Global–local preserving method of quality-related maximization and its application for process monitoring

Common multivariate statistical quality-related process monitoring methods often separate feature extraction from quality-related process modeling, which can lead to insufficient extraction of quality-related information. In this paper, a quality-related maximization model with global and local preservation constraints is proposed. The process data are mapped to a high-dimensional feature space using kernel projection, which better linearizes the nonlinear data. Kernel sparse representation local linear embedding is applied to adaptively determine local relationships. Based on these local relationships, global-local constraints are constructed, and quality-related features are extracted according to the principle of maximizing correlation with quality indicators, resulting in a low-dimensional embedding matrix. This embedding matrix is used for process monitoring by dividing the quality-related and quality-independent subspaces and constructing a monitoring statistical strategy. The effectiveness of the proposed method is verified using the Tennessee-Eastman process, and it is further applied to a fluid catalytic cracking process.