Fault diagnosis of nonlinear processes based on structured adaptive kernel PCA

Chouaib Chakour, Mohamed-Faouzi Harkat, Messaoud Djeghaba

Research output: Contribution to conferencePaper

Abstract

In this paper a new algorithm for adaptive kernel principal component analysis (AKPCA) is proposed for dynamic process monitoring. The proposed AKPCA algorithm combine two existing algorithms, 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
Pages61-66
Number of pages6
DOIs
Publication statusPublished - 1 Jan 2013
Externally publishedYes
Event2013 3rd International Conference on Systems and Control, ICSC 2013 - Algiers, Algeria
Duration: 29 Oct 201331 Oct 2013

Other

Other2013 3rd International Conference on Systems and Control, ICSC 2013
CountryAlgeria
CityAlgiers
Period29/10/1331/10/13

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ASJC Scopus subject areas

  • Control and Systems Engineering

Cite this

Chakour, C., Harkat, M-F., & Djeghaba, M. (2013). Fault diagnosis of nonlinear processes based on structured adaptive kernel PCA. 61-66. Paper presented at 2013 3rd International Conference on Systems and Control, ICSC 2013, Algiers, Algeria. https://doi.org/10.1109/ICoSC.2013.6750836