KPCA与LTSA融合的转子故障数据集降维算法  

Research on the method of dimension reduction of rotor fault data set by fusing KPCA and LTSA

作　　者：赵荣珍[1] 陈昱吉 ZHAO Rong-zhen;CHEN Yu-ji(College of Mechano-Electronic Engineering, Lanzhou Univ. of Tech., Lanzhou 730050, China)

机构地区：[1]兰州理工大学机电工程学院,甘肃兰州730050

出　　处：《兰州理工大学学报》2021年第1期36-40,共5页Journal of Lanzhou University of Technology

基　　金：国家自然科学基金(51675253)。

摘　　要：针对核主成分分析(kernel principal component analysis,KPCA)和局部切空间排列算法(local tangent space,LTSA)在降维过程中无法兼顾保持数据全局结构特性和局部结构特性的问题,利用核函数的可线性叠加性质,提出一种将KPCA算法与LTSA算法融合的非线性降维算法.该算法能使故障数据集经过降维后同时保持数据样本间的全局距离关系和局部邻域关系.应用验证表明:本算法可以准确地提取故障数据集中所包含的全局与局部结构特征模式,使故障分类的结果更明晰、更准确、更有效.In order to solve the problem that kernel principal component analysis(KPCA)and local tangent space alignment(LTSA)algorithm can not keep both the global and local structure characteristics of data in the process of dimensionality reduction,this paper uses linear superposition of kernel function to derive a nonlinear dimensionality reduction algorithm which combines KPCA algorithm with LTSA algorithm.The dimensionality reduction algorithm can make a fault dataset maintaining both global distance relationship and local neighborhood relationship between data samples after dimensionality reduction.Computational experiments show that this algorithm can accurately extract the global and local structural characteristics contained in the fault dataset,and make the results of fault classification clearer and more accurate as well as more effective.

关 键 词：核主成分分析 局部切空间排列 数据降维 故障分类 

分 类 号：TH165[机械工程—机械制造及自动化]