转子故障特征数据分类的KPCA-BFDA方法

KPCA-BFDA for the Classification of Rotor Fault Feature Data

对非线性转子系统故障特征数据的分类方法进行了研究。在提出一种偏费歇判别分析法(biased fisher discriminatory analysis,简称BFDA)的前提下,进一步提出将核主成分分析法(kernel principal component analysis,简称KPCA)与偏费歇判别分析法相结合的数据集降维方法,该方法中的核主成分分析步骤用于构造剔除数据集冗余信息的降维数据集,偏费歇判别分析步骤用于进一步降低数据集维数并提高不同类别数据子集间的分离程度。对实例数据与典型故障数据的分类结果表明,提出的偏费歇判别分析法在具备费歇判别分析降维可分性能的基础上,具有更低计算复杂度的特点。提出的核主成分分析结合偏费歇判别分析的算法,其对应的降维结果能直接应用于线性分类器,且取得了较好的分类效果。

Aiming at the classification of fault feature data for the nonlinear rotor system,a method is studied.On the premise of introduction of Biased Fisher Discriminatory Analysis,a method of dimension reduction for data set by KPCA combined with BFDA is introduced furthermore.The procedure of KPCA which removes redundant information of original data set is used to construct data set of dimension reduction,BFDA is used to reduce dimension of data set further and improve the separation of data subset from different category.The classification results of instance data and typical fault data show that,on the basis of dimension reduction separability of FDA,the proposed method of BFDA is lower for the characteristics of computational complexity.The result of dimension reduction corresponding to the proposed method of KPCA combined with BFDA can apply to linear classification directly,and effective data classification can be achieved.