Mukherjee and Chakraborti 1 proposed a single distribution-free (nonparametric) Shewhart-type chart based on the Lepage 2 statistic for simultaneously monitoring both the location and the scale parameters of a continuous distribution when both of these parameters are unknown. In the present work, we consider a single distribution-free CUSUM chart, based on the Lepage 2 statistic, referred to as...

Monitoring Markov Dependent Observations with a Log-Likelihood Based CUSUM Shabnam Mousavi and Marion R. Reynolds, Jr. Department of Statistics, The Pennsylvania State University, University Park, PA 16802-2111, U.S.A., Max Planck Institute for Human Development, Lentzeallee 94, 14195 Berlin, Germany, Departments of Statistics and Forestry, Virginia Polytechnic Institute and State University, B...

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 ...

At the start of a production run, the parameters of the distribution of a quality characteristic are frequently unknown. If the quality characteristic should by monitored on-line by means of a control chart and the production run length is too short, then it is impossible to get a sufficient Phase I number of samples allowing for the correct estimation of parameters and the evaluation of the ch...

Shewhart, exponentially weighted moving average (EWMA), and cumulative sum (CUSUM) charts are famous statistical tools, to handle special causes and to bring the process back in statistical control. Shewhart charts are useful to detect large shifts, whereas EWMA and CUSUM are more sensitive for small to moderate shifts. In this study, we propose a new control chart, named mixed CUSUM-EWMA chart...

For an improved monitoring of process parameters, it is generally desirable to have ef?cient designs of control charting structures. The addition of Shewhart control limits to the cumulative sum (CUSUM) control chart is a simple monitoring scheme sensitive to wide range of mean shifts. To improve the detection ability of the combined Shewhart–CUSUM control chart to off-target processes, we deve...

103 AIJ Modeling, Identification, Simulation and Control, Vol 48, No. 2, Fall 2016 Please cite this article using: Aminnayeri, M., and Sogandi, F., 2016. “A Risk-Adjusted Self-Starting Bernoulli CUSUM Control Chart with Dynamic Probability Control Limits”. Amirkabir International Journal of Modeling, Identification, Simulation and Control, 48(2), pp. 103–110. DOI: 10.22060/miscj.2016.833 URL: h...

The reliability data is getting used to monitor and improve the quality of products or services. Nowadays, most of products or services are the results of processes with dependent stages referred to as multi-stage process. In these processes, the quality characteristics are affected by the quality characteristics in the previous stages, called as cascade property. In some cases, it is not possi...

Most control charts for variables data are constructed upon the assumption that the observations from a process are independent. In most applications however this assumption does not hold. The performance of process control schemes have to be evaluated in such circumstances. However most of the work published deal with shifts in the location of correlated processes. In this paper, the ARL perfo...

The multi-chart consists of several CUSUM or EWMA charts with different reference values that are used simultaneously to detect anticipated process changes. We not only prove that the chart can quickly achieve the asymptotic optimal bound, but also give an integral equation to determine the reference values to arrive at optimality. Simulation results are used to verify the theoretical optimal p...