中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 力学研究所
  3. 中国科学院力学研究所
  4. 高温气体动力学国家重点实验室
Multiresolution Hypergraph Neural Network for Intelligent Fault Diagnosis

文献类型：期刊论文

作者Yan, Xunshi3; Liu, Yang1,2; Zhang, Chen-an2; Liu Y(刘洋); Zhang CA(张陈安)
刊名IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
出版日期2022
卷号71页码:10
关键词Convolution Fault diagnosis Correlation Task analysis Machinery Machine learning algorithms Laplace equations Fault diagnosis graph convolutional network (GCN) hypergraph hypergraph neural network (HGNN) multiresolution
ISSN号0018-9456
DOI10.1109/TIM.2022.3212532
通讯作者Yan, Xunshi(yanxs@tsinghua.edu.cn)
英文摘要Intelligent fault diagnosis has made significant progress, thanks to machine learning, particularly deep-learning algorithms. However, most machine-learning algorithms treat samples as independent and ignore the correlations between samples that contain valuable information for creating discriminative features. In recent years, graph neural networks have increased diagnostic performance by capturing the correlation between samples according to establishing the inherent structure of data, but they also suffer from two shortcomings. First, a simple graph only represents pairwise relationships of samples and cannot depict complex higher-order structures. Second, the generated graph structure is insufficient to characterize the data without an explicit structure. To address the above two issues, this article proposes a multiresolution hypergraph neural network, a novel algorithm that can discover higher-order complex relationships between samples, and mine the structure hidden in data by establishing and fusing hypergraph structures at multiple resolutions. Experiments are conducted on three datasets to demonstrate the effectiveness of the proposed algorithm.
资助项目National Science and Technology Major Project of China[ZX069] ; Strategic Priority Research Program of Chinese Academy of Sciences[XDA17030100]
WOS研究方向Engineering ; Instruments & Instrumentation
语种英语
WOS记录号WOS:000871033400009
资助机构National Science and Technology Major Project of China ; Strategic Priority Research Program of Chinese Academy of Sciences
源URL[http://dspace.imech.ac.cn/handle/311007/90394]  
专题力学研究所_高温气体动力学国家重点实验室
通讯作者Yan, Xunshi
作者单位1.Univ Chinese Acad Sci, Sch Engn Sci, Beijing 100049, Peoples R China
2.Chinese Acad Sci, Inst Mech, State Key Lab High Temp Gas Dynam, Beijing 100190, Peoples R China
3.Tsinghua Univ, Inst Nucl & New Energy Technol, Beijing 100084, Peoples R China
推荐引用方式
GB/T 7714		 Yan, Xunshi,Liu, Yang,Zhang, Chen-an,et al. Multiresolution Hypergraph Neural Network for Intelligent Fault Diagnosis[J]. IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT,2022,71:10.
APA Yan, Xunshi,Liu, Yang,Zhang, Chen-an,刘洋,&张陈安.(2022).Multiresolution Hypergraph Neural Network for Intelligent Fault Diagnosis.IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT,71,10.
MLA Yan, Xunshi,et al."Multiresolution Hypergraph Neural Network for Intelligent Fault Diagnosis".IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 71(2022):10.

入库方式： OAI收割

来源：力学研究所

浏览0
下载0
收藏0
其他版本

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。