CIFASIS   20631
CENTRO INTERNACIONAL FRANCO ARGENTINO DE CIENCIAS DE LA INFORMACION Y DE SISTEMAS
Unidad Ejecutora - UE
artículos
Título:
New contributions to nonlinear process monitoring through Kernel Partial Least Squares
Autor/es:
JOSÉ LUIS GODOY; DAVID ZUMOFFEN; JORGE VEGA; JACINTO MARCHETTI
Revista:
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS
Editorial:
ELSEVIER SCIENCE BV
Referencias:
Lugar: Amsterdam; Año: 2014 vol. 135 p. 76 - 89
ISSN:
0169-7439
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 nonlinear 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 nonlinear process. The effectiveness of the proposed methods is confirmed by using simulation examples.