A Robust Pca Modeling Method for Process Monitoring

A robust method for dealing with the gross errors in the data collected for PCA model is proposed. This method, using M-estimator based on the generalized t distribution, adaptively transforms the data in the score space in order to eliminate the effects of the outliers in the original data. The robust estimation of the covariance or correlation matrix is obtained by the proposed approach so that the accurate PCA model can be obtained for the process monitoring purpose. Comparisons with the conventional PCA modeling and other robust outlier’s replacement approaches are illustrated through a chemical engineering example. Keyword: Principal component analysis, multivariate outliers, robust estimation, winsorization, process monitoring.