Implementing PCA Based on Fault Detection System Based on Selected Important Variables for Continuous Process

Multivariate Statistical Process Control (MSPC) is known generally as an upgraded technique, from which, it was emerged as a result of reformation in conventional Statistical Process Control (SPC) method where MSPC technique has been widely used for fault detection and diagnosis. Currently, contribu...

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Main Author:	Mohd Huzaifah, Hamzah
Format:	Undergraduates Project Papers
Language:	English
Published:	2013
Subjects:	QA Mathematics
Online Access:	http://umpir.ump.edu.my/id/eprint/7209/ http://umpir.ump.edu.my/id/eprint/7209/ http://umpir.ump.edu.my/id/eprint/7209/1/CD7099.pdf

id	ump-7209
recordtype	eprints
spelling	ump-72092015-03-03T09:34:46Z http://umpir.ump.edu.my/id/eprint/7209/ Implementing PCA Based on Fault Detection System Based on Selected Important Variables for Continuous Process Mohd Huzaifah, Hamzah QA Mathematics Multivariate Statistical Process Control (MSPC) is known generally as an upgraded technique, from which, it was emerged as a result of reformation in conventional Statistical Process Control (SPC) method where MSPC technique has been widely used for fault detection and diagnosis. Currently, contribution plots are used in MSPC method as basic tools for fault diagnosis. This plot does not exactly diagnose the fault but it just provides greater insight into possible causes and thereby narrow down the search. Therefore, this research is conducted to introduce a new approach and method for detecting and diagnosing fault via correlation technique. The correlation coefficient is determined using multivariate analysis techniques that could use less number of newly formed variables to represent the original data variations without losing significant information, namely Principal Component Analysis (PCA). In order to solve these problems, the objective of this research is to develop new approaches, which can improve the performance of the present conventional MSPC methods. The new approaches have been developed, the Outline Analysis Approach for examining the distribution of Principal Component Analysis (PCA) score. The result from the conventional method and ne approach 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. 2013-02 Undergraduates Project Papers NonPeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/7209/1/CD7099.pdf Mohd Huzaifah, Hamzah (2013) Implementing PCA Based on Fault Detection System Based on Selected Important Variables for Continuous Process. Faculty of Chemical and Natural Resources Engineering, Universiti Malaysia Pahang. http://iportal.ump.edu.my/lib/item?id=chamo:75827&theme=UMP2
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	QA Mathematics
spellingShingle	QA Mathematics Mohd Huzaifah, Hamzah Implementing PCA Based on Fault Detection System Based on Selected Important Variables for Continuous Process
description	Multivariate Statistical Process Control (MSPC) is known generally as an upgraded technique, from which, it was emerged as a result of reformation in conventional Statistical Process Control (SPC) method where MSPC technique has been widely used for fault detection and diagnosis. Currently, contribution plots are used in MSPC method as basic tools for fault diagnosis. This plot does not exactly diagnose the fault but it just provides greater insight into possible causes and thereby narrow down the search. Therefore, this research is conducted to introduce a new approach and method for detecting and diagnosing fault via correlation technique. The correlation coefficient is determined using multivariate analysis techniques that could use less number of newly formed variables to represent the original data variations without losing significant information, namely Principal Component Analysis (PCA). In order to solve these problems, the objective of this research is to develop new approaches, which can improve the performance of the present conventional MSPC methods. The new approaches have been developed, the Outline Analysis Approach for examining the distribution of Principal Component Analysis (PCA) score. The result from the conventional method and ne approach 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.
format	Undergraduates Project Papers
author	Mohd Huzaifah, Hamzah
author_facet	Mohd Huzaifah, Hamzah
author_sort	Mohd Huzaifah, Hamzah
title	Implementing PCA Based on Fault Detection System Based on Selected Important Variables for Continuous Process
title_short	Implementing PCA Based on Fault Detection System Based on Selected Important Variables for Continuous Process
title_full	Implementing PCA Based on Fault Detection System Based on Selected Important Variables for Continuous Process
title_fullStr	Implementing PCA Based on Fault Detection System Based on Selected Important Variables for Continuous Process
title_full_unstemmed	Implementing PCA Based on Fault Detection System Based on Selected Important Variables for Continuous Process
title_sort	implementing pca based on fault detection system based on selected important variables for continuous process
publishDate	2013
url	http://umpir.ump.edu.my/id/eprint/7209/ http://umpir.ump.edu.my/id/eprint/7209/ http://umpir.ump.edu.my/id/eprint/7209/1/CD7099.pdf
first_indexed	2023-09-18T22:03:42Z
last_indexed	2023-09-18T22:03:42Z
_version_	1777414567648821248

Implementing PCA Based on Fault Detection System Based on Selected Important Variables for Continuous Process

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