US 11,905,926 B2
Method and apparatus for inspecting wind turbine blade, and device and storage medium thereof
Weiyu Cui, Shanghai (CN); Shu Wei, Shanghai (CN); Qingsheng Zhao, Shanghai (CN); Zhongji Yin, Shanghai (CN); Yong Ai, Shanghai (CN); Dong Ao, Shanghai (CN); and Zhimeng Wang, Shanghai (CN)
Assigned to ENVISION DIGITAL INTERNATIONAL PTE. LTD., Singapore (SG); and SHANGHAI ENVISION DIGITAL CO., LTD., Shanghai (CN)
Appl. No. 17/790,078
Filed by ENVISION DIGITAL INTERNATIONAL PTE. LTD., Singapore (SG); and SHANGHAI ENVISION DIGITAL CO., LTD., Shanghai (CN)
PCT Filed Dec. 28, 2020, PCT No. PCT/SG2020/050785
§ 371(c)(1), (2) Date Jun. 29, 2022,
PCT Pub. No. WO2021/137760, PCT Pub. Date Jul. 8, 2021.
Claims priority of application No. 201911420554 (CN), filed on Dec. 31, 2019.
Prior Publication US 2023/0123117 A1, Apr. 20, 2023
Int. Cl. F03D 17/00 (2016.01); G06F 18/22 (2023.01)
CPC F03D 17/00 (2016.05) [G06F 18/22 (2023.01); F05B 2260/83 (2013.01); F05B 2270/709 (2013.01); F05B 2270/81 (2013.01)] 9 Claims
OG exemplary drawing
 
1. A method for inspecting a wind turbine blade, wherein the wind turbine blade is a blade in a wind power generation device, the wind power generation device further comprising a tower provided with a sound acquisition device that is mounted away from the wind turbine blade; wherein the method comprises:
acquiring, via the sound acquisition device, a sound signal in response to an impingement of wind on the wind turbine blade, wherein the sound signal comprises a sound signal generated by sliding of air between blades in the case that the wind impinges on the wind turbine blade;
extracting a signal envelope from a time domain signal diagram formed by the sound signal by calling a signal analysis algorithm;
determining a position of a point, where a wave trough appears on the signal envelope, in a time domain as a segmentation point; and
converting the sound signal into a frequency spectrogram, and obtaining a segmented frequency spectrogram by segmenting the frequency spectrogram based on the segmentation point; and
obtaining a damage recognition result of the wind turbine blade by performing image recognition on the segmented frequency spectrogram based on a damage recognition model, wherein the damage recognition model is obtained by training a neural network model.