Research on Control Scheme of Coordinate Data Based on Multivariate Statistical Process Control

2012 ◽  
Vol 11 (1) ◽  
pp. 425-428
Author(s):  
Shen Zhen ◽  
He Yihai ◽  
Mi Kai ◽  
Wu Chunhui
Keyword(s):  
Process Control ◽  
Statistical Process Control ◽  
Multivariate Statistical Process Control ◽  
Multivariate Statistical ◽  
Statistical Process ◽  
Control Scheme ◽  
Coordinate Data

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

scholarly journals Diagnosis of Out-of-control Signals in Multivariate Statistical Process Control Based on Bagging and Decision Tree

2017 ◽  
Vol 2 (2) ◽  
pp. 1 ◽  
Author(s):  
Jing Jiang ◽  
Hua-Ming Song
Keyword(s):  
Process Control ◽  
Decision Tree ◽  
Statistical Process Control ◽  
Ensemble Learning ◽  
Experimental Results ◽  
Multivariate Statistical Process Control ◽  
Multivariate Statistical ◽  
Statistical Process ◽  
Multiple Classifiers ◽  
Control Signals

In this paper, we propose an ensemble method based on bagging and decision tree to resolve the problem of diagnosing out-of-control signals in multivariate statistical process control. To classify the out-of-control signals, we obtain a series of classifiers through ensemble learning on decision tree. Then we will integrate the classification results of multiple classifiers to determine the final classification. The experimental results show that our method could improve the accuracy of classification and is superior to other methods in terms of diagnosing out-of-control signals in multivariate statistical process control.

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