Chen Honghua, Cen Jian, Yang Zhuohong, Si Weiwei, Cheng Hongchao
School of Automation, Guangdong Polytechnic Normal University, Guangzhou 510665, China.
Guangzhou Intelligent Building Equipment Information Integration and Control Key Laboratory, Guangzhou 510665, China.
ACS Omega. 2022 Sep 12;7(38):34389-34400. doi: 10.1021/acsomega.2c04017. eCollection 2022 Sep 27.
Deep learning provides new ideas for chemical process fault diagnosis, reducing potential risks and ensuring safe process operation in recent years. To address the problem that existing methods have difficulty extracting the dynamic fault features of a chemical process, a fusion model (CS-IMLSTM) based on a convolutional neural network (CNN), squeeze-and-excitation (SE) attention mechanism, and improved long short-term memory network (IMLSTM) is developed for chemical process fault diagnosis in this paper. First, an extended sliding window is utilized to transform data into augmented dynamic data to enhance the dynamic features. Second, the SE is utilized to optimize the key fault features of augmented dynamic data extracted by CNN. Then, IMLSTM is used to balance fault information and further mine the dynamic features of time series data. Finally, the feasibility of the proposed method is verified in the Tennessee-Eastman process (TEP). The average accuracies of this method in two subdata sets of TEP are 98.29% and 97.74%, respectively. Compared with the traditional CNN-LSTM model, the proposed method improves the average accuracies by 5.18% and 2.10%, respectively. Experimental results confirm that the method developed in this paper is suitable for chemical process fault diagnosis.
近年来，深度学习为化工过程故障诊断提供了新思路，降低了潜在风险并确保了过程的安全运行。针对现有方法难以提取化工过程动态故障特征的问题，本文提出了一种基于卷积神经网络（CNN）、挤压激励（SE）注意力机制和改进长短期记忆网络（IMLSTM）的融合模型（CS-IMLSTM）用于化工过程故障诊断。首先，利用扩展滑动窗口将数据转换为增强动态数据以增强动态特征。其次，利用SE优化CNN提取的增强动态数据的关键故障特征。然后，使用IMLSTM平衡故障信息并进一步挖掘时间序列数据的动态特征。最后，在田纳西-伊斯曼过程（TEP）中验证了该方法的可行性。该方法在TEP的两个子数据集上的平均准确率分别为98.29%和97.74%。与传统的CNN-LSTM模型相比，该方法的平均准确率分别提高了5.18%和2.10%。实验结果证实了本文所提出的方法适用于化工过程故障诊断。
