Development of multivariate statistical process monitoring using combination MLR-PCA method

The most popular types of process monitoring systems is Multivariate Statistical Process Monitoring (MSPM) which has the most practical method in handling the complicated large scale processes. This is due to the ability of the system in maximizing the usage of abundant historial process data, in su...

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Main Author:	Norliza, Ishak
Format:	Thesis
Language:	English English English
Published:	2015
Subjects:	T Technology (General) TP Chemical technology
Online Access:	http://umpir.ump.edu.my/id/eprint/12737/ http://umpir.ump.edu.my/id/eprint/12737/ http://umpir.ump.edu.my/id/eprint/12737/1/FKKSA%20-%20NORLIZA%20ISHAK%20-%20CD%209668.pdf http://umpir.ump.edu.my/id/eprint/12737/2/FKKSA%20-%20NORLIZA%20ISHAK%20-%20CD%209668%20-%20CHAP%201.pdf http://umpir.ump.edu.my/id/eprint/12737/3/FKKSA%20-%20NORLIZA%20ISHAK%20-%20CD%209668%20-%20CHAP%203.pdf

id	ump-12737
recordtype	eprints
spelling	ump-127372016-04-07T02:52:41Z http://umpir.ump.edu.my/id/eprint/12737/ Development of multivariate statistical process monitoring using combination MLR-PCA method Norliza, Ishak T Technology (General) TP Chemical technology The most popular types of process monitoring systems is Multivariate Statistical Process Monitoring (MSPM) which has the most practical method in handling the complicated large scale processes. This is due to the ability of the system in maximizing the usage of abundant historial process data, in such a way that the original data dimensions are compressed and data variations preserved to certain extent in a set of transformed variables by way of linear combinations. Thus, the composite model is generally flexible regardless of the amount of variables that utilized. In this regard, conventional Principal Component Analysis (PCA) has been widely applied to conduct such compression function particularly for MSPM. However, the conventional PCA is a linear technique which results sometimes inappropriately employed especially in modeling processes that exhibit severe non-linear correlations. Therefore, a new solution is demanded, whereby the number of original variables can be reduced to certain extent (in terms of scales), while it still can maintain the variation as maximally as possible corresponding to the original, which are then transferable into monitoring statistics. One of the potential techniques available in addressing the issue is known as Multiple Linear Regression (MLR). The main objective of the technique is to predict a set of output values (criterion) based from a specified of linear function, which consists of a set of predictor. Therefore, the main multivariate data will be divided into two groups, which are the criterion and predictor categories. The study adopts, Tennessee Eastman Process (TEP) and Multiple Output and Multiple Input (MIMO) Pilot Plant System for demonstration. The general finding is that MLR-PCA normally employs less number of PCs compared to PCA, and thus, this will perhaps reduce complication during diagnosis. By adopting such approach, the monitoring task can be made simpler and perhaps more effective, in the sense that only those selected criterion variables (predicted values) will be taken for monitoring, while preserving the rest of the predictor value trends in the form of linear functions in association with the criterion variables. This study also shows that MLR-PCA works relatively better in terms of fault detection and identification against the conventional system. 2015-10 Thesis NonPeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/12737/1/FKKSA%20-%20NORLIZA%20ISHAK%20-%20CD%209668.pdf application/pdf en http://umpir.ump.edu.my/id/eprint/12737/2/FKKSA%20-%20NORLIZA%20ISHAK%20-%20CD%209668%20-%20CHAP%201.pdf application/pdf en http://umpir.ump.edu.my/id/eprint/12737/3/FKKSA%20-%20NORLIZA%20ISHAK%20-%20CD%209668%20-%20CHAP%203.pdf Norliza, Ishak (2015) Development of multivariate statistical process monitoring using combination MLR-PCA method. Masters thesis, Universiti Malaysia Pahang. http://iportal.ump.edu.my/lib/item?id=chamo:93872&theme=UMP2
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English English English
topic	T Technology (General) TP Chemical technology
spellingShingle	T Technology (General) TP Chemical technology Norliza, Ishak Development of multivariate statistical process monitoring using combination MLR-PCA method
description	The most popular types of process monitoring systems is Multivariate Statistical Process Monitoring (MSPM) which has the most practical method in handling the complicated large scale processes. This is due to the ability of the system in maximizing the usage of abundant historial process data, in such a way that the original data dimensions are compressed and data variations preserved to certain extent in a set of transformed variables by way of linear combinations. Thus, the composite model is generally flexible regardless of the amount of variables that utilized. In this regard, conventional Principal Component Analysis (PCA) has been widely applied to conduct such compression function particularly for MSPM. However, the conventional PCA is a linear technique which results sometimes inappropriately employed especially in modeling processes that exhibit severe non-linear correlations. Therefore, a new solution is demanded, whereby the number of original variables can be reduced to certain extent (in terms of scales), while it still can maintain the variation as maximally as possible corresponding to the original, which are then transferable into monitoring statistics. One of the potential techniques available in addressing the issue is known as Multiple Linear Regression (MLR). The main objective of the technique is to predict a set of output values (criterion) based from a specified of linear function, which consists of a set of predictor. Therefore, the main multivariate data will be divided into two groups, which are the criterion and predictor categories. The study adopts, Tennessee Eastman Process (TEP) and Multiple Output and Multiple Input (MIMO) Pilot Plant System for demonstration. The general finding is that MLR-PCA normally employs less number of PCs compared to PCA, and thus, this will perhaps reduce complication during diagnosis. By adopting such approach, the monitoring task can be made simpler and perhaps more effective, in the sense that only those selected criterion variables (predicted values) will be taken for monitoring, while preserving the rest of the predictor value trends in the form of linear functions in association with the criterion variables. This study also shows that MLR-PCA works relatively better in terms of fault detection and identification against the conventional system.
format	Thesis
author	Norliza, Ishak
author_facet	Norliza, Ishak
author_sort	Norliza, Ishak
title	Development of multivariate statistical process monitoring using combination MLR-PCA method
title_short	Development of multivariate statistical process monitoring using combination MLR-PCA method
title_full	Development of multivariate statistical process monitoring using combination MLR-PCA method
title_fullStr	Development of multivariate statistical process monitoring using combination MLR-PCA method
title_full_unstemmed	Development of multivariate statistical process monitoring using combination MLR-PCA method
title_sort	development of multivariate statistical process monitoring using combination mlr-pca method
publishDate	2015
url	http://umpir.ump.edu.my/id/eprint/12737/ http://umpir.ump.edu.my/id/eprint/12737/ http://umpir.ump.edu.my/id/eprint/12737/1/FKKSA%20-%20NORLIZA%20ISHAK%20-%20CD%209668.pdf http://umpir.ump.edu.my/id/eprint/12737/2/FKKSA%20-%20NORLIZA%20ISHAK%20-%20CD%209668%20-%20CHAP%201.pdf http://umpir.ump.edu.my/id/eprint/12737/3/FKKSA%20-%20NORLIZA%20ISHAK%20-%20CD%209668%20-%20CHAP%203.pdf
first_indexed	2023-09-18T22:14:37Z
last_indexed	2023-09-18T22:14:37Z
_version_	1777415254614999040

Development of multivariate statistical process monitoring using combination MLR-PCA method

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