IMECH-IR  > 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 XS1,2,3; Sun Z1,2,3; Zhao JJ1,2,3; Shi ZG1,2,3; Zhang CA(张陈安)4
Corresponding AuthorYan, Xunshi(yanxs@tsinghua.edu.cn)
Source PublicationJOURNAL OF SOUND AND VIBRATION
2019-09-15
Volume456Pages:49-64
ISSN0022-460X
AbstractThe 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.
KeywordRotating machinery Fault diagnosis Multi-sensor fusion Active magnetic bearing Shaft orbit
DOI10.1016/j.jsv.2019.05.036
Indexed BySCI ; EI
Language英语
WOS IDWOS:000471250400004
WOS KeywordCONVOLUTIONAL NEURAL-NETWORK ; FUSION ; CLASSIFICATION
WOS Research AreaAcoustics ; Engineering ; Mechanics
WOS SubjectAcoustics ; Engineering, Mechanical ; Mechanics
Funding ProjectNational 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]
Funding OrganizationNational Science and Technology Major Project of China ; NSFC ; Strategic Priority Research Program of Chinese Academy of Sciences
Classification二类/Q1
Ranking5
Citation statistics
Document Type期刊论文
Identifierhttp://dspace.imech.ac.cn/handle/311007/79233
Collection高温气体动力学国家重点实验室
空天飞行科技中心(筹)
Corresponding AuthorYan XS
Affiliation1.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
Recommended Citation
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.
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