经验小波变换及其在机械故障盲分离中的应用

The Empirical Wavelet Transform and its Application in Mechanical Fualt Blind Source Separation

提出了一种基于经验小波变换针对非线性、非平稳混合信号的单通道盲源分离方法,并应用于机械设备故障诊断中。该方法拟采用EMD-SVD-BIC估计源数,以确定经验小波变换中频谱分割边界的个数N,再将经验小波变换用于单通道盲源分离,从而进行欠定条件下多故障的分离与诊断。利用该方法对仿真信号和单通道轴承振动信号进行了分析,结果表明,该方法能够有效地将不同故障的源信号分离出来,并准确提取出机械的故障特征。

A new single blind separation method based on empirical wavelet transform(EWT)for the non-linear and nonstationary mixed signal is proposed in this paper,and it is applied to the mechanical equipment fault diagnosis.The EMDSVD-BIC method is employed to estimate the number of source signals,which is equals to the number of the spectrum segmentation boundary in EWT.Then,the EWT is utilized to the separation of mixed faults in the condition of single channel.The proposed method was applied to the analysis of numerical simulation signal and measured single channel bearing mixed fault signal.The results show that the proposed method can effectively separate different fault source signals,and can also accurately extract the mechanical fault characteristics.