Distribution-free (nonparametric) control charts can be useful to the quality practitioner when the underlying distribution is not known. A Phase II nonparametric CUSUM chart based on the exceedance statistics, called the exceedance CUSUM chart, is proposed here for detecting a shift in the unknown location parameter of a continuous distribution. The exceedance statistics can be more efficient ...

In this study, different ranked-based sampling (RBS) schemes are used to design a sensitive control chart monitor the small or moderate shifts in process mean and named combined EWMA moving average RBS (CEM-RBS) charts. The run-length (ARL) standard deviation of (SDRL) through Monte Carlo simulation runs computed evaluate performance proposed charts comparison with existing such as MA, EWMA, EW...

In reality, the process observations are away from the assumption that are normal distributed. The observations could be skew distributions which should use an asymmetric chart rather than symmetric chart. Consequently, this research aim to study the robustness of the asymmetric Tukey’s control chart for skew and non-skew distributions as Lognormal and Laplace distributions. Furthermore, the pe...

STATISTICAL process control (SPC) is concerned with monitoring processes for a change in distribution. In traditional SPC settings, it is assumed that the distribution of the process prior to any change points is fully known, including all its parameters. The process is said to be in-control if it is being generated by the prechange distribution and out-of-control if a change occurs that causes...

This work presents a comparative study of the performance of the cumulative sum (CuSum), as well as the exponentially weighted moving average (EWMA) control charts. The objective of this research is to verify when CuSum and EWMA control charts do the best control region, in order to detect small changes in the process average. Starting from the data of a productive process, several series were ...

Risk-adjusted CUSUM schemes are designed to monitor the number of adverse outcomes following a medical procedure. An approximation of the average run length (ARL), which is the usual performance measure for a risk-adjusted CUSUM, may be found using its Markov property. We compare two methods of computing transition probability matrices where the risk model classifies patient populations into di...

In order to provide a balanced protection against range of shift sizes in high-quality processes, new one-sided adaptive truncated exponentially weighted moving average (ATEWMA) control chart with known and estimated parameters is developed for monitoring time-between-events (TBE) data. A dedicated Markov chain model established evaluating the run length properties operating conditions. Further...

In usual quality control methods, the of a process or product is evaluated by monitoring one more characteristics using their corresponding distributions. However, when characteristic defined through relationship between response and independent variables, regime referred to as profiles monitoring. this article, we improve performance Exponentially Weighted Moving Average Range (EWMAR) charts, ...

Joint monitoring of time and magnitude is an important issue in many industrial non-industrial fields several proposals exist the literature. The aim this article to propose a new maximum exponentially weighted moving average (Max-EWMA) chart assuming generalized exponential distribution for as well magnitude. As test-statistic Max-EWMA consists two EWMA statistics, it easy identify out-of-cont...