Modeling using dynamic factor analysis and its application in process monitoring

ZHAO Zhonggai; LIU Fei

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Modeling using dynamic factor analysis and its application in process monitoring

更新时间：2025-12-10

- Modeling using dynamic factor analysis and its application in process monitoring
- CIESC Journal Issue 1, Pages: 183-186(2009)
- 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 January 2009，
  
  Published：05 January 2009
- 稿件说明：
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ZHAO Zhonggai, LIU Fei. Modeling using dynamic factor analysis and its application in process monitoring[J]. CIESC Journal, 2009, (1): 183-186.
DOI：

ZHAO Zhonggai, LIU Fei. Modeling using dynamic factor analysis and its application in process monitoring[J]. CIESC Journal, 2009, (1): 183-186. DOI：

摘要

在复杂工业系统的监控中，因子分析（FA）方法不需要专业的机理知识，应用系统日常运行数据建立模型，充分考虑了模型误差的普遍意义，具有较大的推广价值。针对实际过程的动态特性，基于自回归（AR）方式扩展过程变量数据矩阵，本文提出一种动态因子分析（DFA）的数据建模方法，充分提取了变量的自相关信息和互相关信息。另一方面，将DFA引入过程监控中，构建统计量作为监控指标，分别衡量变量的特征信息和误差信息，从而实现对动态过程运行状况的监控与评估。在Tennessee-Eastman（TE）过程中的应用研究，反映了这种方法的优越性。

Abstract

In complicated industrial systems

without the first component model and just by use of routine data

factor analysis can model the process operation and take full consideration of general sense of model error

therefore it has a good application prospect.Aimed at the dynamic characteristics in the actual process

the paper proposes a novel method based on dynamic factor analysis (DFA) to model the process data.In DFA

the sample matrix is extended based on the auto-regressive (AR) model

so it can well extract the information of auto-correlation and cross-correlation among process variables.In order to evaluate the operational condition of the process

the paper constructs several statistics as the monitoring indices to measure the features and errors

respectively.The application in the Tennessee-Eastman (TE) process shows the superiority of the DFA-based method.

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