The concepts, principles and usages of principal component analysis （PCA） and independent component analysis （ICA） are interpreted. Then the algorithm and methodology of ICA-based blind source separation （BSS）, in which the pre-whitened based on PCA for observed signals is used, are researched. Aiming at the mixture signals, whose frequency components are overlapped by each other, a simulation of BSS to separate this type of mixture signals by using theory and approach of BSS has been done. The result shows that the BSS has some advantages what the traditional methodology of frequency analysis has not.
讨论了核主元分析(K erne l P rinc ipa l Com ponen t A na lys is,简称KPCA)原理,提出了基于KPCA的透平机械状态监测方法。该方法在低维特征空间利用内积核函数,实现原始空间到高维空间的非线性映射以及对高维映像数据的主元分析,从而在低维空间得到原始特征的非线性主元,并根据非线性主元构建特征子空间,实现特征提取和对透平机械状态的分类识别并监测其状态变化。对仿真数据及透平机械在正常、重负荷状态下试验数据的研究表明,KPCA分类效果比主元分析好,能有效地识别出透平机械的不同状态,并能及时监测到状态发生的变化。
