On-line nonlinear process monitoring based on sparse kernel principal component analysis

ZHAO Zhonggai; LIU Fei

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On-line nonlinear process monitoring based on sparse kernel principal component analysis

更新时间：2025-12-10

- On-line nonlinear process monitoring based on sparse kernel principal component analysis
- CIESC Journal Issue 7, Pages: 1773-1777(2008)
- 作者机构：
  
  江南大学自动化研究所
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Online First：05 July 2008，
  
  Published：05 July 2008
- 稿件说明：
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ZHAO Zhonggai, LIU Fei. On-line nonlinear process monitoring based on sparse kernel principal component analysis[J]. CIESC Journal, 2008, (7): 1773-1777.
DOI：

ZHAO Zhonggai, LIU Fei. On-line nonlinear process monitoring based on sparse kernel principal component analysis[J]. CIESC Journal, 2008, (7): 1773-1777. DOI：

摘要

核主元分析(KPCA)适合非线性过程的监控，但存在计算量大、实时性差等缺点。提出一种基于稀疏KPCA(SKPCA)的过程监控方法，先使用SKPCA对正常建模数据进行加权，少数权值大的数据基本能代表全部正常数据的信息，因此稀化了建模数据，然后根据稀化后的正常数据建立过程的KPCA模型，并提出监控指标，大大减少了计算量，提高了监控的实时性，最后以化工分离过程为对象，就KPCA与SKPCA的监控效果和实时性进行了详细的对比研究，结果表明了基于SKPCA监控方法的优越性。

Abstract

Kernel principal component analysis (KPCA) is suitable for nonlinear process monitoring

but it suffers many limitations

such as great calculation load

and poor real time performance. A monitoring method based on sparse KPCA (SKPCA) was proposed to decrease calculation load and improve real time monitoring. SKPCA was firstly used to weight the normal modeling data

and the minority of data with high weight could basically represent the information of the whole data

so modeling data could be largely reduced. Following this

KPCA model and the monitoring indices were built based on the sparse modeling data. In the end

taking a chemical separation process for example

KPCA and SKPCA were compared in terms of monitoring result and real time performance

and the superiority of the proposed SKPCA method was demonstrated.

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