Multivariate statistical process control-based hypothesis testing for damage detection in structural health monitoring systems

2018 ◽  
Vol 26 (1) ◽  
pp. e2287 ◽  
Author(s):  
Marwa Chaabane ◽  
Majdi Mansouri ◽  
Ahmed Ben Hamida ◽  
Hazem Nounou ◽  
Mohamed Nounou
Keyword(s):  
Structural Health Monitoring ◽  
Process Control ◽  
Damage Detection ◽  
Statistical Process Control ◽  
Health Monitoring ◽  
Monitoring Systems ◽  
Multivariate Statistical Process Control ◽  
Multivariate Statistical ◽  
Statistical Process ◽  
Health Monitoring Systems

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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