Fault diagnosis of rotating machinery equipped with multiple sensors using space-time fragments
文献类型:期刊论文
作者 | Yan XS1,2,3; Sun Z1,2,3; Zhao JJ1,2,3; Shi ZG1,2,3; Zhang CA(张陈安)4 |
刊名 | JOURNAL OF SOUND AND VIBRATION |
出版日期 | 2019-09-15 |
卷号 | 456页码:49-64 |
ISSN号 | 0022-460X |
关键词 | Rotating machinery Fault diagnosis Multi-sensor fusion Active magnetic bearing Shaft orbit |
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 |
WOS关键词 | CONVOLUTIONAL NEURAL-NETWORK ; FUSION ; CLASSIFICATION |
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 |
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. |
入库方式: OAI收割
来源:力学研究所
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