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
2025-04-16
21 pages
Article/Chapter (Book)
Electronic Resource
English
Industrial Process Monitoring Using Fisher Discriminant-Based Global-Local Preserving Projection
Springer Verlag | 2025
|Drone-Assisted Fingerprint Localization Based on Kernel Global Locally Preserving Projection
DOAJ | 2023
|Locality preserving and global discriminant projection with prior information
British Library Online Contents | 2010
|British Library Online Contents | 2012
|