Weighted KPCA based on fault feature selection and feature information fusion

doi:10.13465/j.cnki.jvs.2014.09.016

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	Weighted KPCA based on fault feature selection and feature information fusion
	Zhang, Heng 1,2; Zhao, Rong-Zhen 1,2
刊名	Zhendong yu Chongji/Journal of Vibration and Shock
	2014
卷号	33 期号:9 页码:89-93+121
关键词	Failure analysis Frequency domain analysis Information fusion Machinery Principal component analysis Time domain analysis Corresponding relations Fault characteristics Fault feature selections Feature information Feature selection methods Kernel principal component analyses (KPCA) Sensitive features Time frequency domain
ISSN号	10003835
DOI	10.13465/j.cnki.jvs.2014.09.016
英文摘要	Aiming at unperfect corresponding relations between fault characteristics and fault categories of rotating machineries, a 12-channel fault information set for a double-span rotor system was taken as a study object, a new method about the feature extraction based on weighted KPCA was proposed. At first, the feature extractions of time domain, frequency domain and time-frequency domain for a single channel vibration signal were done, and the original fault feature set of 12 channels was obtained for the double-span rotor system. Secondly, 12 sensitive feature subsets were screened out from the original fault feature set by using the multiple-criterion feature selection method. And then, a fusion feature vector was obtained by fusing the 12 sensitive feature subsets. Finally, the main components of the fusion feature vector were extracted by using the weighted kernel principal component analysis (KPCA). Experimental results showed that this method can find sensitive feature subsets, the kernel main components can reveal the differences among the different fault categories effectively.
语种	中文
出版者	Chinese Vibration Engineering Society
内容类型	期刊论文
源URL	[http://ir.lut.edu.cn/handle/2XXMBERH/112715]
专题	兰州理工大学
作者单位	1.College of Mechatronic Engineering, Lanzhou University of Technology, Lanzhou 730050, China 2.MOE Key laboratory of Digital Manufacturing Technology and Application, Lanzhou University of Technology, Lanzhou 730050, China;
推荐引用方式 GB/T 7714	Zhang, Heng,Zhao, Rong-Zhen. Weighted KPCA based on fault feature selection and feature information fusion[J]. Zhendong yu Chongji/Journal of Vibration and Shock,2014,33(9):89-93+121.
APA	Zhang, Heng,&Zhao, Rong-Zhen.(2014).Weighted KPCA based on fault feature selection and feature information fusion.Zhendong yu Chongji/Journal of Vibration and Shock,33(9),89-93+121.
MLA	Zhang, Heng,et al."Weighted KPCA based on fault feature selection and feature information fusion".Zhendong yu Chongji/Journal of Vibration and Shock 33.9(2014):89-93+121.