الفهرس 
Computer ProgramsOnly 14 pages are availabe for public view
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Abstract
In modern manufacturing processes, amounts of multivariate data are routinely collected through automated in- process sensing. These data exhibit high correlation, rank deficiency, low signal- to- noise and missing values. Considering the fact that although a process may have several tens to hundreds of variables being monitored simultaneously, these variables are often correlated. Conventional univariate and multivariate statistical process control techniques are not suitable to be used in these environments . This research discusses these issues and advocates the use of Multivariate Statistical Process Control Based on Principal Component Analysis (MSPC-PCA) as an efficient statistical tool for process understanding ,monitoring and diagnosing causes for special events in these contexts. (MSPC -PCA) has been widely accepted in chemical process but on the other hand we can not apply PCA in some cases due to the assumption the monitored variables are normally distributed. Recently ,to further improve the monitoring performance a new MSPC method based on Independent Component Analysis (ICA) referred to ICA-SPC was proposed, ICA –SPC however , don’t always out perform (PCA -SPS). (ICA , SPC) should be selected when process variables do not follow normal distribution, on the other hand (ICA-SPC) likely will not improve the performance in comparison with (PCA- SPC) if process variables are normally distributed . In practical case , where some Variable follow normal distribution and others do not, Which monitoring method should be selected ?
In this work we answer this question and process a new framework for MSPC Combined, (CMSPC) is developed by integrating (PCA - SPC) and (ICA -SPC) .
Data from urea fertilizer industry process are used to illustrate the practical benefits of using ( MSPC-PCA ) rather than conventional SPC in manufacturing processes .
This study will be divided into the following chapters :
Chapter 1 : Introduction to Multivariate Statistical Process Control based on Principal Component Analysis
Chapter 2: Multivariate Quality control charts
Chapter 3:Multivariate statistical process control Based on principal component Analysis (MSPC-PCA)
Chapter 4:An application study
Chapter 5:Conclusions and Recommendations.