Fault diagnosis method for nuclear power plants based on integrated neural networks and logical fusion

2009 ◽

Cited By ~ 1

Author(s):

Yong-kuo Liu ◽

Hong Xia ◽

Chun-li Xie

Keyword(s):

Neural Networks ◽

Fault Diagnosis ◽

Data Fusion ◽

Nuclear Power ◽

Power Plants ◽

Nuclear Power Plants ◽

Evidence Theory ◽

Source Information ◽

Diagnosis Method ◽

Data Level

Data fusion is a method which suits for complex system fault diagnosis such as nuclear power plants, and is multi-source information processing technology. In this paper, the data fusion information hierarchical thinking used and the nuclear power plants fault diagnosis divided into three levels. In data level data mining method adopted to handle data and reduction attributes. In feature level three parallel neural networks used to deal with attributes reduction of data level and the outputs of three networks are as the basic probability assignment of Dempster-Shafer (D-S) evidence theory. The improved D-S evidence theory synthesizes the outputs of neural networks in decision level, which conquers the traditional D-S evidence theory limitation that cannot dispose conflict information. The diagnosis method is tested through using correlation data of document. The test results indicate that the data fusion diagnosis system can diagnose nuclear power plants faults accurately and the method which has a certain applicable value in use.

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

10.1115/1.0000405v ◽

2021 ◽

Author(s):

Botao Jiang ◽

Xinbo Huang ◽

Jian Zhou

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Fault Diagnosis ◽

Condition Monitoring ◽

Nuclear Power ◽

Power Plants ◽

Nuclear Power Plants ◽

Concise Review ◽

Artificial Neural

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The Fault Diagnosis using Two-Steps Neural Networks for Nuclear Power Plants

Journal of Korean institute of intelligent systems ◽

10.5391/jkiis.2002.12.2.129 ◽

2002 ◽

Vol 12 (2) ◽

pp. 129-134 ◽

Cited By ~ 1

Author(s):

Hyeon Bae ◽

Soon-Il Kwon ◽

Jong-Kyu Lee ◽

Chi-Kwon Song ◽

Sung-Shin Kim

Keyword(s):

Neural Networks ◽

Fault Diagnosis ◽

Nuclear Power ◽

Power Plants ◽

Nuclear Power Plants

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

Volume 2: Nuclear Policy; Nuclear Safety, Security, and Cyber Security; Operating Plant Experience; Probabilistic Risk Assessments; SMR and Advanced Reactors ◽

10.1115/icone2020-16334 ◽

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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