%0 Journal Article %A CHEN Honghua %A CEN Jian %A LIU Xi %A YANG Zhuohong %T Research Progress of Deep Learning in Fault Diagnosis of Chemical Process Industry %D 2022 %R 10.3778/j.issn.1002-8331.2111-0368 %J Computer Engineering and Applications %P 48-62 %V 58 %N 13 %X Chemical process industry fault diagnosis(CPIFD) is an important branch of intelligent manufacturing. In recent years, deep learning has demonstrated unique advantages and potential in feature recognition and classification. Therefore, the research of CPIFD based on deep learning has received extensive attention from scholars. However, there are limited reviews about CPIFD based on deep learning in the published literature. Hence, the purpose of the paper is to offer the state-of-the-art reference for CPIFD and stimulate scholars to further explore the application of deep learning in CPIFD. Firstly, the development of CPIFD technology is described. Secondly, the fundamental theories of representative models in deep learning are expounded and their applications in CPIFD are reviewed. These models include convolutional neural network, deep belief network, stacked autoencoder, long short-term memory network and other emerging neural network models. Finally, the problems faced by deep learning in CPIFD are discussed and the outlooks on future directions worthy of research are also presented. %U http://cea.ceaj.org/EN/10.3778/j.issn.1002-8331.2111-0368