上海交通大学学报 ›› 2022, Vol. 56 ›› Issue (10): 1397-1408.doi: 10.16183/j.cnki.jsjtu.2021.084

• 机械与动力工程 • 上一篇    下一篇

在线压缩核主元分析的自适应过程监控

郭金玉, 李文涛, 李元()   

  1. 沈阳化工大学 信息工程学院,沈阳 110142
  • 收稿日期:2021-03-18 出版日期:2022-10-28 发布日期:2022-11-03
  • 通讯作者: 李元 E-mail:li-yuan@mail.tsinghua.edu.cn
  • 作者简介:郭金玉(1975-),女,山东省聊城市人,博士,从事工业过程的故障检测与诊断研究.
  • 基金资助:
    国家自然科学基金重大项目(61490701);国家自然科学基金项目(61673279);辽宁省教育厅项目(LJ2019007)

Adaptive Process Monitoring of Online Reduced Kernel Principal Component Analysis

GUO Jinyu, LI Wentao, LI Yuan()   

  1. College of Information Engineering, Shenyang University of Chemical Technology, Shenyang 110142, China
  • Received:2021-03-18 Online:2022-10-28 Published:2022-11-03
  • Contact: LI Yuan E-mail:li-yuan@mail.tsinghua.edu.cn

摘要:

对于动态系统,传统的核主元分析(KPCA)方法处理的效果不理想.滑动窗口核主元分析方法能适应动态系统的正常参数漂移,但是该方法处理大量的样本时需要较长的运算时间.因此,提出一种在线压缩核主元分析的自适应过程监控方法.该方法在大量的样本中选定较小的训练集作为初始压缩集进行建模,对在线实时采集的数据进行分析,判断新的样本是否正常.若为正常样本,判断该样本是否加入压缩集中,在加入压缩集的同时自动更新在线KPCA模型.将该方法应用到数值例子和田纳西-伊斯曼(TE)过程,仿真结果验证了该方法的有效性.

关键词: 核主元分析, 在线压缩核主元分析, 滑动窗口, 过程监控

Abstract:

In the case of dynamic systems, the traditional kernel principal component analysis (KPCA) method does not perform well. The moving window kernel principal component analysis method can adapt to the normal parameter drift of dynamic systems, but it needs a longer computation time when processing large number of samples. Therefore, an adaptive process monitoring method for online reduced kernel principal component analysis is proposed. In this method, a small training set is selected as the initial reduced set in a large number of samples for modeling, and the online real-time collected data are analyzed to judge whether the new sample is normal or not. If it is a normal sample, the method judges whether the sample is added to the reduced set, and updates the online KPCA model automatically when adding to the reduced set. The proposed method is applied to a numerical example and the Tennessee-Eastman (TE) process. The simulation results show that the proposed method is effective and feasible.

Key words: kernel principal component analysis (KPCA), online reduced kernel principal component analysis (ORKPCA), moving window, process monitoring

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