单通道旋转机械复合故障信号分离及诊断

针对单通道条件下旋转机械复合故障信号分离和故障类别诊断难以有效实现的问题,采用总体经验模态分解(ensemble empirical mode decomposition,EEMD)方法构建虚拟多通道和线性局部切空间排列(linear local tangent space alignment,LLTSA)维数约减方法解决单通道盲源分离的欠定问题,并通过完备字典训练和稀疏分解提取故障信号稀疏特征,最后利用支持向量机对该诊断方法进行分类评估,并将其运用在滚动轴承故障诊断实验中,实现了单通道旋转机械复合故障信号的有效分离和故障类型的正确诊断。

Blind source separation and fault diagnosis of Single-channel rotating mechanical compound fault signal

To solve the problem that the separation and the fault diagnosis of rotating mechanical compound fault signal always difficult to obtain desired results under the condition of single-channel, first, the method of ensemble empirical mode decomposition (EEMD) was applied to build the virtual channels and the method of linear local tangent space alignment (LLTSA) was applied to reduce the dimension, which solved the problem of underdetermined blind source separation well. Then, training the over-complete dictionary and using the method of signal sparse decomposition to extract the sparse characteristics of rotating mechanical compound fault signal. Finally, the support vector machine was employed to evaluate the effect of signal separation and fault diagnosis method. Moreover, the proposed method was applied to the experiment of rolling bearing fault diagnosis, and it''s found that the separation and classification of compound fault signal was completed efficiently.