Adaptive multiblock kernel principal component analysis for monitoring complex industrial processes

Ying-wei ZHANG; Yong-dong TENG

doi:10.1631/jzus.C1000148

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Adaptive multiblock kernel principal component analysis for monitoring complex industrial processes

计算机与自动化 | Updated：2022-08-31

- Adaptive multiblock kernel principal component analysis for monitoring complex industrial processes
- Adaptive multiblock kernel principal component analysis for monitoring complex industrial processes
- 信息与电子工程前沿（英文） 2010年11卷第12期页码：948-955
- Affiliations：
  
  MOE Key Lab of Integrated Automation of Process Industry, Northeastern University, Shenyang 110004, China
- Author bio：
  
  E-mail:zhangyingwei@mail.neu.edu.cn
- Funds：
- DOI：10.1631/jzus.C1000148
  中图分类号：
- 收稿：2010-04-13，
  
  修回：2010-06-29，
  
  录用：2010-06-10，
  
  纸质出版：2010-12
- Accepted：
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Adaptive multiblock kernel principal component analysis for monitoring complex industrial processes[J]. 信息与电子工程前沿（英文）, 2010,11(12):948-955.

Ying-wei ZHANG, Yong-dong TENG. Adaptive multiblock kernel principal component analysis for monitoring complex industrial processes[J]. Frontiers of Information Technology & Electronic Engineering, 2010, 11(12): 948-955.
Adaptive multiblock kernel principal component analysis for monitoring complex industrial processes[J]. 信息与电子工程前沿（英文）, 2010,11(12):948-955. DOI： 10.1631/jzus.C1000148.

Ying-wei ZHANG, Yong-dong TENG. Adaptive multiblock kernel principal component analysis for monitoring complex industrial processes[J]. Frontiers of Information Technology & Electronic Engineering, 2010, 11(12): 948-955. DOI： 10.1631/jzus.C1000148.

摘要

Abstract

Multiblock kernel principal component analysis (MBKPCA) has been proposed to isolate the faults and avoid the high computation cost. However

MBKPCA is not available for dynamic processes. To solve this problem

recursive MBKPCA is proposed for monitoring large scale processes. In this paper

we present a new recursive MBKPCA (RMBKPCA) algorithm

where the adaptive technique is adopted for dynamic characteristics. The proposed algorithm reduces the high computation cost

and is suitable for online model updating in the feature space. The proposed algorithm was applied to an industrial process for adaptive monitoring and found to efficiently capture the time-varying and nonlinear relationship in the process variables.

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