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

doi:10.1016/j.jsv.2019.05.036

	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
通讯作者	Yan, Xunshi([email protected])
发表期刊	JOURNAL OF SOUND AND VIBRATION
	2019-09-15
卷号	456 页码:49-64
ISSN	0022-460X
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.
关键词	Rotating machinery Fault diagnosis Multi-sensor fusion Active magnetic bearing Shaft orbit
DOI	10.1016/j.jsv.2019.05.036
收录类别	SCI ; EI
语种	英语
WOS记录号	WOS:000471250400004
关键词[WOS]	CONVOLUTIONAL NEURAL-NETWORK ; FUSION ; CLASSIFICATION
WOS研究方向	Acoustics ; Engineering ; Mechanics
WOS类目	Acoustics ; Engineering, Mechanical ; Mechanics
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]
项目资助者	National Science and Technology Major Project of China ; NSFC ; Strategic Priority Research Program of Chinese Academy of Sciences
论文分区	二类/Q1
力学所作者排名	5
RpAuthor	Yan XS
引用统计	被引频次：32[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://dspace.imech.ac.cn/handle/311007/79233
专题	高温气体动力学国家重点实验室空天飞行科技中心
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.Rp_Au:Yan XS
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.

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