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008 131004t2012 my da f m 000 0 eng d
020 _aTHE0002024(Local)
039 9 _a201905131608
_byusri
_c201905131608
_dyusri
_c201311141147
_dnabilah
_c201310231006
_dnabilah
_y201310041658
_znabilah
040 _aUMP
090 _aQA278 .Y877 2012 rs Bc.
100 1 _aMohamad Yusup Abd Wahab
245 1 0 _aDevelopment of PCA-based fault detection system based on various of NOC models for continuous-based process /
_cMohamad Yusup Abd Wahab
260 _aKuantan, Pahang :
_bUMP,
_c2012
300 _axiii, 53 p. :
_bill. ;
_c30 cm. +
_e1 CD-ROM
502 _aProject paper (Bachelor of Chemical Engineering) -- Universiti Malaysia Pahang – 2012
504 _aBibliography : p. 47-48
520 3 _aMultivariate 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.
650 0 _aMultivariate analysis
_xTechnique
650 0 _aMultivariate analysis
650 0 _aPrincipal component analysis
999 _aVIRTUA40
_c4173
_d4179
999 _aVTLSSORT0080*0200*0400*0900*1000*2450*2600*3000*5020*5040*5200*6500*6501*6502*9992