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Communication Dans Un Congrès Année : 2015

Fault estimation of nonlinear processes using kernel principal component analysis

Maya Kallas
Gilles Mourot
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José Ragot

Résumé

The principal component analysis (PCA) is a well-know technique to detect, isolate and estimate faults affecting a system. However, PCA identifies only linear structures in a given dataset. In this paper, we propose a new technique to estimate the fault affecting nonlinear systems, within the frame of kernel machines. To this end, the kernel methods are combined to the PCA, the so-called kernel PCA (KPCA), to diagnose a nonlinear system. As KPCA is applied in a high dimensional feature space, it is necessary to get back to the input space where the estimation can be interpreted. We derived an iterative pre-image technique that minimizes the square prediction error and the distance between the estimation of a new measure and the one just before it. The relevance of the proposed technique is shown on simulated data.
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Dates et versions

hal-01135065 , version 1 (24-03-2015)

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Maya Kallas, Gilles Mourot, Didier Maquin, José Ragot. Fault estimation of nonlinear processes using kernel principal component analysis. 14th European Control Conference, ECC'15, Jul 2015, Linz, Austria. ⟨10.1109/ECC.2015.7331026⟩. ⟨hal-01135065⟩
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