An industrial process monitoring method and its application with fractal-based structure preserving embedding

Abstract

Maintaining stable industrial process operation is a crucial research topic in modern process monitoring. However, industrial process data often contains many redundant correlated variables, which can disrupt the process of data dimensionality reduction. To address this, a novel dimensionality reduction method termed Fractal-based Structural Preservation Embedding (FSPE) is proposed. This introduces a new feature selection approach via fractal analysis, which effectively eliminates redundant variables without altering data orientation, enhancing interpretability. The data structures are then preserved at spatial and temporal scales by solving a dual-objective optimization function. Spatially, it preserves the local data manifold structure while considering global information. Temporally, it maintains time series by searching for nearest neighboring points in time. The efficacy of the FSPE monitoring method is verified through simulations involving the Tennessee Eastman Process (TEP) and the Electric Furnace Magnesia Furnace (EFMF).