Fault diagnosis of rotating machinery equipped with multiple sensors using space-time fragments

doi:10.1016/j.jsv.2019.05.036

CORC > 力学研究所 > 中国科学院力学研究所 > 高温气体动力学国家重点实验室

	Fault diagnosis of rotating machinery equipped with multiple sensors using space-time fragments
	Yan XS 1,2,3; Sun Z 1,2,3; Zhao JJ 1,2,3; Shi ZG 1,2,3; Zhang CA(张陈安)4
刊名	JOURNAL OF SOUND AND VIBRATION
	2019-09-15
卷号	456 页码:49-64
关键词	Rotating machinery Fault diagnosis Multi-sensor fusion Active magnetic bearing Shaft orbit
ISSN号	0022-460X
DOI	10.1016/j.jsv.2019.05.036
通讯作者	Yan, Xunshi(yanxs@tsinghua.edu.cn)
英文摘要	The vibration signals captured by multiple sensors can be fused and provide rich information to distinguish faults of rotating machinery. However, previous studies mostly regard multiple signals as individual signals and ignore the coupling relationship between signals resulting in a loss of information. To overcome the above problem, this paper proposes a new multi-sensor data fusion algorithm for identifying faults. First, space-time fragments are constructed to combine multiple signals together considering the space and time relationship between signals. Second, histograms of multi-channel shaft orbit based on space-time fragments are extracted to describe faults. Third, K-nearest neighbor is selected as the classification method. The experiments are carried out on a rig of rotating machinery supported by active magnetic bearings and demonstrate the effectiveness of our proposed algorithm. (C) 2019 Elsevier Ltd. All rights reserved.
分类号	二类/Q1
资助项目	National Science and Technology Major Project of China[2011ZX069] ; National Science and Technology Major Project of China[61305065] ; NSFC ; Strategic Priority Research Program of Chinese Academy of Sciences[XDA17030100]
WOS关键词	CONVOLUTIONAL NEURAL-NETWORK ; FUSION ; CLASSIFICATION
WOS研究方向	Acoustics ; Engineering ; Mechanics
语种	英语
WOS记录号	WOS:000471250400004
资助机构	National Science and Technology Major Project of China ; NSFC ; Strategic Priority Research Program of Chinese Academy of Sciences
内容类型	期刊论文
源URL	[http://dspace.imech.ac.cn/handle/311007/79233]
专题	力学研究所_高温气体动力学国家重点实验室空天飞行科技创新研究中心（筹）
通讯作者	Yan XS
作者单位	1.Institute of Nuclear and New Energy Technology, Tsinghua University, Beijing 100084, China 2.The Key Laboratory of Advanced Reactor Engineering and Safety, Ministry of Education, Beijing 100084, China 3.Collaborative Innovation Center of Advanced Nuclear Energy Technology, Beijing 100084, China 4.State Key Laboratory of High Temperature Gas Dynamics, Institute of Mechanics, Chinese Academy of Sciences, Beijing 100190, China
推荐引用方式 GB/T 7714	Yan XS,Sun Z,Zhao JJ,et al. Fault diagnosis of rotating machinery equipped with multiple sensors using space-time fragments[J]. JOURNAL OF SOUND AND VIBRATION,2019,456:49-64.
APA	Yan XS,Sun Z,Zhao JJ,Shi ZG,&Zhang CA.(2019).Fault diagnosis of rotating machinery equipped with multiple sensors using space-time fragments.JOURNAL OF SOUND AND VIBRATION,456,49-64.
MLA	Yan XS,et al."Fault diagnosis of rotating machinery equipped with multiple sensors using space-time fragments".JOURNAL OF SOUND AND VIBRATION 456(2019):49-64.