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

被引：29
|
作者：
Tang, Peng [1 ]
Peng, Kaixiang [1 ]
Zhang, Kai [1 ]
Chen, Zhiwen [2 ]
Yang, Xu [1 ]
Li, Linlin [1 ]
机构：
[1] Univ Sci & Technol Beijing, Sch Automat & Elect Engn, Minist Educ, Key Lab Knowledge Automat Ind Proc, Beijing 100083, Peoples R China
[2] Cent S Univ, Sch Informat Sci & Engn, Changsha 410083, Hunan, Peoples R China
来源：
IFAC PAPERSONLINE | 2018年 / 51卷 / 24期
基金：
国家重点研发计划;
关键词：
DBN; Restrict Boltzmann Machine; fault detection; nonlinear processes; TE process; DIAGNOSIS;
D O I：
10.1016/j.ifacol.2018.09.522
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
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.
引用
收藏
页码：9 / 14
页数：6
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