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

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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… Click to show full abstract

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.

Keywords: driven kernel; quality; process; kernel projection; subspace; quality driven

Journal Title: IEEE Access
Year Published: 2019

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