A multi-scale orthogonal nonlinear strategy for multi-variate statistical process monitoring

In this paper a multi-scale nonlinear PCA strategy for process monitoring is proposed. The strategy utilizes the optimal wavelet decomposition in such a way that only the approximation and the highest detail functions are used, thus simplifying the overall structure and making the interpretation at each scale more meaningful. An orthogonal nonlinear PCA procedure is incorporated to capture the nonlinear characteristics with a minimum number of principal components. The proposed nonlinear strategy also eliminates the requirement of nonlinear functions relating the nonlinear principal scores to process measurements for Q-statistics as in other nonlinear PCA process monitoring approaches. In addition, the strategy is considerably robust to the presence of typical outliers.