Application of fuzzy neural network to the nuclear power plant in process fault diagnosis

2005 ◽  
Vol 4 (1) ◽  
pp. 34-38
Author(s):  
Liu Yong-Kuo ◽  
Xia Hong ◽  
Xie Chun-li
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Power Plant ◽  
Nuclear Power Plant ◽  
Nuclear Power ◽  
Fuzzy Neural Network ◽  
Fuzzy Neural ◽  
Process Fault Diagnosis

Application of Fuzzy Neural Network to Fault Diagnosis of Sensor with Mechanical Properties in Nuclear Power Plant

2013 ◽  
Vol 644 ◽  
pp. 68-71 ◽  
Author(s):  
Jin Yang Li ◽  
Hong Xia
Keyword(s):  
Neural Network ◽  
Mechanical Properties ◽  
Fault Diagnosis ◽  
Power Plant ◽  
Nuclear Power Plant ◽  
Nuclear Power ◽  
Fuzzy Neural Network ◽  
Back Propagation ◽  
Back Propagation Algorithm ◽  
Fuzzy Neural

In view of the sensor fault in nuclear power plant, it puts forward a method to fault diagnosis of sensor with mechanical properties based on fuzzy neural network. The method would be fuzzy logic control combined with neural network. It adjusted and corrected membership function parameters and network weights with back propagation algorithm. After the completion of fuzzy neural network training, it could get the credibility of sensor with mechanical properties real time. Taking pressurizer water-level sensor as the case, the simulation experiment results showed that the method is valid for the fault diagnosis of sensor with mechanical properties in nuclear power plant.

Fault diagnosis of nuclear power plant based on genetic-RBF neural network

2010 ◽  
Vol 39 (1/2/3) ◽  
pp. 159 ◽  
Author(s):  
Chun Ling Xie ◽  
Jen Yuan Chang ◽  
Xiao Cheng Shi ◽  
Jing Min Dai
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Power Plant ◽  
Nuclear Power Plant ◽  
Nuclear Power ◽  
Rbf Neural Network

Fuzzy Neural Network Modeling for Fault Diagnosis in Turbine Startup of a Power Plant

2005 ◽  
Author(s):  
Weiliang Chen ◽  
Guodong Xia ◽  
Hongyu Sun
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Power Plant ◽  
Neural Network Model ◽  
Fuzzy Neural Network ◽  
Fuzzy Theory ◽  
Network Modeling ◽  
Neural Network Modeling ◽  
Fuzzy Neural ◽  
Mode A

A fault set and a symptom set were established in order to exactly judge and to quickly dispose in turbine startup of a power plant. There are ten typical faults in the fault set and sixteen fault symptoms in the symptom set. In consideration of the various kinds of change directions and ranges of the fault symptom parameters, the fuzzy disposal of nine degrees is put forward to build a set of typical fault-character-sample mode. A neural network model for fault diagnosis was obtained by fuzzy theory and radial basis function, and it was validated by using evaluator. It shows that the fuzzy fault disposal and the swiftness of training constringency are very satisfied in turbine startup of this power plant.

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