An aligned mixture probabilistic principal component analysis for fault detection of multimode chemical processes

Yawei Yang; Yuxin Ma; Bing Song; Hongbo Shi

doi:10.1016/j.cjche.2015.01.014

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An aligned mixture probabilistic principal component analysis for fault detection of multimode chemical processes

过程系统工程与过程安全 | Updated：2026-01-06

- An aligned mixture probabilistic principal component analysis for fault detection of multimode chemical processes
- An aligned mixture probabilistic principal component analysis for fault detection of multimode chemical processes
- 中国化学工程学报（英文版） 2015年23卷第8期页码：1357-1363
- Affiliations：
  
  Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education, East China University of Science and Technology,Shanghai,China,200237
- Author bio：
- Funds：
- DOI：10.1016/j.cjche.2015.01.014
  中图分类号：
- 纸质出版：2015
- Accepted：
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Yawei Yang, Yuxin Ma, Bing Song, 等. An aligned mixture probabilistic principal component analysis for fault detection of multimode chemical processes[J]. 中国化学工程学报（英文版）, 2015,23(8):1357-1363.

Yawei Yang, Yuxin Ma, Bing Song, Hongbo Shi. An aligned mixture probabilistic principal component analysis for fault detection of multimode chemical processes[J]. Chin.J.Chem.Eng., 2015, 23(8): 1357-1363.
Yawei Yang, Yuxin Ma, Bing Song, 等. An aligned mixture probabilistic principal component analysis for fault detection of multimode chemical processes[J]. 中国化学工程学报（英文版）, 2015,23(8):1357-1363. DOI： 10.1016/j.cjche.2015.01.014.

Yawei Yang, Yuxin Ma, Bing Song, Hongbo Shi. An aligned mixture probabilistic principal component analysis for fault detection of multimode chemical processes[J]. Chin.J.Chem.Eng., 2015, 23(8): 1357-1363. DOI： 10.1016/j.cjche.2015.01.014.

摘要

Abstract

A novel approach named aligned mixture probabilistic principal component analysis (AMPPCA) is proposed in this study for fault detection of multimode chemical processes. In order to exploit within-mode correlations

the AMPPCA algorithm first estimates a statistical description for each operating mode by applyingmixture probabilistic principal component analysis (MPPCA). As a comparison

the combined MPPCA is employed where monitoring results are softly integrated according to posterior probabilities of the test sample in each local model. For exploiting the cross-mode correlations

which may be useful but are inadvertently neglected due to separately held monitoring approaches

a global monitoring model is constructed by aligning all local models together. In this way

bothwithin-mode and cross-mode correlations are preserved in this integrated space. Finally

the utility and feasibility of AMPPCA are demonstrated through a non-isothermal continuous stirred tank reactor and the TE benchmark process.

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