Small sample size fault data recognition based on the principal component analysis and kernel local Fisher discriminant analysis

doi:10.13245/j.hust.151203

CORC > 兰州理工大学 > 兰州理工大学 > 机电工程学院

	Small sample size fault data recognition based on the principal component analysis and kernel local Fisher discriminant analysis
	Zhao, Rongzhen; Wang, Xuedong; Deng, Linfeng
刊名	Huazhong Keji Daxue Xuebao (Ziran Kexue Ban)/Journal of Huazhong University of Science and Technology (Natural Science Edition)
	2015-12-23
卷号	43 期号:12 页码:12-16
关键词	Discriminant analysis Failure analysis Fisher information matrix Principal component analysis Classification features Data dimension reduction Dimension reduction Fault data Gaussian kernels High-dimensional Local Fisher Discriminant Analysis Small Sample Size
ISSN号	16714512
DOI	10.13245/j.hust.151203
英文摘要	Aiming at the dimension reduction of the small sample size fault data set, a new method in dimension reduction was proposed based on the combination of principal component analysis (PCA) and kernel local Fisher discriminant analysis. This method first used PCA to extract key information and dimension reduction of the data set, then the Gaussian kernel was used to map the feature subset to a high-dimensional liner space, and in this space, local Fisher discriminant analysis was applied to a train most discrimination classification feature set. Finally, a small sample size rotor fault data feature set were employed to verify this method. According to the result of dimension reduction, clear space between various faults categories and small distance in the similar class can be obtained. This method provide an effective way to solve the problem of small sample size rotor fault data set classification. © 2015, Editorial Board of Journal of Huazhong University of Science and Technology. All right reserved.
语种	中文
出版者	Huazhong University of Science and Technology
内容类型	期刊论文
源URL	[http://ir.lut.edu.cn/handle/2XXMBERH/112806]
专题	机电工程学院
作者单位	School of Mechanical and Electronical Engineering, Lanzhou University of Technology, Lanzhou; 730050, China
推荐引用方式 GB/T 7714	Zhao, Rongzhen,Wang, Xuedong,Deng, Linfeng. Small sample size fault data recognition based on the principal component analysis and kernel local Fisher discriminant analysis[J]. Huazhong Keji Daxue Xuebao (Ziran Kexue Ban)/Journal of Huazhong University of Science and Technology (Natural Science Edition),2015,43(12):12-16.
APA	Zhao, Rongzhen,Wang, Xuedong,&Deng, Linfeng.(2015).Small sample size fault data recognition based on the principal component analysis and kernel local Fisher discriminant analysis.Huazhong Keji Daxue Xuebao (Ziran Kexue Ban)/Journal of Huazhong University of Science and Technology (Natural Science Edition),43(12),12-16.
MLA	Zhao, Rongzhen,et al."Small sample size fault data recognition based on the principal component analysis and kernel local Fisher discriminant analysis".Huazhong Keji Daxue Xuebao (Ziran Kexue Ban)/Journal of Huazhong University of Science and Technology (Natural Science Edition) 43.12(2015):12-16.