Diagnostic de fonctionnement des systèmes dynamiques par analyse en composantes principales non linéaires

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This work focused on the diagnosis of dynamic systems based on multivariate statistical process mon- itoring (MSPM) approach, namely the Stacked Sparse Autoencoders (SSAE). Dynamic systems, such as electrical networks, industrial processes and biological systems, require accurate monitoring and diagnosis to ensure correct operation and safety. Principal Component Analysis (PCA) is a commonly used statistical technique to reduce the dimensionality of data and extract the most signifcant and relevent information. However, global linear PCA and Sparse PCA are only able to capture linear relationships and not take into account the nonlinear relationships and correlation present in dynamic systems which can be interpreted as non linear principal component analysis.