A progressive average chart usually triggers initial out-of-control (OC) signals more simply and quickly than other memory-type charts . In this paper, two progressive average control procedures are proposed for monitoring the coefficient of variation (CV) of a normally distributed process variable, namely, the progressive CV (PCV) and progressive resetting CV (PRCV) control charts , respectively. The implementation of the proposed charts is presented, and the necessary design parameters are provided. Through extensive numerical simulations, it is shown that the proposed PCV and PRCV charts outperform several existing control charts to detect the initial OC signals, especially for the small and moderate CV shifts, under each combination of the shift size, the sample size, and the in-control target value of the CV. In addition, the application of the proposed control charts is illustrated by a detection example for a spinning process.

中文翻译：

---

检测变异系数的渐进方法

渐进式平均图表通常比其他记忆型图表更简单、更快速地触发初始失控 (OC) 信号。在本文中，提出了两种渐进平均控制程序来监测正态分布过程变量的变异系数 (CV)，分别是渐进 CV (PCV) 和渐进重置 CV (PRCV) 控制图。介绍了建议图表的实施，并提供了必要的设计参数。通过广泛的数值模拟，表明所提出的 PCV 和 PRCV 图优于几个现有的控制图来检测初始 OC 信号，特别是对于小和中等 CV 变化，在移位大小、样本大小和CV 的受控目标值。此外，