A Deep Belief Network-based Fault Detection Method for Nonlinear Processes

Deep learning has been obtained extensive attention in many fields. In this paper, a fault detection based on deep belief network (DBN) method is proposed for nonlinear processes. Then the industrial processes abnormal monitoring is realized by test statistics, which is built by feature variables and residual variables produced by DBN. The Tennessee-Eastman (TE) process have been used to evaluate the efficiency of the proposed method. (C) 2018, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.