基于敏感核主成分的化工过程统计监控(英文)

• 作 者: 姜庆超 颜学峰
• 机 构: Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education,
• 出 处: Chinese Journal of Chemical Engineering
• 发表日期: 2013年06期
• 关键词: statistical process monitoring kernel principal component analysis sensitive ker
• 基金项目: 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

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 di-rectly 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 em-ployed to demonstrate the efficiency of SKPCA on online monitoring. The results indicate that the monitoring per-formance is improved significantly.