Quality-Driven Kernel Projection to Latent Structure Model for Nonlinear Process Monitoring

A novel quality-driven kernel projection to latent structure (QKPLS) modeling scheme is proposed for concurrent quality-related and process-fault detection for nonlinear processes. Process data are initially mapped into a high-dimensional feature space by nonlinear mapping. The mapped data in the feature space are then projected by kernel representation into a process-dominant subspace that captures the main process variance and a process-residual subspace orthogonal to the process-dominant subspace. On the basis of the relationship with quality variables, the process-dominant subspace is further decomposed into two orthogonal subspaces, namely, a quality-related subspace that maximizes the covariance between the subspace and the quality variables and a quality-residual subspace orthogonal to the quality-related subspace. Afterward, three orthogonal subspaces are obtained, and monitoring statistics are established to achieve concurrent quality-related and process-fault detection. The application examples on a numerical example and Tennessee Eastman process verify the effectiveness of the QKPLS-based monitoring scheme.