Relation Awareness Network for Few-Shot Fine-Grained Fault Diagnosis-Reference-Cited by-同舟云学术

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Relation Awareness Network for Few-Shot Fine-Grained Fault Diagnosis

Published:2024-07-01 Issue:13 Volume:24 Page:20949-20958
ISSN:1530-437X
Container-title:IEEE Sensors Journal
language:
Short-container-title:IEEE Sensors J.

Author:

Xu Yan¹^ORCID,Ma Xinyao¹^ORCID,Wang Xuan¹^ORCID,Wang Jinjia²^ORCID,Tang Gang³^ORCID,Ji Zhong¹^ORCID

Affiliation:

1. School of Electrical and Information Engineering, Tianjin University, Tianjin, China

2. School of Information Science and Engineering, Yanshan University, Qinhuangdao, Hebei, China

3. College of Mechanical and Electrical Engineering, Beijing University of Chemical Technology, Beijing, China

Funder

National Natural Science Foundation of China

Science and Technology Development

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/7361/10577556/10530542.pdf?arnumber=10530542

Reference46 articles.

1. Rolling Bearing Fault Diagnosis Method Base on Periodic Sparse Attention and LSTM

2. Intelligent fault diagnosis of rolling bearings based on normalized CNN considering data imbalance and variable working conditions

3. Adversarial Domain-Invariant Generalization: A Generic Domain-Regressive Framework for Bearing Fault Diagnosis Under Unseen Conditions

4. A novel fault diagnosis algorithm for rotating machinery based on a sparsity and neighborhood preserving deep extreme learning machine

5. Deep Learning Based Approach for Bearing Fault Diagnosis

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