An Intelligent Fault Diagnosis Method for Imbalanced Nuclear Power Plant Data Based on Generative Adversarial Networks-Reference-Cited by-同舟云学术

登录注册会员服务联系我们

An Intelligent Fault Diagnosis Method for Imbalanced Nuclear Power Plant Data Based on Generative Adversarial Networks

Published:2023-04-21 Issue:4 Volume:18 Page:3237-3252
ISSN:1975-0102
Container-title:Journal of Electrical Engineering & Technology
language:en
Short-container-title:J. Electr. Eng. Technol.

Author:

Dai Yuntao,Peng Lizhang,Juan Zhaobo,Liang Yuan,Shen Jihong,Wang Shujuan,Tan Sichao,Yu Hongyan,Sun Mingze

Publisher

Springer Science and Business Media LLC

Subject

Electrical and Electronic Engineering

Link

https://link.springer.com/content/pdf/10.1007/s42835-023-01491-2.pdf

Reference38 articles.

1. Hines JW, Uhrig RE (2005) Computational intelligence in nuclear engineering. Prog Nucl Energy 37(2):127–138

2. Rai A, Upadhyay SH (2016) A review on signal processing techniques utilized in the fault diagnosis of rolling element bearings. Tribol Int 96:289–306

3. Gao Z, Cecati C, Ding SX (2015) A Survey of fault diagnosis and fault tolerant techniques part I: fault diagnosis with model-based and signal-based approaches. IEEE Trans Ind Electron 62(6):3757–3767

4. Wang H, Peng MJ, Ayodeji A et al (2020) Advanced fault diagnosis method for nuclear power plant based on convolutional gated recurrent network and enhanced particle swarm optimization. Ann Nucl Energy 151:107934

5. Abbasi AR, Mahmoudi MR (2021) Application of statistical control charts to discriminate transformer winding defects. Electr Power Syst Res 191:106890

Cited by 3 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. Imbalanced data fault diagnosis method for nuclear power plants based on convolutional variational autoencoding Wasserstein generative adversarial network and random forest;Nuclear Engineering and Technology;2024-07

2. Self-improving few-shot fault diagnosis for nuclear power plant based on man-machine collaboration;Nuclear Engineering and Design;2024-04

3. A review: the application of generative adversarial network for mechanical fault diagnosis;Measurement Science and Technology;2024-03-27

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