Multiattention-Based Feature Aggregation Convolutional Networks With Dual Focal Loss for Fault Diagnosis of Rotating Machinery Under Data Imbalance Conditions-Reference-Cited by-同舟云学术

Multiattention-Based Feature Aggregation Convolutional Networks With Dual Focal Loss for Fault Diagnosis of Rotating Machinery Under Data Imbalance Conditions

Published:2024 Issue: Volume:73 Page:1-11
ISSN:0018-9456
Container-title:IEEE Transactions on Instrumentation and Measurement
language:
Short-container-title:IEEE Trans. Instrum. Meas.

Author:

Xu Yadong¹^ORCID,Li Sheng²^ORCID,Yan Xiaoan³^ORCID,He Jianliang¹^ORCID,Ni Qing⁴^ORCID,Sun Yuxin¹^ORCID,Wang Yulin¹^ORCID

Affiliation:

1. School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing, China

2. Business School, Hohai University, Nanjing, China

3. School of Mechatronics Engineering, Nanjing Forestry University, Nanjing, China

4. School of Mechanical and Mechatronic Engineering, University of Technology Sydney, NSW, Australia

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/19/10367905/10379533.pdf?arnumber=10379533

Reference34 articles.

1. Global contextual multiscale fusion networks for machine health state identification under noisy and imbalanced conditions

2. Digital twin-driven partial domain adaptation network for intelligent fault diagnosis of rolling bearing

3. Attention-based multiscale denoising residual convolutional neural networks for fault diagnosis of rotating machinery

4. CFCNN: A novel convolutional fusion framework for collaborative fault identification of rotating machinery

5. An iterative frequency-domain envelope-tracking filter for dispersive signal decomposition in structural health monitoring

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