Diagnosis of nonlinear systems using kernel principal component analysis
Résumé
Technological advances in the process industries during the past decade have
resulted in increasingly complicated processes, systems and products. Therefore, recent
researches consider the challenges in their design and management for successful operation.
While principal component analysis (PCA) technique is widely used for diagnosis, its structure
cannot describe nonlinear related variables. Thus, an extension to the case of nonlinear systems
is presented in a feature space for process monitoring. Working in a high-dimensional feature
space, it is necessary to get back to the original space. Hence, an iterative pre-image technique
is derived to provide a solution for fault diagnosis. The relevance of the proposed technique is
illustrated on artificial and real dataset.