Comprehensive Monitoring of Nonlinear Processes Based on Concurrent Kernel Projection to Latent Structures


Projection to latent structures (PLS) and concurrent PLS are approaches for solving quality-relevant method monitoring. In this paper, a new approach referred to as concurrent kernel PLS (CKPLS) is presented to detect faults comprehensively for nonlinear processes. The new model divides the nonlinear method and quality areas into 5 subspaces: the co-varying, process-principal, method-residual, quality-principal, and quality-residual subspaces. The co-varying subspace reflects nonlinear relationship between quality variables and original method variables. The process-principal and process-residual subspaces reflect the principal variations and residuals, respectively, within the nonlinear method space. More, the quality-principal and quality-residual subspaces mirror the principal variations and residuals, respectively, in the quality space. The proposed approach is demonstrated by a numerical simulation and an application of the Tennessee Eastman process.

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