Research on nonlinear dynamic processes monitoring based on WPG-KNMF
ZHANG Cheng
DENG Cheng-long
LI Yuan
Abstract：Aiming at the fault detection of nonlinear dynamic processes,a fault detection method based on the Wasser-stein distance projection gradient kernel non-negative matrix factorization(WPG-KNMF)is proposed.Firstly,the projec-tion gradient method is used to update the basis matrix and coefficient matrix in kernel non-negative matrix factorization(KNMF).Secondly,the Wasserstein distance combined with the sliding window method is used to construct new statis-tics for fault detection in high-dimensional feature space.In this paper,the iterative method in KNMF is improved to the projection gradient method.The nonlinear structure of the data is captured by the KNMF and the Wasserstein distance is combined to eliminate the influence of autocorrelation between samples.The proposed approach is tested in a numerical case and in the development and application of methods for actuator diagnosis in industrial control systems(DAMADICS)process.The experimental results indicate that the proposed approach has an advantage over conventional methods,such as the kernel principal component analysis(KPCA)and the kernel non-negative matrix factorization.
Keywords：kernel non-negative matrix factorizationnonlinear processdynamic processprojected gradientwasser-stein distancefault detection
Publication Date：2025-03-31
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：10( 569-578 )