Dimension reduction of a rotor faults data set based on standard orthogonal discriminant projection

doi:10.13465/j.cnki.jvs.2020.18.012

CORC > 兰州理工大学 > 兰州理工大学

	Dimension reduction of a rotor faults data set based on standard orthogonal discriminant projection
	Shi, Mingkuan; Zhao, Rongzhen
刊名	Zhendong yu Chongji/Journal of Vibration and Shock
	2020-09-28
卷号	39 期号:18 页码:96-102
关键词	Decision making Dimensionality reduction Frequency domain analysis Machinery Pattern recognition Time domain analysis Dimension reduction Dimensionality reduction algorithms Discriminant informations High dimensional feature Intelligent decision making Rotor fault diagnosis Sensitive features Time frequency domain
ISSN号	10003835
DOI	10.13465/j.cnki.jvs.2020.18.012
英文摘要	Aiming at the insufficiency in fault data classification in the technology of rotating machinery intelligent decision-making, a dimensionality reduction algorithm for a rotor fault data set based on standard orthogonal discriminant projection (SODP) was proposed. First, an original fault feature set was constructed in time domain, frequency domain or time-frequency domain, and the vibration signal was transformed into a high-dimensional feature data set. Then SODP was used to select the sensitive feature subset that could best reflect the nature of the fault. Finally, the low-dimensional feature subsets were input into a KNN classifier for fault pattern identification. The vibration signal set of a double-span rotor system was used to verify the method. It is proved that the method can extract global and local discriminant information comprehensively, which makes the difference between fault categories clearer and the corresponding fault pattern recognition accuracy improved. The algorithm provides a reference to the actual rotor fault diagnosis. © 2020, Editorial Office of Journal of Vibration and Shock. All right reserved.
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/150720]
专题	兰州理工大学
作者单位	School of Mechanic Engineering, Lanzhou University of Technology, Lanzhou; 730050, China
推荐引用方式 GB/T 7714	Shi, Mingkuan,Zhao, Rongzhen. Dimension reduction of a rotor faults data set based on standard orthogonal discriminant projection[J]. Zhendong yu Chongji/Journal of Vibration and Shock,2020,39(18):96-102.
APA	Shi, Mingkuan,&Zhao, Rongzhen.(2020).Dimension reduction of a rotor faults data set based on standard orthogonal discriminant projection.Zhendong yu Chongji/Journal of Vibration and Shock,39(18),96-102.
MLA	Shi, Mingkuan,et al."Dimension reduction of a rotor faults data set based on standard orthogonal discriminant projection".Zhendong yu Chongji/Journal of Vibration and Shock 39.18(2020):96-102.