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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    Titel :

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


    Beteiligte:
    Qiu, Xiaoyu (Autor:in) / Du, Xianjun (Autor:in)


    Erscheinungsdatum :

    20.10.2021


    Format / Umfang :

    1156713 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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