文献类型：期刊文献

中文题名：Fault Diagnosis in Chemical Process Based on Self-organizing Map Integrated with Fisher Discriminant Analysis

英文题名：Fault Diagnosis in Chemical Process Based on Self-organizing Map Integrated with Fisher Discriminant Analysis

作者：Chen Xinyi[1];Yan Xuefeng[1]

机构：[1]E China Univ Sci & Technol, Minist Educ, Key Lab Adv Control & Optimizat Chem Proc, Shanghai 200237, Peoples R China

年份：2013

卷号：21

期号：4

起止页码：382

中文期刊名：Chinese Journal of Chemical Engineering

外文期刊名：CHINESE JOURNAL OF CHEMICAL ENGINEERING

收录：CSTPCD;;EI(收录号：20132116351652);Scopus;WOS:【SCI-EXPANDED(收录号:WOS:000318887900006)】；CSCD:【CSCD2013_2014】；

基金：Supported by the National Basic Research Program of China (2013CB733600), the National Natural Science Foundation of China (21176073), the Doctoral Fund of Ministry of Education of China (20090074110005), the Program for New Century Excellent Talents in University (NCET-09-0346), Shu Guang Project (09SG29) and the Fundamental Research Funds for the Central Universities.

语种：英文

中文关键词：self-organizing maps; Fisher discriminant analysis; fault diagnosis; monitoring; Tennessee Eastman process

外文关键词：self-organizing maps; Fisher discriminant analysis; fault diagnosis; monitoring; Tennessee Eastman process

摘要：Fault diagnosis and monitoring are very important for complex chemical process. There are numerous methods that have been studied in this field, in which the effective visualization method is still challenging. In order to get a better visualization effect, a novel fault diagnosis method which combines self-organizing map (SOM) with Fisher discriminant analysis (FDA) is proposed. FDA can reduce the dimension of the data in terms of maximizing the separability of the classes. After feature extraction by FDA, SOM can distinguish the different states on the output map clearly and it can also be employed to monitor abnormal states. Tennessee Eastman (TE) process is employed to illustrate the fault diagnosis and monitoring performance of the proposed method. The result shows that the SOM integrated with FDA method is efficient and capable for real-time monitoring and fault diagnosis in complex chemical process.
Fault diagnosis and monitoring are very important for complex chemical process. There are numerous methods that have been studied in this field, in which the effective visualization method is still challenging. In order to get a better visualization effect, a novel fault diagnosis method which combines self-organizing map (SOM) with Fisher discriminant analysis (FDA) is proposed. FDA can reduce the dimension of the data in terms of maximizing the separability of the classes. After feature extraction by FDA, SOM can distinguish the different states on the output map clearly and it can also be employed to monitor abnormal states. Tennessee Eastman (TB) process is employed to illustrate the fault diagnosis and monitoring performance of the proposed method. The result shows that the SOM integrated with FDA method is efficient and capable for real-time monitoring and fault diagnosis in complex chemical process.