Dynamic process monitoring based on canonical global and local preserving projection analysis. (October 2021)

• Record Type: Journal Article
• Authors: Tang, Qiu; Liu, Yan; Chai, Yi; Huang, Chenghong; Liu, Bowen
• Is Part Of: Journal of process control. Volume 106(2021)
• Page Start: 221
• Page End: 232
• Publication Date: 2021-10
• Subjects: Dynamic process monitoring -- Fault detection -- Fault identification -- Serial correlation -- Spatial correlation -- Canonical GLPP analysis; Process control -- Periodicals; Fabrication -- Contrôle -- Périodiques; Process control; Periodicals; Electronic journals; 660.281
• DOI: 10.1016/j.jprocont.2021.09.009
• Languages: English
• ISSNs: 0959-1524

Abstract

Abstract: For the purpose of fault monitoring of dynamic processes, this study proposes a hybrid framework of canonical variate analysis (CVA) and global-local preserving projection (GLPP) and refer to it as canonical GLPP analysis. The construction of Laplacian matrix based on the Hankel matrix provides a novel technology for preserving projection analysis. The optimal projection matrix solved by the canonical GLPP analysis framework can simultaneously preserves time series correlations and spatial distribution information in dimension reduction. This method inherits the advantages of GLPP in handling high-dimensional data with manifold learning and the strength of CVA in self-correlation analysis with state-space model. The solution to the proposed method is formulated as a generalized eigenvalue problem. The effectiveness of the canonical GLPP analysis method in fault detection and identification of dynamic processes is verified with a case study on the Tennessee Eastman process. Compared with the Fisher discriminant analysis based CVA, dynamic PCA, dynamic GLPP and the GLPP method, the proposed canonical GLPP analysis framework provides a more robust and accurate paradigm for dynamic process monitoring. Graphical abstract: Highlights: Addressing the dynamic process monitoring based on manifold learning method. Preserving both spatial structure and time series information in dimension reduction. Expanding CVA from fault detection to fault diagnosis.