Novel fault diagnosis scheme utilizing deep learning networks-Reference-Cited by-同舟云学术

登录注册会员服务联系我们

Novel fault diagnosis scheme utilizing deep learning networks

Published:2020-01 Issue: Volume:118 Page:103066
ISSN:0149-1970
Container-title:Progress in Nuclear Energy
language:en
Short-container-title:Progress in Nuclear Energy

Author:

Saeed Hanan A.,Peng Min-jun,Wang Hang,Zhang Bo-wen

Funder

Research of Online Monitoring and Operator Support Technology

Chinese national scholarship council

Publisher

Elsevier BV

Subject

Waste Management and Disposal,Energy Engineering and Power Technology,Safety, Risk, Reliability and Quality,Nuclear Energy and Engineering

Reference20 articles.

1. Continuous machine learning for abnormality identification to aid condition-based maintenance in nuclear power plant;Ayo-Imoru;Ann. Nucl. Energy,2018

2. Support vector ensemble for incipient fault diagnosis in nuclear plant components;Ayodeji,2018

3. Knowledge base operator support system for nuclear power plant fault diagnosis;Ayodeji;Prog. Nucl. Energy,2018

4. Operation characteristic of integrated pressurized water reactor under coordination control scheme;Du;Ann. Nucl. Energy,2015

5. Research on fault diagnosis methods for the reactor coolant system of nuclear power plant based on DS evidence theory;Gong;Ann. Nucl. Energy,2018

Cited by 61 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. Unsupervised anomaly detection of nuclear power plants under noise background based on convolutional adversarial autoencoder combining self-attention mechanism;Nuclear Engineering and Design;2024-11

2. Fault diagnosis in reactor coolant pump with an automatic CNN-based mixed model;Progress in Nuclear Energy;2024-10

3. A System Predictive Maintenance Framework for Advanced Reactors Using a Data-Driven Digital Twin;Nuclear Science and Engineering;2024-08-02

4. An intelligent fault detection and diagnosis monitoring system for reactor operational resilience: Unknown fault detection;Progress in Nuclear Energy;2024-06

5. A system diagnostic and prognostic framework based on deep learning for advanced reactors;Progress in Nuclear Energy;2024-05

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