基于最优Morlet小波和SVD的滤波消噪方法及故障诊断的应用

A DE-NOISING METHOD BASED ON OPTIMAL MORLET WAVELET AND SINGULAR VALUE DECOMPOSITION AND ITS APPLICATION IN FAULT DIAGNOSIS

分析了传统的小波去噪方法和小波变换的滤波特性。利用小波变换技术、奇异值分解技术和Morlet小波良好的时域和频域特性,提出了基于最优Morlet小波和SVD的滤波消噪方法。首先,采用最小Shannon熵方法确定出最优Morlet小波;然后,利用奇异值分解技术确定出最佳变换尺度a;最后对信号进行滤波消噪处理,从而提取信号中的有用成分。实验结果表明,该方法具有良好的去噪性能,用于故障特征提取是有效的。

The traditional wavelet-decomposition-based de-noising method and the wavelet-filter-based filter characteristic are introduced.Using wavelet transformation technology,singular value decomposition technology and good characteristics of Morlet wavelet in time domain and frequency domain,a de-noising method based on optimal Morlet wavelet and singular value decomposition is put forward.Firstly,minimum Shannon entropy is used to optimize the Morlet wavelet shape factor.Then,a periodicity detection method based on singular value decomposition(SVD) is used to choose the appropriate scale a for the wavelet transformation.Finally,the useful components of the signal analyzed can be obtained by the wavelet-filter-based de-noising method.The experimental result shows the proposed method has a good de-nosing performance and it is effective in fault feature extraction.