KEYWORDS: Principal component analysis, Nonlinear dynamics, Statistical modeling, Performance modeling, Dimension reduction, Data processing, Process modeling, Feature selection, Estimation theory
In this paper, we introduce a new technique for nonlinear monitoring process relying on kernel entropy principal component analysis (KEPCA). KEPCA can transform input data into high-dimensional feature space using the nonlinear kernel function and determine the number of principal components (PCs) based on the computation of the entropy. The retained PCs are the ones that explain the maximum entropy of data in the feature space. Then, we introduce a new approach to calculate the upper control limits (UCLs) for the squared prediction error (SPE) and the T2 Hotelling in the feature space based on the density estimation via the k-nearest neighbors (kNN) estimator. The abovementioned approaches were applied to fault detection for the benchmark Tennessee Eastman process (TE). Results were robust and supply better performance than KPCA.
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