Improved multivariate statistical process control for chemical process fault detection and diagnosis

This thesis demonstrates the application of Multivariate Statistical Process Control (MSPC) monitoring method that is capable of detecting and diagnosing process faults. Conventionally, r Control Chart and Contribution Chart, which have been widely used for these purposes, are not accurate and sensi...

Full description

Saved in:

Bibliographic Details
Main Author:	Lam, Hon Loong
Format:	Thesis
Language:	English
Published:	2004
Subjects:	TP Chemical technology
Online Access:	http://eprints.utm.my/id/eprint/4916/1/LamHonLoongMFKKKSA2004.pdf
Tags:	Add Tag No Tags, Be the first to tag this record!

id	my-utm-ep.4916
record_format	uketd_dc
spelling	my-utm-ep.49162018-02-28T06:50:00Z Improved multivariate statistical process control for chemical process fault detection and diagnosis 2004-11 Lam, Hon Loong TP Chemical technology This thesis demonstrates the application of Multivariate Statistical Process Control (MSPC) monitoring method that is capable of detecting and diagnosing process faults. Conventionally, r Control Chart and Contribution Chart, which have been widely used for these purposes, are not accurate and sensitive enough to detect and diagnose abnormal changes in operating conditions. In order to overcome these problems, the objeGtive of this research is to develop new approaches, which can improve the performance of the present conventional MSPC methods. Three new approaches have been developed i.e., the Outline Analysis Approach for examining the distribution of Principal Component Analysis (PCA) scores, the Correlation Coefficient (Cik) Approach for detecting changes in the correlation structure within the variables, and the Signal Cumulating Approach for gathering more information regarding the fault. In order to implement the three new approaches, this research proposed PCA Outline Analysis Control Chart and Correlation Coefficient (Cik) Control Chart for fault detection; and the r Score Contribution Chart, the Cik Score Contribution Chart, r Score Contribution Chart with Signal Cumulating Approach and the Cik Score Contribution Chart with Signal Cumulating Approach for fault diagnosis. The results from the conventional method and new approaches were compared based on their accuracy and sensitivity. Based on the results of the study, the new approaches generally performed better compared to the conventional approaches, particularly the PCA Outline Analysis Control Chart and C;k Score Contribution Chart with Signal Cumulating Approach. 2004-11 Thesis http://eprints.utm.my/id/eprint/4916/ http://eprints.utm.my/id/eprint/4916/1/LamHonLoongMFKKKSA2004.pdf application/pdf en public masters Universiti Teknologi Malaysia, Faculty of Chemical and Natural Resources Engineering Faculty of Chemical and Natural Resources Engineering
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	TP Chemical technology
spellingShingle	TP Chemical technology Lam, Hon Loong Improved multivariate statistical process control for chemical process fault detection and diagnosis
description	This thesis demonstrates the application of Multivariate Statistical Process Control (MSPC) monitoring method that is capable of detecting and diagnosing process faults. Conventionally, r Control Chart and Contribution Chart, which have been widely used for these purposes, are not accurate and sensitive enough to detect and diagnose abnormal changes in operating conditions. In order to overcome these problems, the objeGtive of this research is to develop new approaches, which can improve the performance of the present conventional MSPC methods. Three new approaches have been developed i.e., the Outline Analysis Approach for examining the distribution of Principal Component Analysis (PCA) scores, the Correlation Coefficient (Cik) Approach for detecting changes in the correlation structure within the variables, and the Signal Cumulating Approach for gathering more information regarding the fault. In order to implement the three new approaches, this research proposed PCA Outline Analysis Control Chart and Correlation Coefficient (Cik) Control Chart for fault detection; and the r Score Contribution Chart, the Cik Score Contribution Chart, r Score Contribution Chart with Signal Cumulating Approach and the Cik Score Contribution Chart with Signal Cumulating Approach for fault diagnosis. The results from the conventional method and new approaches were compared based on their accuracy and sensitivity. Based on the results of the study, the new approaches generally performed better compared to the conventional approaches, particularly the PCA Outline Analysis Control Chart and C;k Score Contribution Chart with Signal Cumulating Approach.
format	Thesis
qualification_level	Master's degree
author	Lam, Hon Loong
author_facet	Lam, Hon Loong
author_sort	Lam, Hon Loong
title	Improved multivariate statistical process control for chemical process fault detection and diagnosis
title_short	Improved multivariate statistical process control for chemical process fault detection and diagnosis
title_full	Improved multivariate statistical process control for chemical process fault detection and diagnosis
title_fullStr	Improved multivariate statistical process control for chemical process fault detection and diagnosis
title_full_unstemmed	Improved multivariate statistical process control for chemical process fault detection and diagnosis
title_sort	improved multivariate statistical process control for chemical process fault detection and diagnosis
granting_institution	Universiti Teknologi Malaysia, Faculty of Chemical and Natural Resources Engineering
granting_department	Faculty of Chemical and Natural Resources Engineering
publishDate	2004
url	http://eprints.utm.my/id/eprint/4916/1/LamHonLoongMFKKKSA2004.pdf
_version_	1747814553693978624

Improved multivariate statistical process control for chemical process fault detection and diagnosis

Similar Items