Kim, H.; Kim, H.; Kang, K. Accuracy Improvement of Debonding Damage Detection Technology in Composite Blade Joints for 20 kW Class Wind Turbine. Mach. Learn. Knowl. Extr.2024, 6, 1857-1870.
Kim, H.; Kim, H.; Kang, K. Accuracy Improvement of Debonding Damage Detection Technology in Composite Blade Joints for 20 kW Class Wind Turbine. Mach. Learn. Knowl. Extr. 2024, 6, 1857-1870.
Kim, H.; Kim, H.; Kang, K. Accuracy Improvement of Debonding Damage Detection Technology in Composite Blade Joints for 20 kW Class Wind Turbine. Mach. Learn. Knowl. Extr.2024, 6, 1857-1870.
Kim, H.; Kim, H.; Kang, K. Accuracy Improvement of Debonding Damage Detection Technology in Composite Blade Joints for 20 kW Class Wind Turbine. Mach. Learn. Knowl. Extr. 2024, 6, 1857-1870.
Abstract
Securing the structural safety of blades has become crucial, owing to the increasing size and weight of blades resulting from the recent development of large wind turbines. Composites are primarily used for blade manufacturing because of their high specific strength and specific stiff-ness. However, in composite blades, joints may experience fractures from the loads generated during wind turbine operation, leading to deformation caused by changes in the structural stiff-ness. In this study, 7,132 debonding damage data, classified by damage type, position, and size, are selected to predict debonding damage based on natural frequency. The change in the natural frequency caused by debonding damage is acquired through finite element (FE) modeling and modal analysis. Synchronization between the FE analysis model and manufactured blades is achieved through modal testing and data analysis. Finally, the relationship between the debond-ing damage and the change in natural frequency is examined using artificial neural network techniques
Copyright:
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