Principal component analysis multivariate statistical process control

Ferrer A.. Multivariate Statistical Process Control based on Principal Component Analysis (MSPC-PCA) Some Reflections and a Case Study in an Autobody Assembly Process Quality Engineering. 2007 19 311-325. 

Ferrer A. Multivariate statistical process control based on principal component analysis (MSPC-IYJA) some reflections and a case study in an autobody assembly process. Qual Eng 2007 19 311-25. 

Data generated from process analysis techniques are commonly displayed on control charts and the term statistical process control (SPC) is often used to describe the use of such data visualisation [5], As the amount of data available increases, due both to different measurements and greater frequency of measurements, then combinations of different data provide improved methods of monitoring the processes concerned. The procedures and concepts of multivariate SPC incorporating principal component analysis (PCA) and partial least squares (PLS) analysis then become important [6]. The different SPC approaches are all aimed at providing better process control and improved process understanding. 

Other simple signal analyses can be used to detect common problems, such as sticking control valves (Shoukat Choudhury et al., 2008) and oscillating control loops (Thornhill and Horch, 2007). For AED applications that involve many process variables, multivariable statistical techniques, such as Principal Component Analysis (PCA) and Partial Least Squares (PLS), can be very effective (see Chapter 21). 

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