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<p>A 3D-CNN-based acoustic pattern recognition model is developed for accurate detection of abrasion faults in wind turbine blades. The model first processes acoustic vibration signals through empirical mode decomposition and wavelet denoising to account for local signal characteristics. The denoised signals are then subjected to frame splitting, windowing, and discrete Fourier transform to construct two-dimensional energy spectrograms, which are subsequently downscaled using Mel-filter banks to extract distinctive acoustic features associated with blade abrasion faults. These features are input into an innovative three-dimensional convolutional neural network for fault identification. Experimental results demonstrate the model’s effectiveness, achieving a peak recognition accuracy of 98.8% and consistent performance with accuracy rates above 94% across tests. The model exhibits a reliable capability to distinguish between normal operational sounds and various severities of blade abrasion faults, while maintaining low misjudgment rates.</p>

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3D-CNN-based Acoustic Recognition Model for Large Wind Turbine Blade Abrasion Faults

  • 期刊论文

  • 发表日期：

    2026年1月23日

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  • Haoming Mo,
  • Fanyong Yin

摘要

A 3D-CNN-based acoustic pattern recognition model is developed for accurate detection of abrasion faults in wind turbine blades. The model first processes acoustic vibration signals through empirical mode decomposition and wavelet denoising to account for local signal characteristics. The denoised signals are then subjected to frame splitting, windowing, and discrete Fourier transform to construct two-dimensional energy spectrograms, which are subsequently downscaled using Mel-filter banks to extract distinctive acoustic features associated with blade abrasion faults. These features are input into an innovative three-dimensional convolutional neural network for fault identification. Experimental results demonstrate the model’s effectiveness, achieving a peak recognition accuracy of 98.8% and consistent performance with accuracy rates above 94% across tests. The model exhibits a reliable capability to distinguish between normal operational sounds and various severities of blade abrasion faults, while maintaining low misjudgment rates.