Journal ArticleDOI
Optimal designs of the double sampling X¯ chart with estimated parameters
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TLDR
In this paper, the authors evaluate the performance of the DS X ¯ chart when process parameters are estimated by means of a new proposed theoretical method, and show that performances with estimated parameters are different from that with known parameters, and propose three optimal design procedures.About:
This article is published in International Journal of Production Economics.The article was published on 2013-07-01. It has received 36 citations till now. The article focuses on the topics: X-bar chart & \bar x and R chart.read more
Citations
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Journal ArticleDOI
Recent Advances in Process Monitoring: Nonparametric and Variable-Selection Methods for Phase I and Phase II
TL;DR: In this article, the need for a nonparametric approach to phase I analysis and the use of variable selection-based control charts in multivariate phase II monitoring is reviewed and discussed.
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Design of exponential control charts based on average time to signal using a sequential sampling scheme
TL;DR: The average time to signal (ATS) is used instead of the average run length to evaluate the performance of TBE charts, since the ATS involves both the number and the time of samples inspected until a signal occurs.
Journal ArticleDOI
Design of Gamma Charts Based on Average Time to Signal
TL;DR: For the phase I monitoring, a new ATS-unbiased design with unknown parameters is developed, and a sequential sampling scheme is adopted to start process monitoring as soon as possible.
Journal ArticleDOI
Optimal designs of the variable sample size and sampling interval X¯ chart when process parameters are estimated
TL;DR: In this article, the Markov chain approach for the variable sample size and sampling interval (VSSI) X ¯ chart with estimated parameters is developed to facilitate process monitoring in manufacturing and service industries.
Journal ArticleDOI
Synthetic Double Sampling X̄ Chart with Estimated Process Parameters
TL;DR: In this article, the performance of the SDS X chart with estimated process parameters was investigated in terms of the average run length (ARL), average number of observations to signal (ANOS), and standard deviation of the run length.
References
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Journal ArticleDOI
Minimal Euclidean distance chart based on support vector regression for monitoring mean shifts of auto-correlated processes
Shichang Du,Jun Lv +1 more
TL;DR: In this article, a new minimal Euclidean distance (MED) based monitoring approach is proposed for enhancing the monitoring mean shifts of auto-correlated processes, which can provide a comprehensive and quantitative assessment for the current process state.
Journal ArticleDOI
An improved double sampling s chart
David He,Arsen Grigoryan +1 more
TL;DR: In this article, an improved double sampling s chart is developed without the normality assumption of the sample standard deviations, and the design of the improved DS s chart was formulated as a statistical design optimization problem and solved with a genetic algorithm.
Journal ArticleDOI
A new approach for the economic design of fully adaptive control charts
TL;DR: In this paper, the authors proposed a unified approach for the development of economically designed variable-parameter (Vp) X ¯ -Shewhart, X ¯-CUSUM and X¯ -EWMA control chart models, for monitoring the process mean in infinite-horizon production runs.
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An economic design of double sampling X charts for correlated data using genetic algorithms
TL;DR: An economic design model of DS is developed based on Yang and Hancock's assumption of correlation and Lorenzen and Vance's cost model to determine sample size, sampling interval, and coefficients of control limits and warring limits.
Journal ArticleDOI
Double sampling \(\overline{X} \) control chart for a first-order autoregressive moving average process model
TL;DR: In this article, the authors consider the double sampling (DS) control chart for monitoring processes in which the observations can be represented as a first-order autoregressive moving average (ARMA(1, 1)) model.