Abstract
Kernel principal component analysis is a type of nonlinear principal component analysis, to decouple the nonlinear correlation of variables by using kernel functions and integral operators, and by computing the principal components in the high dimensional feature space. A method of fault diagnosis for dynamic nonlinear system by dynamic kernel principal component analysis is presented in this paper, and the root of fault causes is isolated by the reconstructed variables with nonlinear least squares optimization. The simulations in the continuous stirred-tank reactor (CSTR) indicate that the performances of process monitoring and fault diagnosis by this presented method are superior to that by kernel principal component analysis.
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