首页|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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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 applying mixture prob-abilistic principal component analysis (MPPCA). As a comparison, the combined MPPCA is employed where mon-itoring 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 al local models together. In this way, both within-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.
Multimode process monitoringMixture probabilistic principal component analysisModel alignmentFault detection
Yawei Yang、Yuxin Ma、Bing Song、Hongbo Shi
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Key Laboratory of Advanced Control and 0ptimization for Chemical Processes of Ministry of Education, East China University of Science and Technology, Shanghai 200237, China
National Natural Science Foundation of ChinaShanghai Pujiang Program
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