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1篇 您的检索式：作者名="Shanjun Mu"

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		题名	作者	年代	出处	被引量
	1	Fault diagnosis for distillation process based on CNN–DAE显示文摘Distillation is the most widely used operation for liquid mixture separation in the chemical industry. It is of great importance to detect and diagnose faults in distillation process. Due to the strong feedback and coupling of processes in a distillation column, it is difficult to use deep auto-encoders(DAEs) alone to achieve good results in detecting and diagnosing faults, in terms of accuracy and efficiency. This paper proposes a hybrid fault-diagnosis model based on convolutional neural networks(CNNs) and DAEs, by integrating the powerful capability of CNN in feature extraction and of DAE in classification. A case study was carried out with the distillation process of depropanization. It is shown that the proposed hybrid model is of good performance compared to other models, in terms of the accuracy of fault detection in such a process. Also, with the increase of structural layers of the CNN–DAE model, the diagnostic accuracy will be improved, with an optimal accuracy of 92.2%.	Chuankun Li Dongfeng Zhao Shanjun Mu Weihua Zhang Ning Shi Lening Li	2019	Chinese Journal of Chemical Engineering2019，27，3：	12

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