Multivariate Statistical Process Control and Signature

This paper proposes novel eigenfactor multivariate quality control techniques that are capable of detecting covariance structure change as well as providing information about the real nature of the change occurring in the process. Eigenspace analysis is especially advantageous in data-rich manufacturing processes because it can reduce the data dimension, much like principal components analysis, yet retains the ability to detect and distinguish between subtle covariance structure change