Memory-Type Control Charts for Monitoring the Process Dispersion

2013 ◽  
Vol 30 (5) ◽  
pp. 623-632 ◽  
Author(s):  
Nasir Abbas ◽  
Muhammad Riaz ◽  
Ronald J. M. M. Does
Keyword(s):  
Control Charts ◽  
Process Dispersion ◽  
Memory Type

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.

On the location‐based memory type control charts under modified successive sampling scheme

2021 ◽  
Author(s):  
Mehvish Hyder ◽  
Tahir Mahmood ◽  
Muhammad Moeen Butt ◽  
Syed Muhammad Muslim Raza ◽  
Nasir Abbas
Keyword(s):  
Control Charts ◽  
Sampling Scheme ◽  
Successive Sampling ◽  
Memory Type

A comparative study of memory-type control charts under normal and contaminated normal environments

2015 ◽  
Vol 32 (4) ◽  
pp. 1347-1356 ◽  
Author(s):  
Hafiz Zafar Nazir ◽  
Nasir Abbas ◽  
Muhammad Riaz ◽  
Ronald J.M.M. Does
Keyword(s):  
Comparative Study ◽  
Control Charts ◽  
Memory Type

scholarly journals NEW MEMORY-TYPE CONTROL CHARTS FOR MONITORING PROCESS MEAN AND DISPERSION

2017 ◽  
Vol 0 (0) ◽  
pp. 0-0
Author(s):  
Jimoh Olawale Ajadi ◽  
Muhammad Riaz
Keyword(s):  
Control Charts ◽  
Process Mean ◽  
Monitoring Process ◽  
Memory Type

scholarly journals Nonparametric EWMA-Type Control Charts for Monitoring Industrial Processes: An Overview

2021 ◽  
Vol 6 (3) ◽  
pp. 708-751
Author(s):  
Ioannis S. Triantafyllou ◽  
Mangey Ram
Keyword(s):  
Probability Distribution ◽  
Statistical Process Control ◽  
Crucial Role ◽  
Control Charts ◽  
Moving Average ◽  
Industrial Processes ◽  
Statistical Process ◽  
Practical Applications ◽  
Memory Type ◽  
Weighted Moving Average

In the present paper we provide an up-to-date overview of nonparametric Exponentially Weighted Moving Average (EWMA) control charts. Due to their nonparametric nature, such memory-type schemes are proved to be very useful for monitoring industrial processes, where the output cannot match to a particular probability distribution. Several fundamental contributions on the topic are mentioned, while recent advances are also presented in some detail. In addition, some practical applications of the nonparametric EWMA-type control charts are highlighted, in order to emphasize their crucial role in the contemporary online statistical process control.

Memory type control charts with inverse-Gaussian response: An application to yarn manufacturing industry

2020 ◽  
pp. 014233122095296
Author(s):  
Muhammad Amin ◽  
Tahir Mahmood ◽  
Summera Kinat
Keyword(s):  
Simulation Study ◽  
Control Charts ◽  
Manufacturing Industry ◽  
Moving Average ◽  
Inverse Gaussian ◽  
Monitoring Methods ◽  
Pearson Residuals ◽  
Memory Type ◽  
Exponential Weighted Moving Average ◽  
The Relationship

Control charts are commonly applied for monitoring and controlling the performance of the manufacturing process. Usually, control charts are designed based on the main quality characteristics variable. However, there exist numerous other variables which are highly associated with the main variable. Therefore, generalized linear model (GLM)-based control charts were used, which are capable of maintaining the relationship between variables and of monitoring an abrupt change in the process mean. This study is an effort to develop the Phase II GLM-based memory type control charts using the deviance residuals (DR) and Pearson residuals (PR) of inverse Gaussian (IG) regression model. For evaluation, a simulation study is designed, and the performance of the proposed control charts is compared with the counterpart memory less control charts and data-based control charts (excluding the effect of covariate) in terms of the run length properties. Based on the simulation study, it is concluded that the exponential weighted moving average (EWMA) type control charts have better detection ability as compared with Shewhart and cumulative sum (CUSUM) type control charts under the small or/and moderate shift sizes. Moreover, it is shown that utilizing covariate may lead to useful conclusions. Finally, the proposed monitoring methods is implemented on the dataset related to the yarn manufacturing industry to highlight the importance of the proposed control chart.

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.

Effective Control Charts for Monitoring Multivariate Process Dispersion

2011 ◽  
Vol 28 (4) ◽  
pp. 409-426 ◽  
Author(s):  
Chia-Ling Yen ◽  
Jyh-Jen Horng Shiau ◽  
Arthur B. Yeh
Keyword(s):  
Control Charts ◽  
Effective Control ◽  
Multivariate Process ◽  
Process Dispersion
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