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New Adaptive Kernel Principal Component Analysis for Nonlinear Dynamic Process Monitoring |
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PP: 1833-1845 |
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Author(s) |
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Chouaib CHAKOUR,
Mohamed Faouzi HARKAT,
Messaoud DJEGHABA,
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Abstract |
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| 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 AKPCAmodel 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. |
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