A new S2‐TEWMA control chart for monitoring process dispersion

2020 ◽  
Author(s):  
Kashinath Chatterjee ◽  
Christos Koukouvinos ◽  
Angeliki Lappa
Keyword(s):  
Control Chart ◽  
Monitoring Process ◽  
Process Dispersion

Comparing Robustness of EWMA Dispersion Control Chart for Non-Normal Process

2014 ◽  
Vol 912-914 ◽  
pp. 1189-1192
Author(s):  
Hai Yu Wang
Keyword(s):  
Control Chart ◽  
Control Charts ◽  
Normal Process ◽  
Comparison Analysis ◽  
Run Length ◽  
Monitoring Process ◽  
Process Dispersion

This article discusses robustness to non-normality of EWMA charts for dispersion. Comparison analysis of run length of four kinds of EWMA charts to monitoring process dispersion is provided to evaluate control charts performance and robustness. At last robust EWMA dispersion charts for non-normal processes are proposed by this way.

scholarly journals A Robust Control Chart for Monitoring Dispersion

2013 ◽  
Vol 2013 ◽  
pp. 1-5
Author(s):  
Maoyuan Zhou ◽  
Wei Geng
Keyword(s):  
Robust Control ◽  
Control Chart ◽  
Control Charts ◽  
Nonparametric Test ◽  
Dispersion Parameters ◽  
Monitoring Process ◽  
Location Parameters ◽  
Process Dispersion ◽  
Change Point Model ◽  
Process Location

Most robust control charts in the literature are for monitoring process location parameters, such as mean or median, rather than process dispersion parameters. This paper develops a new robust control chart by integrating a two-sample nonparametric test into the effective change-point model. Our proposed chart is easy in computation, convenient to use, and very powerful in detecting process dispersion shifts.

scholarly journals An Application of Univariate and Multivariate Control Charts in Monitoring Water Quality

2020 ◽  
pp. 1-7
Author(s):  
Siti Rahayu Mohd Hashim ◽  
Azwaan Andrew ◽  
Wilter Azwal Malandi
Keyword(s):  
Water Quality ◽  
Control Chart ◽  
Control Charts ◽  
Ph Value ◽  
Statistical Process ◽  
Multivariate Control Charts ◽  
Monitoring Process ◽  
Process Dispersion ◽  
Water Treatments ◽  
Multivariate Control

Control chart is a tool for detecting an out-of-control signal in statistical process control (SPC). It is widely used in process monitoring in order to detect changes in process mean or process dispersion. This study aims to illustrate the application of multivariate control charts in monitoring water quality at one of the water treatments plants in Kota Kinabalu, Sabah. The tested water quality variables in this study are turbidity, pH value, dissolved oxygen (DO) and concentration of ferum. Two multivariate control charts, Hotelling’sT2 and MCUSUM control charts are constructed under the violation of the multivariate normality assumption. The purpose is to study the effect of non-normal data upon the monitoring process using the selected multivariate control charts. By comparing the monitoring process between the two types of control charts, the consistency of the results is studied. All the univariate and multivariate control charts produced out-of-control signals from different points, hence inconclusive results obtained. Keywords: Water quality; multivariate control chart; univariate control chart; Hotelling’s T2; MCUSUM

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