动态盲源分离问题是多故障源盲分离的一个热点。传统的机械故障源分离方法要求满足统计特征保持稳定，且混合系统保持不变等假设，而忽略了时序信息。针对此不足，结合规范变量分析(Canonical variate analysis, CVA)和独立分量分析(Independent component analysis, ICA)，提出一种基于CVA-ICA的机械多故障源动态盲分离方法。该方法的基本思想是将源信号看成状态空间的状态变量，观测信号看成状态空间的输出变量，从而将动态混合盲源分离问题转化为状态空间盲源分离问题，利用规范变量分析作为降维工具来构造状态空间，再利用传统的ICA算法对规范的观测信号进行盲源分离。仿真研究表明，在处理动态混合的盲分离中，提出的方法明显优于静态ICA方法，取得了满意的分离效果。将该方法应用到滚动轴承内圈和滚动体的故障盲分离中，试验结果进一步验证了该方法的有效性。

Dynamic blind source separation is a focus in the blind source separation of multi-fault. Traditional blind source separation (BSS) is restricted to the stable statistical characteristics and static mixture system, and ignores the sequential information. Based on this deficiency, combining to canonical variate analysis (CVA) and independent component analysis (ICA), a dynamic blind source separation method based on CVA-ICA is proposed. In the proposed method, the source signal is regarded as state variable in the state space, observation signal as output variable, thus the dynamics ICA is transform into the state space ICA. The proposed method employs CVA as a reduction tool to construct a state space, then the statistically independent sources are separated by the conventional ICA algorithm. The simulation results show that the CVA-ICA method is superior to traditional blind source separation in the dynamic blind source separation, and has satisfactory separation performance. The proposed method is applied in blind separation of bearing inner and ball fault, the experiment results further validate the effectiveness of the proposed method.