Real-time simulations to enhance distributed on-line monitoring and fault detection in Pressurized Water Reactors-Reference-Cited by-同舟云学术

Real-time simulations to enhance distributed on-line monitoring and fault detection in Pressurized Water Reactors

Published:2017-11 Issue: Volume:109 Page:557-573
ISSN:0306-4549
Container-title:Annals of Nuclear Energy
language:en
Short-container-title:Annals of Nuclear Energy

Author:

Peng Min-jun,Wang Hang,Yang Xu,Liu Yong-kuo,Guo Liang-zhuang,Li Wei,Jiang Nan

Publisher

Elsevier BV

Subject

Nuclear Energy and Engineering

Reference27 articles.

1. Data-driven approach for fault detection and diagnosis of turbine in thermal power plant using independent component analysis (ICA);Ajami;Electric. Power Energy Syst.,2012

2. Correlating hardware fault detection information from distributed control systems to isolate and diagnose a fault in pressurized water reactors;Cilliers;Ann. Nucl. Energy,2013

3. Fault detection and characterisation in pressurized water reactors using real-time simulations;Cilliers;Ann. Nucl. Energy,2011

4. Adapting plant measurement data to improve hardware fault detection performance in pressurized water reactors;Cilliers;Ann. Nucl. Energy,2012

5. Data driven approach for fault detection and diagnosis of boiler system in coal fired power plant using principal component analysis;Daneshvar;Int. Rev. Automat. Control (IREACO),2010

Cited by 11 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. Enhancing interpretability in neural networks for nuclear power plant fault diagnosis: A comprehensive analysis and improvement approach;Progress in Nuclear Energy;2024-09

2. Development of online validation and monitoring system for the thermal performance of nuclear power plant in service;Annals of Nuclear Energy;2024-01

3. A novel transfer CNN with spatiotemporal input for accurate nuclear power fault diagnosis under different operating conditions;Annals of Nuclear Energy;2023-12

4. Development and validation of machine learning-based transient identification models in a liquid-fueled molten salt reactor system;Nuclear Engineering and Design;2023-12

5. Feature extraction for subtle anomaly detection using semi-supervised learning;Annals of Nuclear Energy;2023-02