Multiple-Failure Signal Validation in Nuclear Power Plants Using Artificial Neural Networks

1996 ◽  
Vol 113 (3) ◽  
pp. 368-374 ◽  
Author(s):  
Paolo F. Fantoni ◽  
Alessandro Mazzola
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Nuclear Power ◽  
Power Plants ◽  
Nuclear Power Plants ◽  
Signal Validation ◽  
Artificial Neural

Hybrid accident simulation methodology using artificial neural networks for nuclear power plants

2004 ◽  
Vol 160 (1-4) ◽  
pp. 207-224 ◽  
Author(s):  
Young Joon Choi ◽  
Hyun Koon Kim ◽  
Won Pil Baek ◽  
Soon Heung Chang
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Nuclear Power ◽  
Power Plants ◽  
Nuclear Power Plants ◽  
Simulation Methodology ◽  
Accident Simulation ◽  
Artificial Neural

Using Artificial Neural Networks for Predicting Mental Workload in Nuclear Power Plants Based on Eye Tracking

2019 ◽  
Vol 206 (1) ◽  
pp. 94-106 ◽  
Author(s):  
Yiqian Wu ◽  
Zhiyao Liu ◽  
Ming Jia ◽  
Cong Chi Tran ◽  
Shengyuan Yan
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Eye Tracking ◽  
Nuclear Power ◽  
Power Plants ◽  
Nuclear Power Plants ◽  
Mental Workload ◽  
Artificial Neural

Artificial Neural Networks in Condition Monitoring and Fault Diagnosis of Nuclear Power Plants: A Concise Review

2020 ◽  
Author(s):  
B. T. Jiang ◽  
J. Zhou ◽  
X. B. Huang
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Fault Diagnosis ◽  
Condition Monitoring ◽  
Nuclear Power ◽  
Power Plants ◽  
Nuclear Power Plants ◽  
Classification Problem ◽  
Concise Review ◽  
Artificial Neural

Abstract Artificial neural networks (ANNs) are recognized for their good properties in solving the non-linear classification problem. Especially, ANNs and their latest advancements in deep learning (DL) are blooming in artificial intelligence (AI) fields in the past few years. They have recently proven their abilities to handle some complex fault diagnosis problems. In the context of these backgrounds, this paper provides a concise review on the applications of ANNs to condition monitoring and fault diagnosis (CMFD) of nuclear power plants (NPPs). Firstly, a brief description of basic principle of ANNs are given. Then, a number of studies reported in both the journals and conferences are reviewed. These studies are divided into two categories according the application types of ANNs: shallow ANNs and deep ANNs. Finally, the conclusions and trends developed in the future are summarized.

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