000 02512nam a2200265 a 4500
001 vtls000075827
003 KUKTEM
005 20251114204539.0
008 131106t2013 my da f m 000 0 eng d
020 _aTHE0002029(Local)
039 9 _a201905131610
_byusri
_c201311121655
_dnabilah
_y201311061052
_znabilah
040 _aUMP
090 _aQA278.5 .H89 2013 rs Bc.
100 0 _aMohd Huzaifah Hamzah
245 1 0 _aImplementing PCA based on fault detection system based on selected important variables for continuous process /
_cMohd Huzaifah Hamzah
260 _aKuantan, Pahang :
_bUMP,
_c2013
300 _axiii, 67 p. :
_bill. ;
_c30 cm. +
_e1 CD-ROM
502 _aProject paper (Bachelor of Chemical Engineering) -- Universiti Malaysia Pahang – 2013
504 _aBibliography : p. 61-64
520 3 _aMultivariate 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.
650 0 _aPrincipal component analysis
650 0 _aMultivariate analysis
650 0 _aProcess control
_xStatistical methods
999 _aVIRTUA40
_c3924
_d3930
999 _aVTLSSORT0080*0200*0400*0900*1000*2450*2600*3000*5020*5040*5200*6500*6501*6502*9992