The coefficient of variation (CV) usually describes the relative dispersion in production or service processes and has been widely applied in various fields. Monitoring the CV has received great attention in statistical process monitoring. This paper develops two one-sided adaptive EWMA (AEWMA) CV schemes to enhance the existing CV schemes’ monitoring efficiency. This scheme can effectively defect various shift sizes by dynamically adjusting the smoothing parameter of the EWMA based on the residuals. The optimization models of the AEWMA CV are designed based on the Markov chain method from two different perspectives, (i) a pair of changes; (ii) a range of changes. The numerical and graphical comparisons confirm the superiority of the proposed schemes. Moreover, in the data collection process, measurement errors from devices and human operations may arise. To assess the impact of such measurement errors on the monitoring performance, a linear covariate error model is adopted. For illustration and validation, the proposed schemes are implemented to a real industrial example from the sintering process.

中文翻译：

---

自适应 EWMA 监测方案的优化设计，用于变异系数和测量误差的性能评估

变异系数（CV）通常描述生产或服务过程中的相对离散程度，在各个领域得到了广泛的应用。 CV的监测在统计过程监测中受到了极大的关注。本文开发了两种单侧自适应 EWMA (AEWMA) CV 方案，以提高现有 CV 方案的监控效率。该方案通过根据残差动态调整EWMA的平滑参数，可以有效地检测各种移位大小。 AEWMA CV的优化模型是基于马尔可夫链方法从两个不同的角度设计的，（i）一对变化； (ii) 一系列变化。数值和图形比较证实了所提出方案的优越性。此外，在数据采集过程中，可能会出现设备和人为操作带来的测量误差。为了评估此类测量误差对监测性能的影响，采用线性协变量误差模型。为了说明和验证，所提出的方案被实施到烧结过程中的真实工业示例中。