田口损失函数下ewma -半圆图的经济统计设计

Shin-Li Lu
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引用次数: 2

摘要

单个指数加权移动平均(EWMA)图被有效地用于同时监测过程均值和/或方差。从经济统计的角度出发,将田口的二次损失函数与Lorenzen和Vance的成本模型相结合,设计了ewma -半圆图(EWMA-SC)。此外,将经济统计绩效和对过程能力指标的影响与平方和EWMA (SS-EWMA)和最大EWMA (MaxEWMA)图进行了比较。最优决策变量——即样本量n、采样间隔时间h、控制极限宽度L和平滑常数λ——通过最小化期望成本函数获得。通过仿真,发现当过程均值和方差同时移动时,EWMA-SC图产生的期望成本最小。然而,当一个过程意味着自己的转变时,MaxEWMA图表产生的缺陷产品成本最低。[收稿日期:2017年5月1日;修订日期:2018年8月22日;录用日期:2019年1月3日]
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Economic-statistical design of EWMA-semicircle charts under the Taguchi loss function
A single exponentially weighted moving average (EWMA) chart is effectively used to monitor the process mean and/or variance simultaneously. An EWMA-semicircle (EWMA-SC) chart designed from the economic-statistical perspective is proposed, which incorporates Taguchi's quadratic loss function into Lorenzen and Vance's cost model. Moreover, economic-statistical performance and the effect on process capability index are compared to those with sum of square EWMA (SS-EWMA) and maximum EWMA (MaxEWMA) charts. The optimal decision variables - namely, sample size n, sampling interval time h, control limit width L and smoothing constant λ - are obtained by minimising the expected cost function. Via simulations, the EWMA-SC chart is found to incur the smallest expected cost when a process mean and variance simultaneously shift. However, the MaxEWMA chart incurs the lowest cost of defective products when a process means shifts on its own. [Received: 1 May 2017; Revised: 22 August 2018; Accepted: 3 January 2019]
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