Skew-Right 분포 공정에서 Long-Tail 관리도의 개발

The research develops Long-Tailed Control Charts(LTCCs) that are effective for detecting assignable causes and maintaining in-control situations in the case of process that follows a left-skewed and heavy-tailed t- distribution. The charts proposed on this paper can be applied to extreme cases under the observation where it tolerates ultimate fatigue cracks and tensile strength. In addition, the study enumerates and analyzes the weighting scheme of subgroup number, such as Exponentially Weighted Moving Average(EWMA) and Moving Average(MA). Long-tailed Autoregressive and Moving Average(ARMA) control charts for monitoring a serially correlated processes are evaluated at the instance of increasing sampling frequency by automatic inspection. On the whole, the research investigates the coefficients tables for the Long-Tailed Control Charts(LTCCs) to provide calculation method of control limits for practioners.