Ayesha Sadaf, Arshad Asma, Albalawi Olayan, Alharthi Aiedh Mrisi, Hanif Muhammad, Yasmeen Uzma, Nabi Muhammad
Department of Statistics, National College of Business Administration and Economics, Lahore, Pakistan.
Department of Statistics, Faculty of Science, University of Tabuk, Tabuk, Saudi Arabia.
Sci Rep. 2024 May 21;14(1):11565. doi: 10.1038/s41598-024-62316-4.
This research presents a new adaptive exponentially weighted moving average control chart, known as the coefficient of variation (CV) EWMA statistic to study the relative process variability. The production process CV monitoring is a long-term process observation with an unstable mean. Therefore, a new modified adaptive exponentially weighted moving average (AAEWMA) CV monitoring chart using a novel function hereafter referred to as the "AAEWMA CV" monitoring control chart. the novelty of the suggested AAEWMA CV chart statistic is to identify the infrequent process CV changes. A continuous function is suggested to be used to adapt the plotting statistic smoothing constant value as per the process estimated shift size that arises in the CV parametric values. The Monte Carlo simulation method is used to compute the run-length values, which are used to analyze efficiency. The existing AEWMA CV chart is less effective than the proposed AAEWMA CV chart. An industrial data example is used to examine the strength of the proposed AAEWMA CV chart and to clarify the implementation specifics which is provided in the example section. The results strongly recommend the implementation of the proposed AAEWMA CV control chart.
本研究提出了一种新的自适应指数加权移动平均控制图，即变异系数（CV）EWMA统计量，用于研究相对过程变异性。生产过程CV监测是对均值不稳定的长期过程观测。因此，一种新的改进型自适应指数加权移动平均（AAEWMA）CV监测图，使用一种新颖的函数，以下简称为“AAEWMA CV”监测控制图。所建议的AAEWMA CV图统计量的新颖之处在于能够识别不频繁的过程CV变化。建议使用一个连续函数，根据CV参数值中出现的过程估计偏移量来调整绘图统计量的平滑常数值。采用蒙特卡罗模拟方法计算运行长度值，用于分析效率。现有的AEWMA CV图的有效性不如所提出的AAEWMA CV图。通过一个工业数据实例来检验所提出的AAEWMA CV图的优势，并在实例部分阐明其实施细节。结果强烈建议实施所提出的AAEWMA CV控制图。
