Reactor fault early warning method based on improved LSTM
To solve the shortcomings of over-fitting and lack of generality of traditional reactor fault early warning methods,a reactor fault early warning method based on short-term and short-term memory neural network and twin neural network(hereinafter referred to as HDDse)is studied.This method combines the advantages of long and short term memory neural network and twin neural network.LSTM structure is used to learn the dynamic change behavior of reactor under healthy state.Besides,twin network structure can re-duce the learning efficiency of reactor information mapping to high-dimensional space.This method has been successfully applied in reactor fault diagnosis.The experiment results show that HDDse can greatly im-prove the accuracy of reactor fault early warning compared with the existing reactor fault early warning meth-ods.
reactor fault predictionearly warning modellong and short term memory neural networktwin neural networkfault diagnosis