Statistical Monitoring of Chemical Processes Based on Sensitive Kernel Principal Components

姜庆超; 颜学峰

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

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Statistical Monitoring of Chemical Processes Based on Sensitive Kernel Principal Components

过程系统工程与过程安全 | Updated：2026-01-06

- Statistical Monitoring of Chemical Processes Based on Sensitive Kernel Principal Components
- Statistical Monitoring of Chemical Processes Based on Sensitive Kernel Principal Components
- 中国化学工程学报（英文版） 2013年第6期页码：633-6431004-954160506-6
- Affiliations：
  
  Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education, East China University of Science and Technology,Shanghai,China,200237
- Author bio：
- Funds：
  
  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.
- DOI：10.1016/S1004-9541(13)60506-6
  中图分类号：
- 纸质出版：2013
- Accepted：
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姜庆超, 颜学峰. Statistical Monitoring of Chemical Processes Based on Sensitive Kernel Principal Components[J]. 中国化学工程学报（英文版）, 2013,(6):633-6431004-954160506-6.

JIANG Qingchao, YAN Xuefeng. Statistical Monitoring of Chemical Processes Based on Sensitive Kernel Principal Components[J]. Chinese Journal of Chemical Engineering, 2013, (6): 633-6431004-954160506-6.
姜庆超, 颜学峰. Statistical Monitoring of Chemical Processes Based on Sensitive Kernel Principal Components[J]. 中国化学工程学报（英文版）, 2013,(6):633-6431004-954160506-6. DOI： 10.1016/S1004-9541(13)60506-6.

JIANG Qingchao, YAN Xuefeng. Statistical Monitoring of Chemical Processes Based on Sensitive Kernel Principal Components[J]. Chinese Journal of Chemical Engineering, 2013, (6): 633-6431004-954160506-6. DOI： 10.1016/S1004-9541(13)60506-6.

摘要

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 T

statistic and squared prediction error δ

SPE

statistic and reduce missed detection rates. T

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 T

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.

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