(158b) Nonlinear Partial Least Square (PLS) Methods with Generalized Likelihood Ratio Test (GLRT) for Fault Detection and Diagnosis of Chemical Processes

Nonlinear partial least square (PLS) methods with generalized
likelihood ratio test (GLRT) for fault detection and diagnosis of chemical
processes. 

Chiranjivi Botre^a,
Majdi Mansouri^b, Mohamed N. Nounou^c,
Hazem N. Nounou^band M. Nazmul Karim^a

Abstract

Partial least squares (PLS) is an input output
technique which can be used for fault detection and diagnosis in chemical and
biochemical processes. In this work we have developed nonlinear PLS methods to
tackle the nonlinear chemical processes. Kernel partial least square analysis
(KPLS) and neural network partial least square (NNPLS) are applied with
generalized likelihood ratio test (GLRT) for fault detection. Radial bases
functions and polynomial functions are used as kernels in KPLS. The prediction
ability of KPLS is compared with the multikernel PLS, in which rather than
using one kernel function, multiple kernels are used in linear combination for
better predictability of the output.

Highly correlated variables in the input matrix is of less
importance to the model as we cannot extract any additional variability from
it, hence we have perform pretreatment on input data by partial correlation
analysis to remove the highly correlated variables from the input matrix. Contribution
plots are used for fault diagnosis; contribution plots compares
the residuals of the variables which result in faults. We have used mean square
error values to compare the prediction power of the models. Fault detection
performance and prediction power of the developed models are illustrated through
Tennessee Eastman process problem, which can be used to simulate wide variety
of faults occurring in a chemical plant based on Eastman chemical company. 

References:

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2.      S. Joe
Qin and T.J. McAvoy. "Nonlinear PLS modeling using neural networks."
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3.      Zhang, Yingwei, and S. Joe Qin. “Improved Nonlinear
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no. 12 (December 1, 2008)