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

doi:10.1016/j.cjche.2015.01.014

Chin.J.Chem.Eng. ›› 2015, Vol. 23 ›› Issue (8): 1357-1363.DOI: 10.1016/j.cjche.2015.01.014

• PROCESS SYSTEMS ENGINEERING • Previous Articles Next Articles

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

Yawei Yang, Yuxin Ma, Bing Song, Hongbo Shi

Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education, East China University of Science and Technology, Shanghai 200237, China

Received:2014-09-03 Revised:2015-01-25 Online:2015-09-26 Published:2015-08-28
Contact: Hongbo Shi
Supported by:
Supported by the National Natural Science Foundation of China (61374140) and Shanghai Pujiang Program (12PJ1402200).

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

Yawei Yang, Yuxin Ma, Bing Song, Hongbo Shi

Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education, East China University of Science and Technology, Shanghai 200237, China

通讯作者: Hongbo Shi
基金资助:
Supported by the National Natural Science Foundation of China (61374140) and Shanghai Pujiang Program (12PJ1402200).

Abstract

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.

Key words: Multimode process monitoring, Mixture probabilistic principal component, analysis, Model alignment, Fault detection

摘要： 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: Multimode process monitoring, Mixture probabilistic principal component, analysis, Model alignment, Fault detection

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, Hongbo Shi. An aligned mixture probabilistic principal component analysis for fault detection of multimode chemical processes[J]. Chinese Journal of Chemical Engineering, 2015, 23(8): 1357-1363.

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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

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