(190a) Data Based Process Monitoring of Industrial Processes Using Multiscale Nonlinear Multivariate Statistical Methods

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
Sheriff, M. Z., Texas A&M University

Data Based Process Monitoring of Industrial Processes Using
Multiscale Nonlinear Multivariate Statistical Method.

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 an
industrial processes to ensure safety of the plant and maintain product quality
at desired level. One of the important steps in process monitoring is fault
detection and diagnosis. Multivariate statistical methods are data based fault
detection methods, which are powerful tools capable to handle noise and correlated
data set. Two commonly used multivariate methods are partial lease square (PLS)
and principle component analysis (PCA).

In this work wavelet-based approach is applied to developed
multivariate statistical model to improve fault detection performance. Wavelet
based approach provides three distinct advantages: denoise
the original signal, de-correlate auto-correlated data, and wavelet coefficients
have Gaussian distribution irrespective of original signal. Most of the
industrial processes are nonlinear in nature; to improve fault detection
performance of the nonlinear processes, Kernel based PLS algorithm is developed.
Our fault detection algorithm consists of wavelet decomposition of the input
data; the resultant data set is modeled at each scale using KPLS algorithm, and
generalized likelihood ration test (GLRT) is used to detect fault at individual
scales and at global scale; GLRT is a composite hypothesis testing method and has
shown better fault detection performance compared to conventional T2
and Q statistic [3]. Fault identification is carried out using contribution
plots, GLRT statistical values are computed for all the process variable to
identify the violated variables that contribute towards fault in the process.

The developed fault detection model is optimized based
on missed fault detection rate, false alarm rate and early fault detection.
These three criteria are also used to demonstrate the effectiveness of multiscale-KPLS based GLRT over convention KPLS model. KPLS
model can also be used as a nonlinear regression model to predict the quality
variables from continuous online variables. Fault detection performance is
illustrated through Tennessee Eastman process problem (TEP), which is a
continuous process problem based on Eastman chemical company. TEP simulator is used
to simulate wide variety of faults occurring in a chemical plant. Results show
that our method has superior performance.

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

References:


1.     Bakshi, Bhavik R. ÒMultiscale PCA with Application to Multivariate Statistical
Process Monitoring.Ó American Institute of Chemical Engineers. AIChE Journal 44, no. 7 (July 1998): 1596.

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.     Mansouri, M., Nounou,
M., Nounou, H., Karim, N.,
2016. "Kernel pca-based glrt
for nonlinear fault detection of chemical processes." Journal of Loss
Prevention in the Process Industries 26 (1), 129–139.

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