Nonlinear Dynamic Process Monitoring Using Canonical Variate Analysis and Kernel Density Estimations

Abstract

Amongst process monitoring techniques the Principal Component Analysis (PCA) and the Partial Least Squares Regression Analysis (PLS) assume that the observations at different times are independent. However, for most industrial processes, these assumptions are invalid because of their dynamic features. For dynamic processes, the Canonical Variate Analysis (CVA) based approach is more appropriate than the PCA and the PLS based approaches. The CVA model is linear and control limits associated with the CVA are traditionally derived based on the Gaussian assumption. However, most industrial processes are non-linear and the Gaussian assumption is invalid for such processes so that techniques based on this assumption may not be able to correctly identify underline faults. In this work, a new monitoring technique using the CVA with control limits derived from the estimated probability density function through kernel density estimation (KDE) is proposed and applied to the Tennessee Eastman Process Plant. The proposed CVA with KDE approach is able to significantly improve the monitoring performance compared to other methods mentioned above.