Monitoring Non-normal Data with Principal Component Analysis and Adaptive Density Estimation

The issue of monitoring non-normally distributed data with principal component analysis (PCA) is addressed through the application of density estimation for evaluating the quality of the principal component scores. Although kernel density estimation has been previously cited as a method for monitoring such data, mixture models are proposed here in order to reduce model complexity and computational effort. Furthermore, several adaptation strategies for the density estimators are developed and suggestions are provided on their use. A rapid thermal anneal case study demonstrates how the estimators outperform the traditional Hotelling´s T² statistic due to the presence of a first wafer effect.