Adaptive kernel principal component analysis for nonlinear dynamic process monitoring

Chakour Chouaib, Mohamed-Faouzi Harkat, Djeghaba Messaoud

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

Abstract

In this paper a new algorithms for adaptive kernel principal component analysis (AKPCA) is proposed for dynamic process monitoring. The proposed AKPCA algorithm combine two existing algorithm, the recursive weighted PCA (RWPCA) and the moving window kernel PCA algorithms. For fault detection and isolation, a set of structured residuals is generated by using a partial AKPCA models. Each partial AKPCA model is performed on subsets of variables. The structured residuals are utilized in composing an isolation scheme, according to a properly designed incidence matrix. The results for applying this algorithm on the nonlinear time varying processes of the Tennessee Eastman shows its feasibility and advantageous performances.

Original languageEnglish
Title of host publication2013 9th Asian Control Conference, ASCC 2013
DOIs
Publication statusPublished - 31 Oct 2013
Externally publishedYes
Event2013 9th Asian Control Conference, ASCC 2013 - Istanbul, Turkey
Duration: 23 Jun 201326 Jun 2013

Other

Other2013 9th Asian Control Conference, ASCC 2013
CountryTurkey
CityIstanbul
Period23/6/1326/6/13

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ASJC Scopus subject areas

  • Control and Systems Engineering

Cite this

Chouaib, C., Harkat, M-F., & Messaoud, D. (2013). Adaptive kernel principal component analysis for nonlinear dynamic process monitoring. In 2013 9th Asian Control Conference, ASCC 2013 [6606291] https://doi.org/10.1109/ASCC.2013.6606291