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Development of PCA-based fault detection system based on various of NOC models for continuous-based process / Mohamad Yusup Abd Wahab

By:

Mohamad Yusup Abd Wahab

Material type: Text

TextPublication details: Kuantan, Pahang : UMP, 2012Description: xiii, 53 p. : ill. ; 30 cm. + 1 CD-ROMISBN:

THE0002024(Local)

Subject(s):

Dissertation note: Project paper (Bachelor of Chemical Engineering) -- Universiti Malaysia Pahang – 2012 Abstract: Multivariate Statistical Process Control (MSPC) technique has been widely used for fault detection and diagnosis. Currently, contribution plots are used as basic tools for fault diagnosis in MSPC approaches. This plot does not exactly diagnose the fault, it just provides greater insight into possible causes and thereby narrow down the search. Hence, the cause of the faults cannot be found in a straightforward manner. Therefore, this study is conducted to introduce a new approach for detecting and diagnosing fault via correlation technique. The correlation coefficient is determined using multivariate analysis techniques, namely Principal Component Analysis (PCA). In order to overcome 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) scores, the Correlation Coefficient Approach for detecting changes in the correlation structure within the variables. This research proposed PCA Outline Analysis Control Chart for fault detection. 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, particularly the PCA Outline Analysis Control Chart.

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Title notes ( 3 )

Holdings
Item type	Current library	Call number	Copy number	Status	Date due	Barcode
Final Year Report	UMPLIB GAMBANG	QA278 .Y877 2012 rs Bc. (Browse shelf(Opens below))	1	Not for loan		0000074935
Final Year Report	UMPLIB GAMBANG	CD 7167 \| QA278 .Y877 2012 rs Bc. (Browse shelf(Opens below))	1	Not for loan		0000074936

Project paper (Bachelor of Chemical Engineering) -- Universiti Malaysia Pahang – 2012

Bibliography : p. 47-48

Multivariate Statistical Process Control (MSPC) technique has been widely used for fault detection and diagnosis. Currently, contribution plots are used as basic tools for fault diagnosis in MSPC approaches. This plot does not exactly diagnose the fault, it just provides greater insight into possible causes and thereby narrow down the search. Hence, the cause of the faults cannot be found in a straightforward manner. Therefore, this study is conducted to introduce a new approach for detecting and diagnosing fault via correlation technique. The correlation coefficient is determined using multivariate analysis techniques, namely Principal Component Analysis (PCA). In order to overcome 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) scores, the Correlation Coefficient Approach for detecting changes in the correlation structure within the variables. This research proposed PCA Outline Analysis Control Chart for fault detection. 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, particularly the PCA Outline Analysis Control Chart.