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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...

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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
http://eprints.utm.my/id/eprint/4916/
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spelling my.utm.49162018-02-28T06:50:00Z http://eprints.utm.my/id/eprint/4916/ Improved multivariate statistical process control for chemical process fault detection and diagnosis 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 NonPeerReviewed application/pdf en http://eprints.utm.my/id/eprint/4916/1/LamHonLoongMFKKKSA2004.pdf Lam, Hon Loong (2004) Improved multivariate statistical process control for chemical process fault detection and diagnosis. Masters thesis, Universiti Teknologi Malaysia, Faculty of Chemical and Natural Resources Engineering.
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
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
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
publishDate 2004
url http://eprints.utm.my/id/eprint/4916/1/LamHonLoongMFKKKSA2004.pdf
http://eprints.utm.my/id/eprint/4916/
_version_ 1643644184495652864
score 13.211869

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