Robust inferential control using kernel density methods
The use of kernel density estimation (KDE) methods to address the issue of control under process uncertainty and unreliability is investigated. It is shown how the KDE-derived joint probability density function of plant operational data can be used to assist in this task. It is also shown how the estimated density function can be used to support robust inference of important plant variables in addition to the detection and isolation of faults. (C) 2000 Elsevier Science Ltd. All rights reserved.
kernel density methods fault detection and isolation multivariate statistical process control control systems chemical process control