Fault diagnosis based on feature enhancement and spatial adjacent region dropout strategy-Reference-Cited by-同舟云学术

登录注册会员服务联系我们

Fault diagnosis based on feature enhancement and spatial adjacent region dropout strategy

Published:2023-09-14 Issue:10 Volume:45 Page:
ISSN:1678-5878
Container-title:Journal of the Brazilian Society of Mechanical Sciences and Engineering
language:en
Short-container-title:J Braz. Soc. Mech. Sci. Eng.

Author:

Zhao Yunji^ORCID,Zhou Yuhang,Xu Xiaozhuo,Qin Baofu,Guo Xiangwei

Funder

National Natural Science Foundation of China

The Fundamental Research Funds for the Universities of Henan Province

The Key Technologies R &D Program of Henan Province of China

Publisher

Springer Science and Business Media LLC

Subject

Mechanical Engineering,General Engineering,Aerospace Engineering,Automotive Engineering,Industrial and Manufacturing Engineering,Applied Mathematics

Link

https://link.springer.com/content/pdf/10.1007/s40430-023-04426-0.pdf

Reference45 articles.

1. Li R, Zhuang L, Li Y, Shen C (2021) Intelligent bearing fault diagnosis based on scaled Ramanujan filter banks in noisy environments. IEEE Trans Instrum Meas 70:1–13. https://doi.org/10.1109/TIM.2021.3122184

2. Akbari M, Khoshnood AM (2021) A new feature selection-aided observer for sensor fault diagnosis of an industrial gas turbine. IEEE Sens J 21(16):18047–18054. https://doi.org/10.1109/JSEN.2021.3085209

3. Zheng H, Yang Y, Yin J, Li Y, Wang R, Xu M (2021) Deep domain generalization combining a priori diagnosis knowledge toward cross-domain fault diagnosis of rolling bearing. IEEE Trans Instrum Meas 70:1–11. https://doi.org/10.1109/TIM.2020.3016068

4. Mousavi M, Chaibakhsh A, Jamali A, Kordestani M, Saif M (2022) A new fault diagnosis approach for heavy-duty gas turbines. IEEE/ASME Trans Mechatron 27(5):3339–3349. https://doi.org/10.1109/TMECH.2021.3138834

5. Liu ZH, Lu BL, Wei HL, Chen L, Li XH, Rätsch M (2021) Deep adversarial domain adaptation model for bearing fault diagnosis. IEEE Trans Syst Man Cybern Syst 51(7):4217–4226. https://doi.org/10.1109/TSMC.2019.2932000

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线，采集、加工和组织学术论文而形成的新型学术文献查询和分析系统，可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容，当前同舟云学术共收录了国内外主流学术期刊6万余种，收集的期刊论文及会议论文总量共计约1.5亿篇，并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询！咨询电话：010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号京ICP备18003416号-3