Statistical process control using kernel PCA

In this paper, we present a robust multivariate statistical process control chart using kernel principal component analysis. The proposed control chart is effective in the detection of outliers, and its control limits are derived from the eigenanalysis of the Gaussian kernel matrix in the Hilbert feature space. Our experimental results show the much improved performance of the proposed control chart in comparison with existing multivariate monitoring and controlling charts.