop an SHM technique, whichutilises machine learning techniques, to reliably determine the structural integrity of theblades with respect to certain damage modes." @default.
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- W2746207856 date "2017-09-28" @default.
- W2746207856 modified "2023-09-27" @default.
- W2746207856 title "On the Structural Health Monitoring of Operational Wind Turbine Blades" @default.
- W2746207856 doi "https://doi.org/10.12783/shm2017/14151" @default.
- W2746207856 hasPublicationYear "2017" @default.
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