关键词: 声发射, 集合经验模态分解, 小波阈值函数, Sigmoid函数, 河马优化算法

Abstract: A denoising method combining Ensemble Empirical Modal Decomposition (EEMD) with an improved wavelet thresholding technique is proposed for acoustic emission signals from gear operation. Firstly, the traditional wavelet thresholding function is improved based on the characteristics of the Sigmoid function, which is smooth and easy to differentiate. Secondly, a simulated noisy signal is constructed and decomposed using EEMD to obtain a series of Intrinsic Mode Function (IMF) components. The correlation coefficients of each IMF component are calculated, and the signal is reconstructed accordingly. Finally, the Hippopotamus Optimization (HO) algorithm is employed to find the optimal adjustment parameter α in the improved wavelet thresholding function. The final denoised signal is obtained using the improved wavelet thresholding denoising method. The evaluation criteria used are Signal-to-Noise Ratio (SNR), Mean Squared Error (MSE), and Cosine Similarity (Cos). Simulation results demonstrate that the proposed method significantly improves the SNR compared to traditional denoising methods. Furthermore, the proposed method is validated using acoustic emission signals collected from gear tests. The experimental results indicate that the method exhibits better denoising performance and practical value compared to traditional denoising approaches.

Key words: acoustic emission, ensemble empirical modal decomposition, wavelet threshold , Sigmoid function, hippopotamus optimization algorithm