(76b) Multiscale Representation of Improved Multi-Kernel Partial Least Square Technique for Process Monitoring of the Industrial Processes

Authors: 
Botre, C., Texas A&M University
Mansouri, M., Texas A&M University, Qatar
Nounou, H., Texas A&M University at Qatar
Nounou, M., Texas A&M University at Qatar
Karim, M. N., Texas A&M University

Multiscale
representation of improved Multi-Kernel Partial least square technique for process
monitoring of the industrial processes

Chiranjivi Botrea,
Majdi Mansourib, Mohamed N. Nounouc,
Hazem N. Nounoub
and M. Nazmul Karima

aArtie McFerrin Dept. of Chemical Engineering,
Texas A&M University, College Station, Texas 77843, USA

bElectrical
and Computer Engineering Program, Texas A&M University at Qatar, Doha,
QATAR,


cChemical Engineering Department, Texas A&M University at Qatar, Doha,
QATAR

Abstract


Process monitoring is an important aspect in the chemical
industries to ensure safe and proper operation and to maintain process
efficiency at the desired level. Data based process monitoring technique have been
successfully applied where accurate process model is not available. Process
monitoring can be carried out in two phases; fault
detection and fault diagnosis. In this work we have proposed Multiscale Multi-Kernel Partial Least Square (MS-MKPLS)
based moving window generalized likelihood ratio test (MW-GLRT) for the fault
detection and fault diagnosis is be carried out with the contribution plot
analysis.

Partial Least Square (PLS) is a popular input output
type fault detection model but this technique can be
effectively applied mostly to linear processes. Kernel extension of PLS
provides an effective technique for fault detection of nonlinear industrial
processes. Being an input output model, KPLS can also used as nonlinear
regression technique. Selection of kernel function and its parameter have a significant
impact on the fault detection performance of the KPLS algorithm, therefore in
this work we have proposed optimized multi-kernel PLS to enhance the fault
detection performance by performing multi-objective genetic algorithm optimization
to minimize missed detection rate, false alarm rate and mean square error of prediction
of the output variable. Wavelet function based multi-scale representation further
enhances the kernel method due to its ability to effectively separate the
deterministic and stochastic features of the data and has the ability to handle
the noise, non-normal distribution and auto-correlated data set. Fault
detection decision is based on the statistical test that is performed on the
residue obtained from the developed model. In our previous work we have showed
superior fault detection ability of the composite hypothesis method like
generalized likelihood ration test (GLRT) over convention methods like T2
test and Q2 test, in this work we have used moving window based GLRT
technique for fault detection.

The proposed MS-MKPLS based MW-GLRT methodology fault
detection performance is illustrated through Tennessee Eastman process problem
(TEP), which is a continuous process problem based on Eastman chemical company.
The fault detection results demonstrate effectiveness of the developed
methodology with lower missed detection rate and false alarm rate.

Keywords: KPLS, GLRT, wavelet
function, fault detection, Tennessee Eastman process.

References:


1.     Botre,
Chiranjivi, Majdi Mansouri,
Mohamed Nounou, Hazem Nounou, and M. Nazmul Karim. 2016. ÒKernel PLS-Based GLRT Method for Fault
Detection of Chemical Processes.Ó Journal of Loss Prevention in the Process
Industries
43 (September): 212–24.

2.     MacGregor, J.F., and T. Kourti.
ÒStatistical Process Control of Multivariate Processes.Ó Control Engineering
Practice 3, no. 3 (March 1995): 403–14.

3.     Teppola, Pekka, and Pentti Minkkinen. 2000.
ÒWavelet–PLS Regression Models for Both Exploratory Data Analysis and
Process Monitoring.Ó Journal of Chemometrics 14
(5-6): 383–99.

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