A Nonparametric Multivariate Statistical Process Control Chart Based on Change Point Model

2020 ◽  
Author(s):  
Yafei Xu ◽  
Ostap Okhrin
Keyword(s):  
Process Control ◽  
Statistical Process Control ◽  
Control Chart ◽  
Change Point ◽  
Multivariate Statistical Process Control ◽  
Point Model ◽  
Multivariate Statistical ◽  
Statistical Process ◽  
Change Point Model

scholarly journals Multivariate statistical process control approach to monitor quality of chloroquine phosphate tablet (bp250mg) in Dana pharmaceutical company

2016 ◽  
Vol 5 (1) ◽  
pp. 1 ◽  
Author(s):  
Umar Abubakar Adamu ◽  
Gulumbe Shehu Usman ◽  
Dikko Hussaini Garba
Keyword(s):  
Process Control ◽  
Statistical Process Control ◽  
Control Chart ◽  
Multivariate Statistical Process Control ◽  
Process Variables ◽  
Multivariate Statistical ◽  
Statistical Process ◽  
Process Variability ◽  
T2 Control Chart

Recently, much attention has been raised on effects of high increase in drugs counterfeiting and sub-standard quality which leads to many casualties in Nigeria. The Multivariate Statistical Process Control Charts approach was employed to examine such defects especially in assessing the official physico-chemical quality of chloroquine phosphate tablet (BP250mg) which claimed to contain the required quality properties. The Multivariate Exponentially Weighted Moving Average (MEWMA) Control Chart gives a powerful and reliable control chart than the widely used Hotelling’s T2˗Control Chart, which detects the smallest shift in the product process means and have minimum process variability. Also, the Matrix of scatter plots indicated the existence of relationship among the process variables and the Principal Component Analysis (PCA) minimized the rate of dimensionality of the process variability, which captured most of the variables outliers and retained the first Principal Components (PC) that explained over 99% variability of the product. To this end, the study results shows that the product quality characteristics (process variables) is under control (stable) and conform to international standard as specified by BP 2002.

Distribution-Free Multivariate Control Chart Based on Change-Point Model

2014 ◽  
Vol 971-973 ◽  
pp. 1435-1439
Author(s):  
Mei Hua Duan ◽  
Xue Min Zi
Keyword(s):  
Control Chart ◽  
Change Point ◽  
Point Model ◽  
Multivariate Statistical ◽  
Statistical Process ◽  
Distribution Free ◽  
Multivariate Control Chart ◽  
The Mean ◽  
Change Point Model ◽  
Multivariate Control

It is common to monitor several quality characteristics of a process simultaneously in modern quality control, and it is called multivariate statistical process control (MSPC) in the literature. A change-point control chart for detecting shifts in the mean of a multivariate statistical process is developed for the case where the nominal value of the mean is unknown but some historical samples are available. This control chart is called distribution-free multivariate control chart based on change-point model. Its distribution robustness is a significant advantage where we usually know nothing about the underlying distribution. And the simulated results show that this approach has a good performance across the range of possible shifts.

Application of multivariate statistical process control to batch operations

2000 ◽  
Vol 24 (2-7) ◽  
pp. 291-296 ◽  
Author(s):  
B. Lennox ◽  
H.G. Hiden ◽  
G.A. Montague ◽  
G. Kornfeld ◽  
P.R. Goulding
Keyword(s):  
Process Control ◽  
Statistical Process Control ◽  
Multivariate Statistical Process Control ◽  
Multivariate Statistical ◽  
Statistical Process

Making use of process tomography data for multivariate statistical process control

AIChE Journal ◽  
2010 ◽  
Vol 57 (9) ◽  
pp. 2360-2368 ◽  
Author(s):  
Bundit Boonkhao ◽  
Rui F. Li ◽  
Xue Z. Wang ◽  
Richard J. Tweedie ◽  
Ken Primrose
Keyword(s):  
Process Control ◽  
Statistical Process Control ◽  
Multivariate Statistical Process Control ◽  
Multivariate Statistical ◽  
Statistical Process ◽  
Process Tomography ◽  
Tomography Data
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