INVESTIGADORES
GODOY Jose Luis
congresos y reuniones científicas
Título:
Geometric properties of kernel partial least squares for non-linear process monitoring
Autor/es:
J.L. GODOY; GERMAN A. BUSTOS; ALEJANDRO H. GONZÁLEZ; J.L. MARCHETTI
Lugar:
Roma
Reunión:
Conferencia; IMAACA 2011 - 5th International Conference on Integrated Modeling and Analysis in Applied Control and Automation (part of the 8th International Mediterranean and Latin American Modelling Multiconference); 2011
Resumen:
This work proposes a new strategy for monitoring
non-linear processes based on Kernel Partial Least Squares (KPLS). When
strongly non-linear process are considered, a PLS regression model could not be
enough accurate. So, the first stage of the proposed method is to map the input
data to a high-dimension space, where a linear regression model can be
obtained. Then, an implicit linear regression model relating the
high-dimension space with output space (output data) is obtained. This model
implicitly induces a decomposition of the high-dimension space into the Model
subspace and the complementary Residual subspace, being the vectors in the
first subspace the effective domain of the linear regression model. Finally,
once the space decomposition is understood, new statistics (metrics) for each
subspace are proposed to monitor the process and detect possible abnormal
behaviours. The effectiveness of the method is tested by means of a synthetic
simulation example from the literature.