Results 171 to 180 of about 3,947 (212)

An insight into control charts using EWMA

Communications in Statistics - Theory and Methods, 2021
In this discussion, an insight into MA-EWMA based control chart is given. The two approaches for MA-EWMA control chart are discussed and compared with each.
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Implementing Statistically Constrained Economic EWMA Control Charts

Journal of Quality Technology, 1995
A computer program is presented for the statistically constrained economic design of exponentially weighted moving average control charts for controlling process means. The program finds the optimal sample size, control limit width, sampling interval, a..
James C.-C. Torng, Jeffery K. Cochran, Douglas C. Montgomery, Frederick P. Lawrence   +3 more
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Fuzzy EWMA and Fuzzy CUSUM Control Charts

2016
Exponentially Weighted Moving- Averages (EWMA) and Cumulative-Sum (CUSUM) control charts have the ability of detecting small shifts in the process mean. Classical EWMA and CUSUM charts are not capable to capture the uncertainty in case of incomplete data.
Erginel, Nihal, Şentürk, Sevil
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A new nonparametric EWMA Sign Control Chart

Expert Systems with Applications, 2011
Research highlights? A new EWMA Sign Chart is proposed for detecting deviation from the process target. ? It is suitable for data came from a process with a non-normal distribution. ? The properties of the EWMA statistics are examined. Example shows that the EWMA chart had better performance compared to Shewhart chart. Many data in practice came from a
Yang, Su-Fen, Lin, Jheng-Sian, Cheng, Smiley W.   +2 more
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EWMA control charts for monitoring poisson observations

2023
The necessity for advanced statistical approaches that efficiently monitor and detect abnormalities in diverse industrial and service processes has been pushed by the growing emphasis on quality control and process improvement. Exponentially Weighted Moving Average (EWMA) control charts, created specifically for Poisson observations, are the main topic
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Robustness properties of multivariate EWMA control charts

Quality and Reliability Engineering International, 2002
AbstractIn this paper, the robustness of the multivariate exponentially weighted moving average (MEWMA) control chart to non‐normal data is examined. Two non‐normal distributions of interest are the multivariate $t$ distribution and the multivariate gamma distribution.
Murat C. Testik, George C. Runger, Connie M. Borror   +2 more
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A Multivariate Poisson Adaptive EWMA Control Chart

Quality and Reliability Engineering International
ABSTRACTThe Multivariate Exponentially Weighted Moving Average (MEWMA) control chart is a highly recommended chart in the literature to monitor processes with the aim of detecting small and moderate process shifts. The MEWMA chart is designed to detect a predetermined shift size but not a range of different shift sizes simultaneously.
Aya A. Aly, Nesma A. Saleh, Mahmoud A. Mahmoud   +2 more
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Linear profile monitoring using an adaptive EWMA control chart

2010 IEEE International Conference on Industrial Engineering and Engineering Management, 2010
A novel adaptive Exponentially Weighted Moving Average scheme, called AEWMA3, is suggested for monitoring the stability of a linear relationship between a quality characteristic and an explanatory variable. Comparisons with an analogous non-adaptive scheme points to a better overall performance of the suggested scheme in a variety of out-of-control ...
CAPIZZI, GIOVANNA, MASAROTTO, GUIDO
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EWMA Control Charts for Monitoring Optimal Portfolio Weights

Sequential Analysis, 2007
Abstract The distribution of the asset returns is subject to structural breaks. These breaks may lead to changes in the optimal portfolio weights. A fast detection of any change in the optimal portfolio weights is of great relevance for a portfolio investor.
Vasyl Golosnoy, Wolfgang Schmid
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Likelihood Ratio-Based Distribution-Free EWMA Control Charts

Journal of Quality Technology, 2010
Nonparametric or distribution-free charts are useful in statistical process control when there is a lack of or limited knowledge about the underlying process distribution.
Zou, Changliang, Tsung, Fu-gee
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