系统仿真学报 ›› 2018, Vol. 30 ›› Issue (2): 622-628.doi: 10.16182/j.issn1004731x.joss.201802031

• 仿真应用工程 • 上一篇    下一篇

一种改进CPLS算法及其在过程监控中的应用

李庆华, 潘丰, 赵忠盖   

  1. 江南大学轻工过程先进控制教育部重点实验室,江苏 无锡 214122
  • 收稿日期:2015-12-03 出版日期:2018-02-08 发布日期:2019-01-02
  • 作者简介:李庆华(1976-),女,湖北,博士生,研究方向为工业过程的建模与监控;潘丰(1963-),男,江苏,博士,教授,博导,研究方向为工业过程建模与软测量等。
  • 基金资助:
    国家自然科学基金(61273131)

Improved CPLS Algorithm and Its Application in Process Monitoring

Li Qinghua, Pan Feng, Zhao Zhonggai   

  1. Key Laboratory of Advanced Process Control for Light Industry Ministry of Education, Jiangnan University, Wuxi 214122, China
  • Received:2015-12-03 Online:2018-02-08 Published:2019-01-02

摘要: CPLS(Concurrent PLS)对PLS分解的过程变量和质量变量的残差和主元进行进一步的提取,从而将变量投影到五个子空间,并由此构建了对过程变量和质量变量信息的完整监控框架。但是,在CPLS中,假设残差为可以求解的确定值,而残差本质上为随机分布量。因此,采用随机模型及其基于随机模型的监控更能反应残差的特性。在基于CPLS的过程监控中,采用因子分析(FA)算法对PLS中的残差进行分析,建立了基于FA的改进CPLS模型,并构建了符合正态分布假设条件的监控指标,提高了监控指标与建模指标的一致性。

关键词: CPLS, 因子分析, 期望最大化(EM)算法, 过程监控

Abstract: Concurrent PLS (CPLS) further extracts information from the residuals of input variables and quality variables drawn by PLS, thus the raw data are projected into five subspaces. The process monitoring based CPLS provides a whole framework for the monitoring of input variables and quality variables. The model for residuals is developed by a deterministic manner while the residuals are inherently stochastic; therefore a probabilistic model is more proper for describing their features. This paper introduces factor analysis (FA) into CPLS, in which FA instead of PCA is used to analyze the residuals to develop the improved CPLS model, and the monitoring indices for checking the validity of variables satisfying Gaussian distribution are built to improve the consistence between the modeling objective and the monitoring indices.

Key words: CPLS, factor analysis, expectation maximization (EM) algorithm, process monitoring

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