A variable parameters multivariate control chart for simultaneous monitoring of the process mean and variability with measurement errors

2020 ◽  
Vol 36 (4) ◽  
pp. 1161-1196 ◽  
Author(s):  
Hamed Sabahno ◽  
Philippe Castagliola ◽  
Amirhossein Amiri
Keyword(s):  
Control Chart ◽  
Measurement Errors ◽  
Process Mean ◽  
Variable Parameters ◽  
Multivariate Control Chart ◽  
Multivariate Control

Design multivariate statistical process control procedure in the case of Ethio cement

2022 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Daniel Ashagrie Tegegne ◽  
Daniel Kitaw Azene ◽  
Eshetie Berhan Atanaw
Keyword(s):  
Neural Network ◽  
Production Process ◽  
Control Chart ◽  
Cement Industry ◽  
Time Interval ◽  
Control Procedure ◽  
Sufficient Information ◽  
Content Type ◽  
Multivariate Control Chart ◽  
Multivariate Control

PurposeThis study aims to design a multivariate control chart that improves the applicability of the traditional Hotelling T2 chart. This new type of multivariate control chart displays sufficient information about the states and relationships of the variables in the production process. It is used to make better quality control decisions during the production process.Design/methodology/approachMultivariate data are collected at an equal time interval and are represented by nodes of the graph. The edges connecting the nodes represent the sequence of operation. Each node is plotted on the control chart based on their Hotelling T2 statistical distance. The changing behavior of each pair of input and output nodes is studied by the neural network. A case study from the cement industry is conducted to validate the control chart.FindingsThe finding of this paper is that the points and lines in the classic Hotelling T2 chart are effectively substituted by nodes and edges of the graph respectively. Nodes and edges have dimension and color and represent several attributes. As a result, this control chart displays much more information than the traditional Hotelling T2 control chart. The pattern of the plot represents whether the process is normal or not. The effect of the sequence of operation is visible in the control chart. The frequency of the happening of nodes is recognized by the size of nodes. The decision to change the product feature is assisted by finding the shortest path between nodes. Moreover, consecutive nodes have different behaviors, and that behavior change is recognized by neural network.Originality/valueModifying the classical Hotelling T2 control chart by integrating with the concept of graph theory and neural network is new of its kind.

Quality control of spectroscopic data in non-targeted analysis – Development of a multivariate control chart

Food Control ◽  
2021 ◽  
pp. 108601
Author(s):  
Carolin Lörchner ◽  
Martin Horn ◽  
Felix Berger ◽  
Carsten Fauhl-Hassek ◽  
Marcus A. Glomb ◽  
...  
Keyword(s):  
Quality Control ◽  
Control Chart ◽  
Spectroscopic Data ◽  
Targeted Analysis ◽  
Multivariate Control Chart ◽  
Multivariate Control

scholarly journals A Synthetic median control chart for monitoring the process mean with measurement errors

2019 ◽  
Vol 35 (4) ◽  
pp. 1100-1116 ◽  
Author(s):  
Phuong Hanh Tran ◽  
Kim Phuc Tran ◽  
Athanasios Rakitzis
Keyword(s):  
Control Chart ◽  
Measurement Errors ◽  
Process Mean

scholarly journals IDENTIFYING THE OUT OF CONTROL VARIABLE IN A MULTIVARIATE CONTROL CHART

2002 ◽  
Vol 31 (12) ◽  
pp. 2391-2408 ◽  
Author(s):  
P. E. Maravelakis ◽  
S. Bersimis ◽  
J. Panaretos ◽  
S. Psarakis
Keyword(s):  
Control Chart ◽  
Control Variable ◽  
Multivariate Control Chart ◽  
Multivariate Control
Sign in / Sign up

Export Citation Format

Share Document