Control charts are effective tools for signal detection in both manufacturing processes and service processes. Much of the data in service industries comes from processes having nonnormal or unknown distributions. The commonly used Shewhart variable control charts, which depend heavily on the normality assumption, are not appropriately used here. In this paper, we propose a new asymmetric EWMA ...

In this paper, we evaluate the capability to detect traffic anomalieswith Shewhart, CUSUM, andEWMA control charts. In order to cope with seasonal variation and serial correlation, control charts are not applied to traffic measurement time-series directly, but to the prediction errors of exponential smoothing and Holt-Winters forecasting. The evaluation relies on flow data collected in an ISP ba...

Duncan first proposed the economic design of x control charts in 1956 to control normal process means and ensure that an economic design control chart actually lowers the cost, compared with a Shewhart control chart. Many authors have studied the control charts from economic viewpoint from then on. An economic design does not consider the statistical properties, such as type I or type II error ...

While researchers and practitioners may seamlessly develop methods of detecting outliers in control charts under a univariate setup, screening multivariate pose serious challenges. In this study, we propose robust chart based on the Stahel-Donoho estimator (SDRE), whilst process parameters are estimated from phase-I. Through intensive Monte-Carlo simulation, study presents how estimation presen...

− The traditional control charts are based on the assumption that the measurement data relating to the average of each subgroup of the parameters under observation of a specific quality characteristic of product, process, system or service, are reported regardless of time stability of its own parameters. This condition is generally not verified in real cases, where we should take into account t...

although control charts are very common to monitoring process changes, they usually do not indicate the real time of the changes. identifying the real time of the process changes is known as change-point estimation problem. there are a number of change point models in the literature however most of the existing approaches are dedicated to normal processes. in this paper we propose a novel appro...

Traditional statistical process control for variable data often involves the use of a separate mean and a standard deviation chart. Several proposals have been published recently, where a single (combination) chart that is simpler and may have performance advantages, is used. The assumption of normality is crucial for the validity of these charts. In this article, a single distribution-free She...

The sensitivity of four tests on Shewhart type Q-eharis and of specially designed EWMA and CUSUM Q-eharts to detect one-step permanent shifts of either a normal mean or standard deviation has been studied. The usual test that signals for one point beyond three standard deviations on a Shewhart chart is found to have poor sensitivity, generally. The test that signals when four out of five consec...

The main purpose of this article is the development and the study of runs rules applied to a Shewhart type control chart for the mean in order to detect non-random patterns in Phase I, when we are interested to conclude if the process is in a stable condition for the first twenty five or thirty samples in order to estimate the process’ parameters. A characterization scheme such as which rule is...