Dynamic monitoring of chemical processes based on latent variable auto-regressive algorithm

Junmiao TANG; Haizhen YU; Xuhua SHI; Chudong TONG

doi:10.11949/j.issn.0438-1157.20181180

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Dynamic monitoring of chemical processes based on latent variable auto-regressive algorithm

Process system engineering | 更新时间：2026-07-24

- Dynamic monitoring of chemical processes based on latent variable auto-regressive algorithm
- CIESC Journal Vol. 70, Issue 3, Pages: 987-994(2019)
- 作者机构：
  
  宁波大学信息科学与工程学院，浙江宁波 315211
- 作者简介：
- 基金信息：
- DOI：10.11949/j.issn.0438-1157.20181180
  CLC： TP 277
- Received：11 October 2018，
  
  Revised：2018-12-20，
  
  Online First：24 July 2026，
  
  Published：05 March 2019
- 稿件说明：
移动端阅览
唐俊苗, 俞海珍, 史旭华, 童楚东. 基于潜变量自回归算法的化工过程动态监测方法[J]. 化工学报, 2019, 70(3): 987-994

TANG Junmiao, YU Haizhen, SHI Xuhua, TONG Chudong. Dynamic monitoring of chemical processes based on latent variable auto-regressive algorithm[J]. CIESC Journal, 2019, 70(3): 987-994
唐俊苗, 俞海珍, 史旭华, 童楚东. 基于潜变量自回归算法的化工过程动态监测方法[J]. 化工学报, 2019, 70(3): 987-994 DOI： 10.11949/j.issn.0438-1157.20181180.

TANG Junmiao, YU Haizhen, SHI Xuhua, TONG Chudong. Dynamic monitoring of chemical processes based on latent variable auto-regressive algorithm[J]. CIESC Journal, 2019, 70(3): 987-994 DOI： 10.11949/j.issn.0438-1157.20181180.

摘要

从建立潜变量自回归（AR）模型的角度出发，提出了一种基于潜变量自回归（LVAR）算法的化工过程动态建模与监测方法，旨在提取动态潜变量的同时给出各潜变量的AR模型。LVAR算法在最小化潜变量的AR模型残差的约束下，通过同时搜寻投影变换向量与AR系数向量，实现了对动态潜变量的特征提取及其AR模型的建立。此外，LVAR算法通过先提取动态潜变量后提取静态成分信息的方式，有效地区分了采样数据中的自相关性与交叉相关性。在对比实验中，通过比较分析LVAR方法与其他三种典型的动态过程监测方法在经典化工过程对象上的故障监测结果，验证了LVAR方法在动态过程监测上的优越性与可靠性。

Abstract

From the viewpoint of constructing auto-regressive (AR) models for latent variables

the current paper proposed a dynamic modeling and monitoring approach for chemical processes based on latent variable auto-regressive (LVAR) algorithm. With respect to the requirement of minimizing AR model residual

the LVAR algorithm simultaneously searches for projecting vectors and AR coefficient vectors

so as to extracting dynamic latent variables and their corresponding AR models. In addition

the LVAR algorithm can efficiently distinguish the auto-correlated and cross-correlated relations inherited in the sampled data

by extracting the dynamic latent variables first and then the static component information. In the comparisons

the superiority and validity of the LVAR method are demonstrated by comparing the fault monitoring results in a classical chemical plant with other three counterparts.

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references

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