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提出了一种基于连续小波变换(continuous walelet transform,CWT)和奇异值分解(singular value decomposition,SVD)相结合的提升小波系数SVD辨识信号振荡频率和模式信息提取及信号去噪的新方法。克服了噪声较大或者密集模态时,小波脊线不清晰甚至会出现混叠和交叉难以提取频率的情况,根据提升的小波系数奇异值分解频率向量识别各阶振荡模式的频率。同时选用小波能量系数来识别主导振荡模式,用小波软阈值去噪和SVD分解后矩阵重构来进行信号去噪。CWT可以处理含时变振荡模式的低频振荡信号,且对模式参数具有较高的辨识精度。仿真算例验证了算法的有效性和适用性。
A new method based on the combination of continuous walelet transform (CWT) and singular value decomposition (SVD) is presented to enhance the oscillation frequency, mode information extraction and signal denoising of SVD identification wavelet coefficients. When the noisy or dense mode is overcome, the wavelet ridges are not clear and even aliasing and crossover are difficult to extract the frequency. The frequency of each order of oscillation modes is identified according to the improved singular value decomposition frequency vector of the wavelet coefficients. At the same time, wavelet energy coefficient is chosen to identify the dominant oscillation mode, and the signal is denoised by using wavelet soft threshold denoising and matrix reconstruction after SVD decomposition. CWT can handle low-frequency oscillation signals with time-varying oscillation modes and has high recognition accuracy for mode parameters. The simulation example verifies the validity and applicability of the algorithm.