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

Your Location：

Home >

Browse articles >

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
- Chinese Journal of Chemical Engineering Vol. 23, Issue 8, Pages: 1357-1363(2015)
- 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
  CLC：
- Published：2015
- Accepted：
Scan QR Code
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：

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.

关键词

Keywords

references

Views

124

Downloads

CSCD

Alert me when the article has been cited

Submit

Tools

Publicity Resources

Randomized autoregressive dynamic slow feature analysis method for industrial process fault monitoring

Improved process monitoring using the CUSUM and EWMA-based multiscale PCA fault detection framework

Research on fault detection method for heat pump air conditioning system under cold weather

Fault diagnosis of chemical processes based on partitioning PCA and variable reasoning strategy

A novel multimode processmonitoring method integrating LCGMMwith modified LFDA

Related Author

Chuangyan Yang

Hancheng Wang

Xun Lang

Aimin Miao

Peng Li

Qingmin Xu

Alamin Idris

Syed Ali Ammar Taqvi

Related Institution

College of Automation, Zhongkai University of Agriculture and Engineering

School of Information Science & Engineering, Yunnan University

Department of Chemical and Engineering Sciences, Karlstad University, Karlstad

Department of Chemical Engineering, NED University of Engineering&Technology

Centre of Contaminant Control&Utilization(CenCoU), Universiti Teknologi PETRONAS, Bandar Seri Iskandar

⁰