Identification of abnormal conditions is essential in the chemical process. With the rapid development of artificial intelligence technology, deep learning has attracted a lot of attention as a promising fault identification method in chemical process recently. In the high-dimensional data identification using deep neural networks, problems such as insufficient data and missing data, measurement noise, redundant variables, and high coupling of data are often encountered. To tackle these problems, a feature based deep belief networks (DBN) method is proposed in this paper. First, a generative adversarial network (GAN) is used to reconstruct the random and non-random missing data of chemical process. Second, the feature variables are selected by Spearman's rank correlation coefficient (SRCC) from high-dimensional data to eliminate the noise and redundant variables and, as a consequence, compress data dimension of chemical process. Finally, the feature filtered data is deeply abstracted, learned and tuned by DBN for multi-case fault identification. The application in the Tennessee Eastman (TE) process demonstrates the fast convergence and high accuracy of this proposal in identifying abnormal conditions for chemical process, compared with the traditional fault identification algorithms.
