Abstract
Wind turbine blade defect classification is a relatively new topic in non-destruction health detection of complex components architecture. This paper investigated the defect classification method for nondestructive detection testing (NDT) of wind power blades based on convolutional neural network (CNN). An augmented dataset based on ultrasonic nondestructive testing was collected from wind turbine blade samples; two types of deep CNN architecture, WPT-CNN and one-dimensional time-domain CNN, aiming at auto defect identification were proposed, and their performances in wind turbine blade defect prediction were compared. The result shows that DCNN can be employed to wind turbine blade flaw detection, and the automatic classifier based on deep learning model brings more feasibility and effectiveness.
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Li, T., Yang, Y., Wan, Q., Wu, D., Song, K. (2021). Investigation of Wind Turbine Blade Defect Classification Based on Deep Convolutional Neural Network. In: Liu, Q., Liu, X., Li, L., Zhou, H., Zhao, HH. (eds) Proceedings of the 9th International Conference on Computer Engineering and Networks . Advances in Intelligent Systems and Computing, vol 1143. Springer, Singapore. https://doi.org/10.1007/978-981-15-3753-0_20
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DOI: https://doi.org/10.1007/978-981-15-3753-0_20
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