Diagnosis of nonlinear systems using kernel principal component analysis

Published in Journal of Physics: Conference Series, 570:072004, 2014.; International audience; Technological advances in the process industries during the past decade haveresulted in increasingly complicated processes, systems and products. Therefore, recentresearches consider the challenges in their design and management for successful operation.While principal component analysis (PCA) technique is widely used for diagnosis, its structurecannot describe nonlinear related variables. Thus, an extension to the case of nonlinear systemsis presented in a feature space for process monitoring. Working in a high-dimensional featurespace, it is necessary to get back to the original space. Hence, an iterative pre-image techniqueis derived to provide a solution for fault diagnosis. The relevance of the proposed technique isillustrated on artificial and real dataset.