Adaptive Attention-Driven Few-Shot Learning for Robust Fault Diagnosis-Reference-Cited by-同舟云学术

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Adaptive Attention-Driven Few-Shot Learning for Robust Fault Diagnosis

Published:2024-08-15 Issue:16 Volume:24 Page:26034-26043
ISSN:1530-437X
Container-title:IEEE Sensors Journal
language:
Short-container-title:IEEE Sensors J.

Author:

Wang Zhe¹^ORCID,Ding Yi¹^ORCID,Han Te²^ORCID,Xu Qiang³^ORCID,Yan Hong⁴^ORCID,Xie Min¹^ORCID

Affiliation:

1. Department of Systems Engineering, City University of Hong Kong, Kowloon Tong, Hong Kong

2. School of Management, Beijing Institute of Technology, Beijing, China

3. School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China

4. Centre for Intelligent Multidimensional Data Analysis, Pak Shek Kok, Hong Kong

Funder

National Natural Science Foundation of China

Research Grant Council of Hong Kong

Hong Kong Innovation and Technology Commission

Sichuan Science and Technology Program

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx8/7361/10637303/10591709.pdf?arnumber=10591709

Reference43 articles.

1. A perspective survey on deep transfer learning for fault diagnosis in industrial scenarios: Theories, applications and challenges

2. Bayesian variational transformer: A generalizable model for rotating machinery fault diagnosis

3. Uncertainty-aware deep learning for reliable health monitoring in safety-critical energy systems

4. Health Monitoring of Plate Structures Based on Tomography With Combination of Guided Wave Transmission and Reflection

5. Application of Variational Mode Decomposition to Feature Isolation and Diagnosis in a Wind Turbine

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