Deep Attention Relation Network: A Zero-Shot Learning Method for Bearing Fault Diagnosis Under Unknown Domains-Reference-Cited by-同舟云学术

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Deep Attention Relation Network: A Zero-Shot Learning Method for Bearing Fault Diagnosis Under Unknown Domains

Published:2023-03 Issue:1 Volume:72 Page:79-89
ISSN:0018-9529
Container-title:IEEE Transactions on Reliability
language:
Short-container-title:IEEE Trans. Rel.

Author:

Chen Zuoyi¹^ORCID,Wu Jun²^ORCID,Deng Chao¹^ORCID,Wang Xiaoqi²,Wang Yuanhang³

Affiliation:

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

2. School of Naval Architecture and Ocean Engineering, Huazhong University of Science and Technology, Wuhan, China

3. China Electronic Product Reliability and Environmental Testing Research Institute, Guangzhou, China

Funder

National Natural Science Foundation of China

Hubei Provincial Natural Science Foundation for Innovation Groups

Ministry of Industry and Information Technology of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Safety, Risk, Reliability and Quality

Link

http://xplorestaging.ieee.org/ielx7/24/10057590/09792444.pdf?arnumber=9792444

Reference40 articles.

1. Gearbox fault diagnosis based on Multi-Scale deep residual learning and stacked LSTM model

2. Bearing vibration data collected under time-varying rotational speed conditions

3. Residual deep subdomain adaptation network: A new method for intelligent fault diagnosis of bearings across multiple domains

4. Detecting the Blockage of the Sensing Lines of a Differential-Pressure Flow Sensor in a Dynamic Process Using Wavelet Transform Techniques

5. Deep Reinforcement Learning-Based Online Domain Adaptation Method for Fault Diagnosis of Rotating Machinery

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