A novel framework that combines canonical variate analysis (CVA) with Global-Local Preserving Projection (GLPP) is proposed for fault monitoring in dynamic processes, referred to as canonical GLPP analysis. This method constructs a Laplacian matrix using a Hankel matrix, effectively capturing temporal correlations and underlying patterns. To solve the problem, Cholesky decomposition is applied to the covariance and cross-covariance of the Laplacian matrix, transforming it into a generalized eigenvalue problem. The resulting canonical GLPP analysis identifies an optimal projection matrix, uncovering dynamic variations by integrating both self-correlations and cross-correlations, while simultaneously preserving global and local structures as dictated by manifold learning. The performance of this method in fault detection and classification is demonstrated using a case study on the Tennessee Eastman process. Case study shows that the canonical GLPP analysis provides a more reliable and accurate framework for fault detection in dynamic processes.
Dynamic Process Monitoring Based on Canonical Global and Local Preserving Projection Analysis
eng. Applications of Computational Methods
16.04.2025
21 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
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