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008 141112t2013 my a f m 000 0 eng d
020 _aTHE0004730(Local)
039 9 _a201905141048
_bsmfh
_c201411261253
_dfawwaz
_c201411141255
_dfawwaz
_c201411121122
_dfawwaz
_y201411121120
_zfawwaz
040 _aUMP
090 _aTP370.9.M38 R53 2013 r Bc.
100 0 _aMuhammad Ridzuan Mamat
245 1 0 _aUtilizing multiple linear regression technique for interential measure of continuous-based process monitoring /
_cMuhammad Ridzuan Mamat
260 _aKuantan, Pahang :
_bUMP,
_c2013
300 _axii, 31 p. :
_bill. ;
_c30 cm. +
_e1 CD-ROM
502 _aProject paper (Bachelor of Chemical Engineering) -- Universiti Malaysia Pahang - 2013
504 _aBibliography : p. 29-31
520 _aThe present conventional MSPC has several weaknesses in process fault detection and diagnosis. Some researchers in this filed had commented that the MSPC is a powerful tool for data complexity reduction and fault detection in the significant fault appearance data. The current fault detection and diagnosis method via MSPC is limited to significant faults and does not point put the insignificant ones accurately. In the real time, all variable will be used in monitoring. However in this case only a few of them are truly important. By developed modeling based on multiple linear regressions the relationship between these variables can be figure out. Multiple linear regressions (MLR) is a method used to model the linear relationship between a dependent variable and one or more independent variables. Some assumption should be made in order to obtain an accurate data analysis. The assumptions are variables should normally distribute, a linear relationship between the independent and dependent variables must exist and also the variable should be measure without an error. MLR is probably the most widely used in dendroclimatology for developing models to reconstruct climate variables. Besides they also proposed for control charting methods for lumber manufacturing and profile monitoring applied in public health surveillance. The methods to perform this modeling involve two phases which are Phase I: offline modeling and monitoring and Phase II: online monitoring. As a conclusion, the MLR method is success introduced as a significant improvement compared to the conventional method. On top of those objectives, the original goals of SPC are also been considered as well as carried together, such a way that the productivity of multivariate process monitoring is improved
650 0 _aChemical engineering
_xMathematical models
999 _aVIRTUA40
_c5573
_d5579
999 _aVTLSSORT0080*0200*0400*0900*1000*2450*2600*3000*5020*5040*5200*6500*9992