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EN
Recently, simultaneous monitoring of process mean and variability has gained increasing attention. By departing from the accurate measurements assumption, this paper investigates the effect of gauge measurement errors on the performance of the maximum generally weighted moving average (Max-GWMA) chart for simultaneous monitoring of process mean and variability under an additive covariate model. Multiple measurements procedure is employed to compensate for the undesired impact of gauge inaccuracy on detection capability of the MaxGWMA chart. Simulation experiments in terms of average run length (ARL) are conducted to assess the power of the developed chart to detect different out-of-control scenarios. The results confirm that the gauge inaccuracy affects the sensitivity of the Max-GWMA chart. Moreover, the results show that taking multiple measurements per item adequately decreases the adverse effect of measurement errors. Finally, a real-life example is presented to demonstrate how measurement errors increases the false alarm rate of the Max-GWMA chart.
EN
Two popular sampling schemes have been used to design control charts by means of a modified exponentially weighted moving average (EWMA) statistic. The structures of the proposed charts, using repetitive group sampling and multiple dependent state sampling, have been presented. The values of average run length have been determined by some specified control chart parameters. The performance of the proposed chart was illustrated via a simulation study. The efficiency of the proposed chart has been compared with the existing one. A practical example based on real data was also given to explain the application of the proposed chart in industry.
EN
A new EWMA control chart has been proposed under repetitive sampling when a quantitative characteristic follows the exponential distribution. The properties of the proposed chart, including the average run lengths has been is compared with two existing control charts with the help of simulated data. An application of the proposed chart hs been illustrated using a healthcare data set.
EN
The design of a new mixed attribute control chart adapted to a truncated life test has been pre-sented. It was assumed that the lifetime of a product follows the Weibull distribution and the number of failures was observed using a truncated life test, where the test duration was specified as a fraction of the mean lifespan. The proposed control chart consists of two pairs of control limits based on a binomial distribution and one lower bound. The average run length of the chart was determined for various levels of shift constants and specified parameters. The efficiency of the chart is compared with an existing control chart in terms of the average run length. The application of the proposed chart is discussed with the aid of a simulation study.
EN
The design of a control chart has been presented using a belief estimator by assuming that the quantitative characteristic of interest follows the gamma distribution. The authors present the structure of the proposed chart and derive the average run lengths for in-control and a shifted process. The aver-age run lengths for various specified parameters have been reported. The efficiency of the proposed chart has been compared to existing control charts. The application of the proposed chart is illustrated with the help of simulated data.
EN
In this paper, we consider a nonparametric Shewhart chart for fuzzy data. We utilize the fuzzy data without transforming them into a real-valued scalar (a representative value). Usually fuzzy data (described by fuzzy random variables) do not have a distributional model available, and also the size of the fuzzy sample data is small. Based on the bootstrap methodology, we design a nonparametric Shewhart control chart in the space of fuzzy random variables equipped with some L2 metric, in which a novel approach for generating the control limits is proposed. The control limits are determined by the necessity index of strict dominance combined with the bootstrap quantile of the test statistic. An in-control bootstrap ARL of the proposed chart is also considered.
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