A New Adversarial Domain Generalization Network Based on Class Boundary Feature Detection for Bearing Fault Diagnosis-Reference-Cited by-同舟云学术

A New Adversarial Domain Generalization Network Based on Class Boundary Feature Detection for Bearing Fault Diagnosis

Published:2022 Issue: Volume:71 Page:1-9
ISSN:0018-9456
Container-title:IEEE Transactions on Instrumentation and Measurement
language:
Short-container-title:IEEE Trans. Instrum. Meas.

Author:

Li Jingde¹^ORCID,Shen Changqing¹^ORCID,Kong Lin²^ORCID,Wang Dong³^ORCID,Xia Min⁴^ORCID,Zhu Zhongkui¹^ORCID

Affiliation:

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

2. Chang Guang Satellite Technology Company Ltd., Changchun, China

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

4. Department of Engineering, Lancaster University, Lancaster, U.K.

Funder

National Natural Science Foundation of China

Research Project of State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Instrumentation

Link

http://xplorestaging.ieee.org/ielx7/19/9717300/09745907.pdf?arnumber=9745907

Reference30 articles.

1. Visualizing time-dependent data using dynamic t-SNE;rauber;Proc EuroVis,2016

2. Attention Recurrent Autoencoder Hybrid Model for Early Fault Diagnosis of Rotating Machinery

3. Deep learning algorithms for rotating machinery intelligent diagnosis: An open source benchmark study

4. Intelligent Bearing Fault Diagnosis Using Multi-Head Attention-Based CNN

5. A novel method for intelligent fault diagnosis of rolling bearings using ensemble deep auto-encoders

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