Nonparametric multivariate control chart for numerical and categorical variables

2022 ◽  
pp. 1-19
Author(s):  
Jiayun Jin ◽  
Geert Loosveldt
Keyword(s):  
Control Chart ◽  
Categorical Variables ◽  
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 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

Diagnosing Problems of Distribution-Free Multivariate Control Chart

2014 ◽  
Vol 971-973 ◽  
pp. 1602-1606
Author(s):  
Wen Li Shi ◽  
Xue Min Zi
Keyword(s):  
Control Chart ◽  
Historical Data ◽  
Important Application ◽  
Distribution Free ◽  
On Line ◽  
Multivariate Control Chart ◽  
Diagnostic Framework ◽  
Multivariate Control ◽  
Control Limits ◽  
New Distribution

In order to solve the problem of only have a few historical data that can be used in multivariate process monitoring, a new distribution-free multivariate control chart has been proposed. And in the control chart structure the control limits are determined on-line with the future observations and the historical data. Therefore, the proposed control chart has very important application in practice. However, the research doesn’t study the problem of the fault diagnosis after the control chart alarms. So we use LASSO-based diagnostic framework to identify when a detected shift has occurred and to isolate the shifted components.

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