中文摘要：

盲源分离对于多振源信号的故障诊断与识别是一种有效的方法，但是传统的盲源分离算法都是针对观察信号大于或等于源信号的情况，但对于观察信号小于源信号的欠定盲分离问题，这在很大程度上制约了盲源分离的实际应用。通过应用经验模式分解和时频分析对非平稳信号分析的优势，提出基于时频分析的欠定盲源分离方法进行设备微弱特征提取。对振动信号进行经验模式分解，并根据分解得到的内蕴模式分量估计源信号个数并选择最优的观察信号，将振动信号与选择的最优观察信号组成新的观察信号进行基于时频分析的盲源分离，通过对仿真信号和齿轮箱实测信号进行验证分析。并与基于独立分量分析的盲源分离算法进行对比，研究表明基于时频分析的盲源分离对混合信号具有更好的分离效果，能够较好地对微弱特征进行提取。

英文摘要：

Blind source separation（BSS） is an effective method for the fault diagnosis and classification of mixture signals with multiple vibration sources. The traditional BSS algorithm is applicable to the number of observed signals is no less to the source signals. BSS performance is limit for the under-determined condition that the number of observed signals is less than source signals. An under-determined BSS method is provided based on the advantage of time-frequency analysis and empirical mode decomposition （EMD）. It is suitable for weak feature extraction and pattern recognition. The vibration signal is decomposed by using EMD. The number of source signals are estimated and the optimal observed signals are determined according to the EMD result. Then, the vibration signal and the optimal observed signals are used to construct the multi-channel observed signals. In the end, blind source separation based on time-frequency analysis are used to the constructed signals. Simulation signal and gearbox signals are used to verify the effectiveness of this method. Compared with independent component analysis, BSS based on time-frequency analysis has good performance on signal separation. It is more suitable for weak feature extraction.