A Deep Belief Network-based Fault Detection Method for Nonlinear Processes. Issue 24 (2018)

• Record Type: Journal Article
• Authors: Tang, Peng; Peng, Kaixiang; Zhang, Kai; Chen, Zhiwen; Yang, Xu; Li, Linlin
• Is Part Of: IFAC-PapersOnLine. Volume 51:Issue 24(2018)
• Page Start: 9
• Page End: 14
• Publication Date: 2018
• Subjects: DBN -- Restrict Boltzmann Machine -- fault detection -- nonlinear processes -- TE process; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2018.09.522
• Languages: English
• ISSNs: 2405-8963

Abstract

Abstract: 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.