A comparative study of the median run length (MRL) performance of the Max-DEWMA and SS-DEWMA control charts

2014 ◽  
Author(s):  
Sin Yin Teh ◽  
Michael Boon Chong Khoo ◽  
Ker Hsin Ong ◽  
Keng Lin Soh
Keyword(s):  
Comparative Study ◽  
Control Charts ◽  
Run Length

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 Multivariate Statistical Quality Control Based on Ranked Set Sampling

2017 ◽  
Vol 14 (1) ◽  
pp. 1
Author(s):  
Hakan Eygü ◽  
M. Suphi Özçomak
Keyword(s):  
Control Charts ◽  
Sampling Methods ◽  
Average Run Length ◽  
Ranked Set Sampling ◽  
Multivariate Statistical ◽  
Run Length ◽  
Hotelling's T2 ◽  
Median Ranked Set Sampling ◽  
Hotelling’S T2 ◽  
T2 Control Chart

The sample of the study was formed using simple random sampling, ranked set sampling, extreme ranked set sampling and median ranked set sampling. At the end of this process, the researcher created Hotelling’s T2 control charts, a multivariate statistical process control method. The performances of SRS, RSS, ERSS and MRSS sampling methods were compared to one another using these control charts. A simulation was performed to see the average run-length values for Hotelling’s T2 control charts, and these findings were also used for the comparison of the sampling performances.At the end of the study, the researcher formed a sample using median ranked set sampling and created the Hotelling’s T2 control chart. As a result of this operation, the researcher found that there was an out-of-control signal in the process, while there was no such signal in other sampling methods. When the average run-length values obtained from Hotelling’s T2 control charts were compared, it was seen that a shift in the process was detected by the ranked set sampling earlier, when compared to other sampling methods. This paper it can be said that the methods used are unique to the literature because they are applied to multivariate data.

scholarly journals Outliers Detection Models in Shewhart Control Charts; an Application in Photolithography: A Semiconductor Manufacturing Industry

Mathematics ◽  
2020 ◽  
Vol 8 (5) ◽  
pp. 857 ◽  
Author(s):  
Ishaq Adeyanju Raji ◽  
Muhammad Hisyam Lee ◽  
Muhammad Riaz ◽  
Mu’azu Ramat Abujiya ◽  
Nasir Abbas
Keyword(s):  
Phase I ◽  
Outlier Detection ◽  
Control Charts ◽  
Semiconductor Manufacturing ◽  
Manufacturing Industry ◽  
Estimation Errors ◽  
Run Length ◽  
Shewhart Control Charts ◽  
Shewhart Control ◽  
Control Limits

Shewhart control charts with estimated control limits are widely used in practice. However, the estimated control limits are often affected by phase-I estimation errors. These estimation errors arise due to variation in the practitioner’s choice of sample size as well as the presence of outlying errors in phase-I. The unnecessary variation, due to outlying errors, disturbs the control limits implying a less efficient control chart in phase-II. In this study, we propose models based on Tukey and median absolute deviation outlier detectors for detecting the errors in phase-I. These two outlier detection models are as efficient and robust as they are distribution free. Using the Monte-Carlo simulation method, we study the estimation effect via the proposed outlier detection models on the Shewhart chart in the normal as well as non-normal environments. The performance evaluation is done through studying the run length properties namely average run length and standard deviation run length. The findings of the study show that the proposed design structures are more stable in the presence of outlier detectors and require less phase-I observation to stabilize the run-length properties. Finally, we implement the findings of the current study in the semiconductor manufacturing industry, where a real dataset is extracted from a photolithography process.

Monitoring length of stay through control charts: a comparative study of diabetic patients

2019 ◽  
Vol 47 (4) ◽  
pp. 196-202 ◽  
Author(s):  
Fatma Pakdil ◽  
Nasibeh Azadeh-Fard ◽  
Afsun Ezel Esatoglu
Keyword(s):  
Length Of Stay ◽  
Comparative Study ◽  
Control Charts ◽  
Diabetic Patients

A comparative study on Poisson control charts

2019 ◽  
Vol 17 (3) ◽  
pp. 354-382 ◽  
Author(s):  
Vasileios Alevizakos ◽  
Christos Koukouvinos
Keyword(s):  
Comparative Study ◽  
Control Charts

Run-Length Performance of Regression Control Charts with Estimated Parameters

2004 ◽  
Vol 36 (3) ◽  
pp. 280-292 ◽  
Author(s):  
Lianjie Shu ◽  
Fugee Tsung ◽  
Kwok-Leung Tsui
Keyword(s):  
Control Charts ◽  
Run Length ◽  
Estimated Parameters ◽  
Regression Control

The effect of Phase I sample size on the run length performance of control charts for autocorrelated data

2008 ◽  
Vol 35 (1) ◽  
pp. 67-87 ◽  
Author(s):  
Gülser Köksal ◽  
Burcu Kantar ◽  
Taylan Ali Ula ◽  
Murat Caner Testik
Keyword(s):  
Phase I ◽  
Sample Size ◽  
Control Charts ◽  
Run Length ◽  
Autocorrelated Data
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