A method of convolutional neural network based on frequency segmentation for monitoring the state of wind turbine blades  

作　　者：Weijun Zhu Yunan Wu Zhenye Sun Wenzhong Shen Guangxing Guo Jianwei Lin 

机构地区：[1]College of Electrical,Energy and Power Engineering,Yangzhou University,Yangzhou 225127,China

出　　处：《Theoretical & Applied Mechanics Letters》2023年第6期465-480,共16页力学快报（英文版）

基　　金：funded by the National Nature Science Founda-tion of China(Grant Nos.51905469 and 11672261);the National key research and development program of China under grant number(Grant No.2019YFE0192600)。

摘　　要：Wind turbine blades are prone to failure due to high tip speed,rain,dust and so on.A surface condition detecting approach based on wind turbine blade aerodynamic noise is proposed.On the experimental measurement data,variational mode decomposition filtering and Mel spectrogram drawing are conducted first.The Mel spectrogram is divided into two halves based on frequency characteristics and then sent into the convolutional neural network.Gaussian white noise is superimposed on the original signal and the output results are assessed based on score coefficients,considering the complexity of the real environment.The surfaces of Wind turbine blades are classified into four types:standard,attachments,polishing,and serrated trailing edge.The proposed method is evaluated and the detection accuracy in complicated background conditions is found to be 99.59%.In addition to support the differentiation of trained models,utilizing proper score coefficients also permit the screening of unknown types.

关 键 词：Wind turbine aerodynamic noise Surface condition detection Mel spectrogram Image segmentation Convolution neural network(CNN) 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程] TM315[自动化与计算机技术—控制科学与工程]