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Détection et localisation de défauts par analyse en composantes principales

Abstract : The aim of this thesis is to study the fault detection and isolation using principal components analysis (PCA). In the first chapter the fundamental principles of linear p.rincipal componerit analysis are presented. PCA is used to model normal process behaviour. In the second chapter the problem of fault detection and isolation based on linear PCA is tackled. On the basis of the analysis of the classical detection indices, a new fault detection index based on the last principal components is developed. For fault isolation, the classical methods, using for instance the reconstruction principle or the contribution calculation, are adapted for the proposed fault detection index. The third chapter is focused on the nonlinear PCA. An extension of the PCA for nonlinear systems, combining principal curves algorithm and RBF networks, is proposed. For the determination of the number of principal components to be kept in the NLPCA model, we propose an extension of the unreconstructed variance criteria in the non-linear case. . Finally, an application, carried out in collaboration with air quality monitoring network in Lorraine AIRLOR, is presented in the fourth chapter. This application concerns the sensqr fault detection and isolation of this network by using the fault detection and isolation procedure developed in the linear case.
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Submitted on : Thursday, March 29, 2018 - 12:24:14 PM
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  • HAL Id : tel-01749705, version 1



Mohamed-Faouzi Harkat. Détection et localisation de défauts par analyse en composantes principales. Autre. Institut National Polytechnique de Lorraine, 2003. Français. ⟨NNT : 2003INPL033N⟩. ⟨tel-01749705⟩



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