Artículos de revistas
New contributions to non-linear process monitoring through kernel partial least squares
Fecha
2014-07Registro en:
Godoy, José Luis; Zumoffen, David Alejandro Ramon; Vega, Jorge Ruben; Marchetti, Jacinto Luis; New contributions to non-linear process monitoring through kernel partial least squares; Elsevier Science; Chemometrics and Intelligent Laboratory Systems; 135; 7-2014; 76-89
0169-7439
CONICET Digital
CONICET
Autor
Godoy, José Luis
Zumoffen, David Alejandro Ramon
Vega, Jorge Ruben
Marchetti, Jacinto Luis
Resumen
The kernel partial least squares (KPLS) method was originally focused on soft-sensor calibration for predicting online quality attributes. In this work, an analysis of the KPLS-based modeling technique and its application to non-linear process monitoring are presented. To this effect, the measurement decomposition, the development of new specific statistics acting on non-overlapped domains, and the contribution analysis are addressed for purposes of fault detection, diagnosis, and prediction risk assessment. Some practical insights for synthesizing the models are also given, which are related to an appropriate order selection and the adoption of the kernel function parameter. A proper combination of scaled statistics allows the definition of an efficient detection index for monitoring a non-linear process. The effectiveness of the proposed methods is confirmed by using simulation examples.