Intelligent Fault Diagnosis for Variable Working Conditions Based on SAAFN and BICP-Reference-Cited by-同舟云学术

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Intelligent Fault Diagnosis for Variable Working Conditions Based on SAAFN and BICP

Published:2024-04-01 Issue:7 Volume:24 Page:10841-10852
ISSN:1530-437X
Container-title:IEEE Sensors Journal
language:
Short-container-title:IEEE Sensors J.

Author:

Xu Xuefang¹^ORCID,Yang Xu¹^ORCID,Qiao Zijian²^ORCID,Liang Pengfei³^ORCID,He Changbo⁴^ORCID,Shi Peiming¹^ORCID

Affiliation:

1. School of Electrical Engineering, Yanshan University, Qinhuangdao, China

2. School of Mechanical Engineering and Mechanics, Ningbo University, Ningbo, China

3. School of Mechanical Engineering, Yanshan University, Qinhuangdao, China

4. College of Electrical Engineering and Automation, Anhui University, Hefei, China

Funder

Hebei Provincial Natural Science Foundation

Natural Science Foundation of Anhui Province

University Synergy Innovation Program of Anhui Province

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/7361/10487994/10440010.pdf?arnumber=10440010

Reference38 articles.

1. Dual-Threshold Attention-Guided GAN and Limited Infrared Thermal Images for Rotating Machinery Fault Diagnosis Under Speed Fluctuation

2. Collaborative fault diagnosis of rotating machinery via dual adversarial guided unsupervised multi-domain adaptation network

3. FGDAE: A new machinery anomaly detection method towards complex operating conditions

4. Generalized MAML for few-shot cross-domain fault diagnosis of bearing driven by heterogeneous signals

5. Intelligent cross-machine fault diagnosis approach with deep auto-encoder and domain adaptation

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Transfer learning-based channel attention enhancement network combined with Gramian angular domain field for fault diagnosis;Measurement Science and Technology;2024-07-18

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