Mohamad Yusup Abd Wahab creator text theses my Kuantan, Pahang UMP 2012 monographic eng

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xiii, 53 p. : ill. ; 30 cm. + 1 CD-ROM 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. specialized Mohamad Yusup Abd Wahab Project paper (Bachelor of Chemical Engineering) -- Universiti Malaysia Pahang – 2012 Bibliography : p. 47-48 Multivariate analysis Technique Multivariate analysis Principal component analysis THE0002024(Local) UMP 131004 20251114204549.0 vtls000074985