Currently, the detection of wind turbine blade damage mainly relies on regular plan-based maintenance and manual inspections. In this study, a method for extracting audio features and detecting damage in wind turbine blades with wavelet denoising is proposed. This method first uses wavelet denoising to process the original audio signal, the denoised audio is then split into frames with Hamming windowing function. After that, multi-scale features are extracted in both time and frequency domains. Principal component analysis is used to reduce the dimensionality of the features, and clustering canters are obtained through K-means clustering analysis. Finally, Gaussian distribution outlier detection is used to detect audio signals from damaged blades. Experimental results using lab-generated audio data show that the proposed method has high accuracy and strong robustness in detecting wind turbine blade damage.
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