Results 161 to 170 of about 4,823 (188)

Generalized EWMA-Charts

1992
The control problem of a production process with output Y(t) = M(t) + e(t) (e(t) being a white noise function with E(e(t)) = σ2 for all time points and M(t) := E(Y(t))) can be considered as the problem of monitoring the unknown expectation function M(t).
Joseph J. Hoegel, Hans W. Wolff
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The efficiency of the EWMA capability chart

2007 IEEE International Conference on Industrial Engineering and Engineering Management, 2007
In order to monitor unstable but capable processes Castagliola & Vannman have recently suggested a procedure based on an EWMA approach, called EWMA capability chart, for monitoring Vannman's Cp(u,v)-family of capability indices and showed how their proposed approach efficiently monitors capable processes by detecting a decrease or increase of the ...
Castagliola, Philippe, Vännman, Kerstin
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A Multivariate Sign EWMA Control Chart

Technometrics, 2011
Nonparametric control charts are useful in statistical process control (SPC) when there is a lack of or limited knowledge about the underlying process distribution, especially when the process measurement is multivariate. This article develops a new multivariate SPC methodology for monitoring location parameters.
Zou, Changliang, Tsung, Fu-gee
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Ewma charts for multivariate time series

Sequential Analysis, 1997
In this paper a muIt,ivariat.e EWMA chart for time series is introduced. In principle, it is a generalization of the control scheme of Lowry et al. (I992) for multivarite indendent observations. The autocovariances of the EWMA recursion are derived for stationary multivariate time series.
H. G. Kramer, L.V. Schmid
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Studentised residual-based EWMA control charts

2011 2nd International Conference on Artificial Intelligence, Management Science and Electronic Commerce (AIMSEC), 2011
Taking multistage correlation process as objective, this article studies the construction and performance of studentised residual-based EWMA control charts. Residual is used to remove the effect of upstream stages on downstream stages in monitoring multistage cascade processes. In order to overcome the influence of dimension and detect small mean shift
null Zhong Jianlan, null Ma Yizhong
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Designing a Multivariate EWMA Control Chart

Journal of Quality Technology, 1997
A multivariate exponentially weighted moving average control chart can be used to improve the detection of small shifts in multivariate statistical process control. Recommendations are provided for the selection of parameters for such a chart. The recom..
Sharad S. Prabhu, George C. Runger
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Multivariate EWMA Charts with Variable Sampling Intervals

Economic Quality Control, 2009
Summary: The standard multivariate control chart usually employs fixed sample sizes at fixed sampling intervals (FSI) to monitor a process. In this study, a multivariate exponentially weighted moving average (MEWMA) chart with variable sampling intervals (VSI) is investigated.
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Combined Shewhart–EWMA control charts with estimated parameters

Journal of Statistical Computation and Simulation, 2010
Shewhart 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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Monitoring the Coefficient of Variation Using EWMA Charts

Journal of Quality Technology, 2011
The coefficient of variation (CV) is a quality characteristic that has several applications in applied statistics and is receiving increasing attention in quality control. This paper suggests a new method to monitor the CV.
CASTAGLIOLA P, CELANO, GIOVANNI, PSARAKIS S.   +2 more
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EWMA control charts for autoregressive processes

Journal of the Operational Research Society, 2001
Summary: Many processes must be monitored by using observations that are correlated. An approach called algorithmic statistical process control can be employed in such situations. This involves fitting an autoregressive-moving average time series model to the data. Forecasts obtained from the model are used for active control, while the forecast errors
Koehler, A. B., Marks, N. B., O'Connell, R. T.   +2 more
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