The data of chemical processes often contains dynamic timing characteristics, and traditional fault detection has low usage of dynamic information, which limits the fault diagnosis performance. To address this problem,a new method of chemical process fault diagnosis based on an attention?enhanced encoder?decoder network model (AEN) was proposed. The coding part uses the LSTM to extract the feature information of the process data and combine it with the attention mechanism to utilize the dynamic information among the process data more effectively； the decoding part uses the LSTM and combines the context vector provided by the attention mechanism to provide more accurate state information for the softmax regression, and finally, the softmax regression is used to obtain the probability value of the fault category for each sample data. The introduction of the attention mechanism improves the efficiency of the model in using process dynamic information in the time domain. The proposed method is experimented with using Tennessee Eastman process data and compared with the results of standard PCA?SVM, DBN and ResNet, and the results show that the proposed method is more effective in diagnosing faults.
