the classification accuracy of Tennessee Eastman (TE) chemical process fault diagnosis is low. In this study, a Long short recurrent neural network (lstm-rnn) model is proposed, which can effectively improve the defects of RNN recurrent neural network that gradients disappear and explode easily with time. Finally, the results of lstm-rnn model, BP model and RNN model are compared to verify the advantages of this method. It is found that lstm-rnn model has stable classification error and high classification accuracy, which effectively improves the fault diagnosis ability of te chemical process.


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    Title :

    Fault Diagnosis of TE Process Using LSTM-RNN Neural Network and BP Model


    Contributors:
    Qiu, Xiaoyu (author) / Du, Xianjun (author)


    Publication date :

    2021-10-20


    Size :

    1156713 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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