Process fault detection based on dynamic kernel slow feature analysis ?
A nonlinear dynamic process monitoring method is presented. The proposed method can extract the inherent slow features from the high-dimensional observed data. A statistic index is built based on slow features to carry out process monitoring. The method is more sensitive to process faults than the conventional KPCA-based method.
fault detection slow feature analysis nonlinear dynamic process kernel principal component analysis