Statistical analysis and online monitoring for multimode processes with between-mode transitions

• Published in: Chemical engineering science Vol. 65; no. 22; pp. 5961 - 5975
• Main Authors: Zhao, Chunhui, Yao, Yuan, Gao, Furong, Wang, Fuli
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ltd 15.11.2010; Elsevier
• Subjects: Applied sciences; Chemical engineering; Communities; Cross-mode and between-mode subspace separation; Exact sciences and technology; Mathematical models; Mode-immune common subspace; Mode-subject specific subspace; Monitoring; Multimode; Multiset PCA (MsPCA); On-line systems; Statistical analysis; Strategy; Subspaces; Transition identification; Multimode; Cross-mode and between-mode subspace separation; Mode-subject specific subspace; Mode-immune common subspace; Transition identification; Multiset PCA (MsPCA); Correlation; Statistical analysis; Algorithm; Modeling; Design; Surveillance; Cross-mode and between-mode subspace; Correlation analysis; separation; Statistical model
• ISSN: 0009-2509; 1873-4405
• DOI: 10.1016/j.ces.2010.08.024

In the present work, an improved statistical analysis, modeling and monitoring strategy is proposed for multimode processes with between-mode transitions. The subject of analysis is multi-source measurement data, with each source of data corresponding to one operation mode. The basic assumption is that the underlying correlations among the different modes are similar to a certain extent and a multimode common community can thus be enclosed by some common bases immune to the mode changes. By making an adequate projection of measurement space, the mode-common subspace is separated and can be represented by a robust statistical model. The remaining mode-specific subspace would be more specific to different operation modes. Moreover, a between-mode transition identification algorithm is designed, which can distinguish the normal transition behaviors from those abnormal disturbances. The proposed method provides a detailed insight into the inherent nature of multimode processes from both inter-mode and inner-mode viewpoints. More process information is captured which enhances one’s understanding of the multimode problem. Its feasibility and performance are illustrated with a practical case.