We propose a new multivariate CUSUM control chart, which is based on selfadaption of its reference value according to the information of current process readings, to quickly detect the multivariate process mean shifts. By specifying the minimum magnitude of the process mean shift in terms of its non-centrality parameter, our proposed control chart can achieve an overall performance for detectin...

Nonparametric control chart is useful when the underlying distribution is unknown, or is not likely to be normal. In this paper, we provide a sequential rank-based nonparametric adaptive EWMA (NAE) control chart for detecting the persistent shift in the location parameter. This NAE chart is a self-starting scheme and thus can be used to monitor processes at the start-up stages rather than waiti...

quality control plays an important role in increasing the product quality. fuzzy control charts are more sensitive than shewhart control chart. hence, the correct use of fuzzy control chart leads to producing better-quality products. this area is complex because it involves a large scope of industries, and information is not well organized. in this research, we provide a literature review of th...

Traditional multivariate control charts assume that measurement from manufacturing processes follows a multivariate normal distribution. However, this assumption may not hold or may be difficult to verify because not all the measurement from manufacturing processes are normal distributed in practice. This study develops a new multivariate control chart for monitoring the processes with non-norm...

in this paper, first the available single charting methods, which have been proposed to detect simultaneous shifts in a single process mean and variance, are reviewed. then, by designing proper simulation studies these methods are evaluated in terms of in-control and out-ofcontrol average run length criteria (arl). the results of these simulation experiments show that the ewma and ewms methods ...

in this research, an iterative approach is employed to recognize and classify control chart patterns. to do this, by taking new observations on the quality characteristic under consideration, the maximum likelihood estimator of pattern parameters is first obtained and then the probability of each pattern is determined. then using bayes’ rule, probabilities are updated recursively. finally, when...

Adaptive sample size and sampling intervals schemes have been widely used to improve the statistical efficiency of Hotelling T control chart in detecting small changes when the quality of a product or a process can be characterised by the multivariate distribution of quality characteristics. In this paper, we design a Hotelling T scheme varying sample sizes and sampling intervals (VSSI-T) for a...

To detect sustainable changes in the production processes, memory-type control charts are frequently utilized. This study is conducted to assess performance of Bayesian adaptive exponentially weighted moving average (AEWMA) chart using ranked set sampling schemes following two different loss functions presence a measurement error for posterior and predictive distributions conjugate priors. base...

This paper considers adaptive schemes for the simultaneous monitoring of mean and variability a multivariate normal quality characteristic. At first, we extend an already existing bivariate non-adaptive control chart to one. Then, develop several schemes, which will cover both previously newly charts. After having designed chart, eight performance measures are computed based on run length, time...