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.


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    Title :

    Dynamic Process Monitoring Based on Canonical Global and Local Preserving Projection Analysis


    Additional title:

    eng. Applications of Computational Methods


    Contributors:
    Yin, Hongpeng (author) / Zhou, Han (author) / Chai, Yi (author) / Tang, Qiu (author)


    Publication date :

    2025-04-16


    Size :

    21 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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