核函数主元分析及其在故障特征提取中的应用

提出了基于核函数主元分析的故障特征提取方法。该方法利用计算原始特征空间的内积核函数来实现原始特征空间到高维特征空间的非线性映射。通过对高维特征数据作主元分析，得到原始特征的非线性主元．以所选的非线性主元作为特征子空间，并应用转子试验台的故障数据对该方法进行了检验。结果表明，核函数主元分析更适于提取故障信号的非线性特征，它提取的故障特征对故障具有更好的识别能力，并对分类器具有较强的鲁棒性。

Kernel Principal Component Analysis and Its Application to Fault Feature Extraction

An approach to fault feature extraction is presented,which bases on kernel principal component analysis(KPCA).In this approach,the integral operator kernel functions is used to realize the nonlinear map from the raw feature space to the high dimensional feature space.By performing PCA on the high dimensional feature sets,the nonlinear principal components of the raw feature space are obtained.In succession,the selected nonlinear principal components are used to construct the feature subspace.The fault data sets of rotator test-bed are used to test the KPCA based method.The results indicate that the method is more suitable for nonlinear feature extraction from fault signals,the extracted features based on KPCA perform better fault recognition ability and they are robust for various classifiers.