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

Ning SHENG, Qiang LIU, S. Joe QIN, Tianyou CHAI

Research output: Journal PublicationsJournal Article (refereed)peer-review

48 Citations (Scopus)

Abstract

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.
Original languageEnglish
Article number7310889
Pages (from-to)1129-1137
Number of pages9
JournalIEEE Transactions on Automation Science and Engineering
Volume13
Issue number2
Early online date28 Oct 2015
DOIs
Publication statusPublished - Apr 2016
Externally publishedYes

Keywords

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

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