Normal view MARC view ISBD view

Development of PCA-based fault detection system based on various modes of NOC models for continuous-based process / Nurul Fadhilah Roslan

By:

Nurul Fadhilah Roslan

Material type: Text

TextPublication details: Kuantan, Pahang : UMP, 2013Description: xiv, 61 p. : ill. ; 30 cm. + 1 CD-ROMISBN:

THE0002027(Local)

Subject(s):

Dissertation note: Project paper (Bachelor of Chemical Engineering) -- Universiti Malaysia Pahang – 2013 Abstract: Multivariate statistical techniques are used to develop detection methodology for abnormal process behavior and diagnosis of disturbance which causing poor process performance (Raich and Cinar, 2004). Hence, this study is about the development of principal component analysis (PCA) -based fault detection system based on various modes of normal operating condition (NOC) models for continuous-based process. Detecting out-of-control status and diagnosing disturbances leading to the abnormal process operation early are crucial in minimizing product quality variations (Raich and Cinar,2004). The scope of the proposed study is to run traditionally multivariate statistical process monitoring (MSPM) by defining mode difference in variance for continuous-based process. The methodology used to identify and detection of fault which undergo two phase which phase I is off-line monitoring while phase II is on-line monitoring. As a result, it will be analyze and compared of the implementing traditional PCA of Single NOC modes and Multiple NOC modes. Particularly, this study is critically concerned more on the performance during the fault detection operations comprising both off-line and on-line applications, hence it will analyze until fault detection and comparing between two modes of NOC data.

Tags from this library: No tags from this library for this title. Log in to add tags.

Average rating: 0.0 (0 votes)

Holdings ( 2 )
Title notes ( 3 )

Holdings
Item type	Current library	Call number	Copy number	Status	Date due	Barcode
Final Year Report	UMPLIB GAMBANG	QA278.5 .F33 2013 rs Bc. (Browse shelf(Opens below))	1	Not for loan		0000074805
Final Year Report	UMPLIB GAMBANG	CD 7102 \| QA278.5 .F33 2013 rs Bc. (Browse shelf(Opens below))	1	Not for loan		0000074806

Project paper (Bachelor of Chemical Engineering) -- Universiti Malaysia Pahang – 2013

Bibliography : p. 48-53

Multivariate statistical techniques are used to develop detection methodology for abnormal process behavior and diagnosis of disturbance which causing poor process performance (Raich and Cinar, 2004). Hence, this study is about the development of principal component analysis (PCA) -based fault detection system based on various modes of normal operating condition (NOC) models for continuous-based process. Detecting out-of-control status and diagnosing disturbances leading to the abnormal process operation early are crucial in minimizing product quality variations (Raich and Cinar,2004). The scope of the proposed study is to run traditionally multivariate statistical process monitoring (MSPM) by defining mode difference in variance for continuous-based process. The methodology used to identify and detection of fault which undergo two phase which phase I is off-line monitoring while phase II is on-line monitoring. As a result, it will be analyze and compared of the implementing traditional PCA of Single NOC modes and Multiple NOC modes. Particularly, this study is critically concerned more on the performance during the fault detection operations comprising both off-line and on-line applications, hence it will analyze until fault detection and comparing between two modes of NOC data.