文献类型：期刊文献

中文题名：Fault diagnosis and process monitoring using a statistical pattern framework based on a self-organizing map

英文题名：Fault diagnosis and process monitoring using a statistical pattern framework based on a self-organizing map

作者：Song Yu[1];Jiang Qing-chao[1];Yan Xue-feng[1]

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

年份：2015

卷号：22

期号：2

起止页码：601

中文期刊名：Journal of Central South University

外文期刊名：JOURNAL OF CENTRAL SOUTH UNIVERSITY

收录：CSTPCD;;EI(收录号：20150900576281);Scopus;WOS:【SCI-EXPANDED(收录号:WOS:000349913400026)】；CSCD:【CSCD2015_2016】；

基金：Foundation item: Project(2013CB733605) supported by the National Basic Research Program of China; Project(21176073) supported by the National Natural Science Foundation of China; Project supported by the Fundamental Research Funds for the Central Universities, China

语种：英文

中文关键词：statistic pattern framework; self-organizing map; fault diagnosis; process monitoring

外文关键词：statistic pattern framework; self-organizing map; fault diagnosis; process monitoring

摘要：A multivariate method for fault diagnosis and process monitoring is proposed. This technique is based on a statistical pattern(SP) framework integrated with a self-organizing map(SOM). An SP-based SOM is used as a classifier to distinguish various states on the output map, which can visually monitor abnormal states. A case study of the Tennessee Eastman(TE) process is presented to demonstrate the fault diagnosis and process monitoring performance of the proposed method. Results show that the SP-based SOM method is a visual tool for real-time monitoring and fault diagnosis that can be used in complex chemical processes.Compared with other SOM-based methods, the proposed method can more efficiently monitor and diagnose faults.
A multivariate method for fault diagnosis and process monitoring is proposed. This technique is based on a statistical pattern (SP) framework integrated with a self-organizing map (SOM). An SP-based SOM is used as a classifier to distinguish various states on the output map, which can visually monitor abnormal states. A case study of the Tennessee Eastman (TE) process is presented to demonstrate the fault diagnosis and process monitoring performance of the proposed method. Results show that the SP-based SOM method is a visual tool for real-time monitoring and fault diagnosis that can be used in complex chemical processes. Compared with other SOM-based methods, the proposed method can more efficiently monitor and diagnose faults.