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笔者提出了一种基于提升小波降噪与局域均值分解(Local Mean Decom position,LMD)的转子故障特征提取方法。LMD在分析非线性、非平稳信号方面效果较好,但是对噪声较敏感。为了消除噪声对LMD分解效果的影响,先用提升小波对原始信号降噪,然后对去噪信号进行LMD分解,选取有用的PF分量进行频谱分析,并提取出转子故障特征。通过仿真试验和转子故障特征提取试验,证明了该方法在提取转子故障特征中的有效性能。
The author presents a rotor fault feature extraction method based on lifting wavelet denoising and Local Mean Decomposition (LMD). LMD works better for analyzing non-linear, non-stationary signals, but is more sensitive to noise. In order to eliminate the effect of noise on the decomposition of LMD, the original signal is first denoised by lifting wavelet and then the denoising signal is LMD-decomposed. The useful PF component is selected for spectrum analysis and the rotor fault feature is extracted. Through the simulation test and rotor fault feature extraction test, the effective performance of the proposed method in extracting rotor fault features is proved.