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

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Original languageEnglish
Article number7310889
Pages (from-to)1129-1137
Journal / PublicationIEEE Transactions on Automation Science and Engineering
Issue number2
Online published28 Oct 2015
Publication statusPublished - Apr 2016
Externally publishedYes


Projection to latent structures (PLS) and concurrent PLS are approaches for solving quality-relevant process monitoring. In this paper, a new approach called concurrent kernel PLS (CKPLS) is presented to detect faults comprehensively for nonlinear processes. The new model divides the nonlinear process and quality spaces into five subspaces: the co-varying, process-principal, process-residual, quality-principal, and quality-residual subspaces. The co-varying subspace reflects nonlinear relationship between quality variables and original process variables. The process-principal and process-residual subspaces reflect the principal variations and residuals, respectively, in the nonlinear process space. Further, the quality-principal and quality-residual subspaces reflect 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.

Research Area(s)

  • Concurrent kernel projection to latent structures (CKPLS), nonlinear process monitoring, process-relevant fault detection, quality-relevant fault detection

Citation Format(s)