Statistical Monitoring of Chemical Processes Based on Sensitive Kernel Principal Components

doi:10.1016/S1004-9541(13)60506-6

Chinese Journal of Chemical Engineering ›› 2013, Vol. 21 ›› Issue (6): 633-643.DOI: 10.1016/S1004-9541(13)60506-6

• 过程系统工程与过程安全 • 上一篇下一篇

Statistical Monitoring of Chemical Processes Based on Sensitive Kernel Principal Components

姜庆超, 颜学峰

Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education, East China University of Science and Technology, Shanghai 200237, China

收稿日期:2011-11-29 修回日期:2012-08-30 出版日期:2013-06-28 发布日期:2013-07-03
通讯作者: YAN Xuefeng
基金资助:
Supported by the 973 project of China (2013CB733600), the National Natural Science Foundation (21176073), the Doctoral Fund of Ministry of Education (20090074110005), the New Century Excellent Talents in University (NCET-09-0346), “Shu Guang” project (09SG29) and the Fundamental Research Funds for the Central Universities.

Statistical Monitoring of Chemical Processes Based on Sensitive Kernel Principal Components

JIANG Qingchao, YAN Xuefeng

Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education, East China University of Science and Technology, Shanghai 200237, China

Received:2011-11-29 Revised:2012-08-30 Online:2013-06-28 Published:2013-07-03
Supported by:
Supported by the 973 project of China (2013CB733600), the National Natural Science Foundation (21176073), the Doctoral Fund of Ministry of Education (20090074110005), the New Century Excellent Talents in University (NCET-09-0346), “Shu Guang” project (09SG29) and the Fundamental Research Funds for the Central Universities.

摘要/Abstract

摘要： The kernel principal component analysis (KPCA) method employs the first several kernel principal components (KPCs), which indicate the most variance information of normal observations for process monitoring, but may not reflect the fault information. In this study, sensitive kernel principal component analysis (SKPCA) is proposed to improve process monitoring performance, i.e., to deal with the discordance of T² statistic and squared prediction error δ_SPE statistic and reduce missed detection rates. T² statistic can be used to measure the variation directly along each KPC and analyze the detection performance as well as capture the most useful information in a process. With the calculation of the change rate of T² statistic along each KPC, SKPCA selects the sensitive kernel principal components for process monitoring. A simulated simple system and Tennessee Eastman process are employed to demonstrate the efficiency of SKPCA on online monitoring. The results indicate that the monitoring performance is improved significantly.

关键词: statistical process monitoring, kernel principal component analysis, sensitive kernel principal component, Tennessee Eastman process

Abstract: The kernel principal component analysis (KPCA) method employs the first several kernel principal components (KPCs), which indicate the most variance information of normal observations for process monitoring, but may not reflect the fault information. In this study, sensitive kernel principal component analysis (SKPCA) is proposed to improve process monitoring performance, i.e., to deal with the discordance of T² statistic and squared prediction error δ_SPE statistic and reduce missed detection rates. T² statistic can be used to measure the variation directly along each KPC and analyze the detection performance as well as capture the most useful information in a process. With the calculation of the change rate of T² statistic along each KPC, SKPCA selects the sensitive kernel principal components for process monitoring. A simulated simple system and Tennessee Eastman process are employed to demonstrate the efficiency of SKPCA on online monitoring. The results indicate that the monitoring performance is improved significantly.

Key words: statistical process monitoring, kernel principal component analysis, sensitive kernel principal component, Tennessee Eastman process

姜庆超, 颜学峰. Statistical Monitoring of Chemical Processes Based on Sensitive Kernel Principal Components[J]. Chinese Journal of Chemical Engineering, 2013, 21(6): 633-643.

JIANG Qingchao, YAN Xuefeng . Statistical Monitoring of Chemical Processes Based on Sensitive Kernel Principal Components[J]. Chin.J.Chem.Eng., 2013, 21(6): 633-643.

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Statistical Monitoring of Chemical Processes Based on Sensitive Kernel Principal Components

Statistical Monitoring of Chemical Processes Based on Sensitive Kernel Principal Components

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