Few-Shot Class-Incremental Learning With Adjustable Pseudo-Incremental Sessions for Bearing Fault Diagnosis-Reference-Cited by-同舟云学术

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Few-Shot Class-Incremental Learning With Adjustable Pseudo-Incremental Sessions for Bearing Fault Diagnosis

Published:2024-06-15 Issue:12 Volume:24 Page:19543-19552
ISSN:1530-437X
Container-title:IEEE Sensors Journal
language:
Short-container-title:IEEE Sensors J.

Author:

Zhu Hongyan¹,Shen Changqing¹^ORCID,Wang Jiaan²,Chen Bojian¹^ORCID,Wang Dong³^ORCID,Zhu Zhongkui¹^ORCID

Affiliation:

1. School of Rail Transportation, Soochow University, Suzhou, China

2. School of Photoelectric Engineering, Changzhou Institute of Technology, Changzhou, China

3. Department of Industrial Engineering and Management and the State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai, China

Funder

National Natural Science Foundation of China

Suzhou Science Foundation

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/7361/10558706/10521474.pdf?arnumber=10521474

Reference25 articles.

1. A review on the application of blind deconvolution in machinery fault diagnosis

2. Self-supervised pretraining via contrast learning for intelligent incipient fault detection of bearings

3. Transfer Learning-Motivated Intelligent Fault Diagnosis Designs: A Survey, Insights, and Perspectives

4. Novel Convolutional Neural Network (NCNN) for the Diagnosis of Bearing Defects in Rotary Machinery

5. Intelligent fault diagnosis of rolling bearings under imbalanced data conditions using attention-based deep learning method

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