Control charts are used to identify the presence of assignable cause of variation in the process. Non-parametric control chart is an emerging area of recent development in the theory of SPC. Its main advantage is that it does not require any knowledge about the underlying distribution of the variable. In this paper a non-parametric control chart for controlling variability has been developed. I...

In statistical process control, the primary method used to monitor the number of nonconformities is the c-chart. The conventional c-chart is based on the assumption that the occurrence of nonconformities in samples is well modeled by a Poisson distribution. When the Poisson assumption is not met, the X-chart (individuals chart) is often used as an alternative charting scheme in practice. In thi...

The existing optimal design of the fixed sampling interval S2-EWMA control chart to monitor the sample variance of a process is based on the average run length (ARL) criterion. Since the shape of the run length distribution changes with the magnitude of the shift in the variance, the median run length (MRL) gives a more meaningful explanation about the in-control and out-of-control performances...

A cumulative sum (CUSUM) control chart is one of the most popular methods used to detect a process mean shift. When one specific size of the mean shift is assumed, the CUSUM chart can be optimally designed in terms of average run length (ARL). In practice, however, the size of the mean shift is usually unknown, and the CUSUM chart can perform poorly when the actual size of the mean shift is sig...

Designs of the double sampling (DS) X chart are traditionally based on the average run length (ARL) criterion. However, the shape of the run length distribution changes with the process mean shifts, ranging from highly skewed when the process is in-control to almost symmetric when the mean shift is large. Therefore, we show that the ARL is a complicated performance measure and that the median r...

A control chart is an important tool in statistical process monitoring that useful to monitor and improve production quality. In this article, attribute using repetitive sampling under a truncated life test proposed for the mean of product where lifetime follows Rayleigh distribution. The parameters limit coefficients are determined so in-control average run length (ARL) very close target ARL. ...

The problem of detecting a shift in the percentile of a Birnbaum–Saunders population in a process monitoring situation is considered. For example, such problems may arise when the quality characteristic of interest is tensile strength or breaking stress. The parametric bootstrap method is used to develop a quality control chart for monitoring percentiles when process measurements have a Birnbau...

Traditionally, an ?̄? chart is used to control the process mean and an R chart is used to control the variance. However, these charts are not sensitive to the small shifts in the processes. The adaptive charts might be considered if the aim is to detect process changes quickly. In this paper, we propose a new adaptive single control chart which integrates the exponentially weighted moving averag...

  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 app...