New contributions to non linear process monitoring through kernel partial least squares
Date
2013
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Abstract
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.
Keywords: KPLS Modeling, Fault Detection, Fault Diagnosis, Prediction Risk Assessment, Nonlinear
Processes.
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Keywords
nonlinear, process monitoring, Kernel Partial
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