| Multiresolution Hypergraph Neural Network for Intelligent Fault Diagnosis | |
Yan,Xunshi1; Liu Y(刘洋)2,3 ; Zhang CA(张陈安)2
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| Corresponding Author | Yan, Xunshi([email protected]) |
| Source Publication | IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
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| 2022 | |
| Volume | 71Pages:10 |
| ISSN | 0018-9456 |
| Abstract | 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. |
| Keyword | Convolution Fault diagnosis Correlation Task analysis Machinery Machine learning algorithms Laplace equations Fault diagnosis graph convolutional network (GCN) hypergraph hypergraph neural network (HGNN) multiresolution |
| DOI | 10.1109/TIM.2022.3212532 |
| Indexed By | SCI ; EI |
| Language | 英语 |
| WOS ID | WOS:000871033400009 |
| WOS Research Area | Engineering ; Instruments & Instrumentation |
| WOS Subject | Engineering, Electrical & Electronic ; Instruments & Instrumentation |
| Funding Project | National Science and Technology Major Project of China[ZX069] ; Strategic Priority Research Program of Chinese Academy of Sciences[XDA17030100] |
| Funding Organization | National Science and Technology Major Project of China ; Strategic Priority Research Program of Chinese Academy of Sciences |
| Classification | 二类/Q1 |
| Ranking | 2 |
| Contributor | Yan, Xunshi |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://dspace.imech.ac.cn/handle/311007/90395 |
| Collection | 高温气体动力学国家重点实验室 |
| Affiliation | 1.Tsinghua Univ, Inst Nucl & New Energy Technol, Beijing 100084, Peoples R China; 2.Chinese Acad Sci, Inst Mech, State Key Lab High Temp Gas Dynam, Beijing 100190, Peoples R China; 3.Univ Chinese Acad Sci, Sch Engn Sci, Beijing 100049, Peoples R China |
| Recommended Citation GB/T 7714 | Yan,Xunshi,Liu Y,Zhang CA. Multiresolution Hypergraph Neural Network for Intelligent Fault Diagnosis[J]. IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT,2022,71:10.Rp_Au:Yan, Xunshi |
| APA | Yan,Xunshi,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. |
| Files in This Item: | Download All | |||||
| File Name/Size | DocType | Version | Access | License | ||
| Jp2022FA592.pdf（2363KB） | 期刊论文 | 出版稿 | 开放获取 | CC BY-NC-SA | View Download | |
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