Monitoring the coefficient of variation (CV) is a successful approach to Statistical Process Control when the process mean and standard deviation are not constant. In recent years the CV has been investigated by many researchers as the monitored statistic for several control charts. Viewed under this perspective, this paper presents a new efficient method to monitor the CV by means of Run Rules...

One-class classification problems have attracted a great deal of attention from various disciplines. In the present study, we attempt to extend the scope of application of the one-class classification technique to statistical process control (SPC) problems. We propose new multivariate control charts that apply the effectiveness of one-class classification to improvement of Phase I and Phase II ...

This paper investigates control charts for detecting special causes in an ARIMA(0,1,1) process that is being adjusted automatically after each observation using a minimum mean-squared error adjustment policy. It is assumed that the adjustment mechanism is designed to compensate for the inherent variation due to the ARIMA(0,1,1) process, but it is desirable to detect and eliminate special causes...

Conventional Phase II statistical process control (SPC) charts are designed using control limits; a chart gives a signal of process distributional shift when its charting statistic exceeds a properly chosen control limit. To do so, we only know whether a chart is out-of-control (OC) at a given time. It is therefore not informative enough about the likelihood of a potential distributional shift....

We consider the problem of monitoring variability of autocorrelated processes. This paper combines variance estimation techniques from simulation literature with two statistical process control charts from statistical process control (SPC) literature. The proposed SPC methods do not require any assumptions on the distribution of the underlying process and use a variance estimate from each batch...

Traditional statistical process control charts used to monitor key process variables are based on the assumption that measurements are independent and identically distributed about a target value. In practice they are not and often are actually correlated. Reliance on univariate charts can lead to misleading conclusions. This paper addresses the methods for improving the quality of industrial p...

Abstract A single exponentially weighted moving average (EWMA) chart is effectively used for monitoring the process mean and variance simultaneously. In this paper, three single EWMA-type control charts including sum of square EWMA (SS-EWMA), maximum EWMA (MaxEWMA), and EWMA-semicircle (EWMA-SC) charts are compared under Lorenzen and Vance’s cost model integrating Taguchi’s loss function. The o...

the classical control charts by using precise and specified data divide the production process into two groups: in-control or out of control, while the fuzzy sets with the definition continuous and cohesive membership functions and also using ambiguous and vague fuzzy numbers (triangular and trapezoid), it introduces different levels of decisions for the decision makers. in this paper, using th...