Results 161 to 170 of about 6,512 (216)
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Shewhart Control Charts in New Perspective
Sequential Analysis, 2007The effects of estimating parameters and the violation of the assumption of normality when dealing with control charts are discussed. Corrections for estimating errors and extensions of the normal control chart to parametric and nonparametric charts are investigated.
Albers, Willem, Kallenberg, Wilbert C.M.
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Estimation in Shewhart control charts: effects and corrections
Metrika, 2004The influence of the estimation of parameters in Shewhart control charts is investigated. It is shown by simulation and asymptotics that (very) large sample sizes are needed to accurately determine control charts if estimators are plugged in. Correction terms are developed to get accurate control limits for common sample sizes in the in-control ...
Albers, Willem, Kallenberg, Wilbert C.M.
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Combined Shewhart–EWMA control charts with estimated parameters
Journal of Statistical Computation and Simulation, 2010Shewhart and EWMA control charts can be suitably combined to obtain a simple monitoring scheme sensitive to both large and small shifts in the process mean. So far, the performance of the combined Shewhart–EWMA (CSEWMA) has been investigated under the assumption that the process parameters are known.
CAPIZZI, GIOVANNA, MASAROTTO, GUIDO
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2016
Process Control is the active correction of a process based on the results of process monitoring. Once the process monitoring tools have detected an assignable cause, this cause is removed to bring the process back into control. This chapter presents the process control techniques under fuzziness.
Cengiz Kahraman +2 more
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Process Control is the active correction of a process based on the results of process monitoring. Once the process monitoring tools have detected an assignable cause, this cause is removed to bring the process back into control. This chapter presents the process control techniques under fuzziness.
Cengiz Kahraman +2 more
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Normality test by Shewhart control chart
Journal of Information and Optimization Sciences, 1999Abstract Transformed quantile-quantile (TQQ) and transformed probability-probability (TPP) plots are developed with control limits for the detection of deviations from the normal distribution. It will be shown that TQQ and TPP plots are useful methods for normality test when they are adopted with Shewhart control technique.
Jea-Young Lee +2 more
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RESIDUAL ANALYSIS WITH SHEWHART CONTROL CHARTS
Quality Engineering, 1991We show that the conditions for process stability are equivalent to the conditions for the error terms of a valid regression model (one in which the least squares estimators are unbiased and have minimum variance among all unbiased estimators). By viewi..
HOWARD S. GITLOW, ROSA OPPENHEIM
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A Nonparametric Shewhart-Type Synthetic Control Chart
Communications in Statistics - Simulation and Computation, 2010In this article, we provide a nonparametric Shewhart-type synthetic control chart based on the signed-rank statistic to monitor shifts in the known in-control process median. The synthetic control chart is a combination of a signed-rank chart due to Bakir (2004) and a conforming run length chart due to Bourke (1991).
V. Y. Pawar, D. T. Shirke
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Standardization of Shewhart Control Charts
Journal of Quality Technology, 1989(1989). Standardization of Shewhart Control Charts. Journal of Quality Technology: Vol. 21, No. 4, pp. 287-289.
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1979
The concept of a control chart was evolved by Dr. Shewhart in America in 1924 — the initial development in Britain being mainly by Dr. Dudding and W. Jennett. Dr. Shewhart suggested that the control chart should have three main objectives.
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The concept of a control chart was evolved by Dr. Shewhart in America in 1924 — the initial development in Britain being mainly by Dr. Dudding and W. Jennett. Dr. Shewhart suggested that the control chart should have three main objectives.
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Design Considerations and Trade-offs for Shewhart Control Charts
2021When in-control parameters are unknown, they have to be estimated using a reference sample. The control chart performance in Phase II, which is generally measured in terms of the Average Run Length (ARL) or False Alarm Rate (FAR), will vary across practitioners due to the use of different reference samples in Phase I. This variation is especially large
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