提出了一种改进独立分量分析(ICA)应用于时频图像的盲源分离问题。由于相似时频图像之间存在潜在的相关性，传统的ICA对于具有相关成分的时频图像盲源分离中效果比较差，本文利用互信息和峭度研究了图像子带之间的相关性和本身的非高斯性，选定特定的子带进行ICA分析。通过仿真时频图像的分离试验，说明此方法分离效果明显优于ICA分离效果，并将该方法应用于转子试验台的基座松动，不对中故障信号复合故障的时频图像中，成功获取了各自故障的时频图像，从而可以获得各自的故障特征信息。

A innovation independent component analysis (ICA) method is presented for the time-frequency image blind source separation (BSS). Because there are common and potent correlations between similar images, the efficiency of traditional ICA applied into the time-frequency image is not satisfied, so the value of mutual information and kurtosis is studied the correlation and non-gauss of the image’s sub-bands severally, and make choice of some sub-band as the input parameter of ICA. After application of simulated time-frequency images, it is validated that the new method can improve the limitation of traditional ICA, finally the new method is applied into time-frequency image of rotor’s fault signals (loose & misalignment), the time-frequency image of respective fault is separated successfully, so the information of the characteristic is method is recognized.