02563nam a2200265 a 4500 vtls000074985 KUKTEM 20251114204549.0 131004t2012 my da f m 000 0 eng d THE0002024(Local) 201905131608 yusri 201905131608 yusri 201311141147 nabilah 201310231006 nabilah 201310041658 nabilah UMP QA278 .Y877 2012 rs Bc. Mohamad Yusup Abd Wahab Development of PCA-based fault detection system based on various of NOC models for continuous-based process / Mohamad Yusup Abd Wahab Kuantan, Pahang : UMP, 2012 xiii, 53 p. : ill. ; 30 cm. + 1 CD-ROM 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. Multivariate analysis Technique Multivariate analysis Principal component analysis 0 0 0 1 10000 10000 2019-09-04 0 QA278 .Y877 2012 rs Bc. 0000074935 2019-09-04 00:00:00 1 2019-09-04 PSM 0 0 0 1 10000 10000 2019-09-04 0 CD 7167 | QA278 .Y877 2012 rs Bc. 0000074936 2019-09-04 00:00:00 1 2019-09-04 PSM VIRTUA40 4173 4179 VTLSSORT0080*0200*0400*0900*1000*2450*2600*3000*5020*5040*5200*6500*6501*6502*9992