SCI和EI收录∣中国化工学会会刊

Chin.J.Chem.Eng. ›› 2013, Vol. 21 ›› Issue (6): 633-643.DOI: 10.1016/S1004-9541(13)60506-6

• PROCESS SYSTEMS ENGINEERING • Previous Articles     Next Articles

Statistical Monitoring of Chemical Processes Based on Sensitive Kernel Principal Components

JIANG Qingchao, YAN Xuefeng   

  1. 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:2013-07-03 Published:2013-06-28
  • 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.

Statistical Monitoring of Chemical Processes Based on Sensitive Kernel Principal Components

姜庆超, 颜学峰   

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

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 δSPE 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

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 δSPE 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