Abstract
Artificial neural networks have been widely used in the research of fault diagnosis in various fields. The application of neural networks is limited due to the complex and interconnected systems of nuclear power plants. Based on the fault evolution process of the letdown flow in chemical and volume control system (RCV), the paper analyzes and decomposes the function of the letdown flow and uses the long short term memory (LSTM) network to create a method of fault diagnosis. After a certain amount of training in the nuclear power plant simulator, the accuracy rate of fault diagnosis is satisfactory.
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