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001			vtls000093872
003			KUKTEM
005			20251117113316.0
008			160316t2015 my da f ab m 000 0 eng d
020			_aTHE0000974(Local)
039		9	_a201905271117 _bnazirah _c201710111626 _daishah _c201603161620 _dasma _y201603161232 _zasma
040			_aUMP
090			_aFKKSA .N675 2015 r Thesis
100	0		_aNorliza Ishak
245	1	0	_aDevelopment of multivariate statistical process monitoring using combination MLR-PCA method / _cNorliza Ishak
260			_aKuantan, Pahang : _bUMP, _c2015
300			_axviii, 132 p. : _bill. (some col.) ; _c30 cm. + _e1 CD ROM
500			_aFaculty of Chemical & Natural Resources Engineering
502			_aThesis (Master of Engineering (Chemical)) -- Universiti Malaysia Pahang – 2015
504			_aBibliography : p. 96-112
520	3		_aThe most popular types of process monitoring systems is Multivariate Statistical Process Monitoring (MSPM) which has the most practical method in handling the complicated large scale processes. This is due to the ability of the system in maximizing the usage of abundant historial process data, in such a way that the original data dimensions are compressed and data variations preserved to certain extent in a set of transformed variables by way of linear combinations. Thus, the composite model is generally flexible regardless of the amount of variables that utilized. In this regard, conventional Principal Component Analysis (PCA) has been widely applied to conduct such compression function particularly for MSPM. However, the conventional PCA is a linear technique which results sometimes inappropriately employed especially in modeling processes that exhibit severe non-linear correlations. Therefore, a new solution is demanded, whereby the number of original variables can be reduced to certain extent (in terms of scales), while it still can maintain the variation as maximally as possible corresponding to the original, which are then transferable into monitoring statistics. One of the potential techniques available in addressing the issue is known as Multiple Linear Regression (MLR). The main objective of the technique is to predict a set of output values (criterion) based from a specified of linear function, which consists of a set of predictor. Therefore, the main multivariate data will be divided into two groups, which are the criterion and predictor categories. The study adopts, Tennessee Eastman Process (TEP) and Multiple Output and Multiple Input (MIMO) Pilot Plant System for demonstration. The general finding is that MLR-PCA normally employs less number of PCs compared to PCA, and thus, this will perhaps reduce complication during diagnosis. By adopting such approach, the monitoring task can be made simpler and perhaps more effective, in the sense that only those selected criterion variables (predicted values) will be taken for monitoring, while preserving the rest of the predictor value trends in the form of linear functions in association with the criterion variables. This study also shows that MLR-PCA works relatively better in terms of fault detection and identification against the conventional system
610	2	0	_aFaculty of Chemical & Natural Resources Engineering _xDissertations
650		0	_aUniversities and Colleges _xDissertations
650		0	_aTheses
856	4	0	_uhttp://ecollib.ump.edu.my/3542/ _zLibrary access only
999			_aVIRTUA40 _c6485 _d6491
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