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Diagnostic de systèmes non linéaires par analyse en composantes principales à noyau

Abstract : In this thesis, the diagnosis of a nonlinear system was performed using data analysis. Initially developed to analyze linear system, Principal Component Analysis (PCA) is coupled with kernel methods for detection, isolation and estimation of faults' magnitude for nonlinear systems. Kernel PCA consists in projecting data using a nonlinear mapping function into a higher dimensional space called feature space where the linear PCA is applied. Due to the fact that the projections are done using kernels, the detection can be performed in the feature space. However, estimating the magnitude of the fault requires the resolution of a nonlinear optimization problem. The variables' contributions make it possible to isolate and estimate these magnitudes. The variable with the largest contribution may be considered as faulty. In our work, we proposed new methods for the isolation and estimation phases for which previous work has some limitations. The new proposed method in this thesis is based on contributions under constraints. The effectiveness of the developed methods is illustrated on the simulated continuous stirred tank reactor (CSTR).
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Submitted on : Friday, May 17, 2019 - 3:10:56 PM
Last modification on : Thursday, March 17, 2022 - 11:07:34 AM


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Kwami Anani. Diagnostic de systèmes non linéaires par analyse en composantes principales à noyau. Automatique / Robotique. Université de Lorraine, 2019. Français. ⟨NNT : 2019LORR0026⟩. ⟨tel-02132788⟩



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