Although the existence of natural variations in process control charts is inevitable, the formations of significant patterns associate out-of-control conditions. Hence, the detection of unnatural patterns is essential to increase the sensitivity of Shewhart’s control charts. In the previous years, numerous models have been offered for recognizing and analysing non-random patterns. These models commonly have applied the simulated samples for training, testing and evaluating, and often have simulated and supervised behaviours in the mean control chart. The serious problem for these models is that they have not simultaneously controlled the process variability and the process mean, while the control of variability chart is main prerequisite for the control of mean chart in the monitor of the variable quality characteristics. On the other hand, few classifier models of the variability control chart patterns have merely applied the unrealistic predefined simulators. In an overview, extraction of the known statistical distributions for the simulation of variations and patterns in the variability control charts has not yet been reported in the literature. Therefore, our paper organizes simulators of non-patterned and patterned samples in the variability control charts. In this work, we extract the known statistical distribution functions for simulation of natural variations in the variability control charts. Then the generating functions of significant patterns and their corresponding numerical parameters are described for these charts. Also, the organized simulator functions are compared with simulators applied by the previous papers. The guideline introduced in this research provides required tools for reliable simulations of samples in the variability control charts.

尽管过程控制图中自然变化的存在是不可避免的，但重要模式的形成与失控条件相关。因此，非自然模式的检测对于提高休哈特控制图的灵敏度至关重要。在前几年，已经提供了许多模型来识别和分析非随机模式。这些模型通常将模拟样本应用于训练、测试和评估，并且通常在均值控制图中具有模拟和监督的行为。这些模型的严重问题是它们没有同时控制过程变异和过程均值，而变异图的控制是变量质量特性监测中均值图控制的主要前提。另一方面，很少有变异控制图模式的分类器模型仅仅应用了不切实际的预定义模拟器。总而言之，文献中尚未报道为模拟变异控制图中的变异和模式而提取已知统计分布。因此，我们的论文在可变性控制图中组织了非图案化和图案化样本的模拟器。在这项工作中，我们提取了已知的统计分布函数，用于模拟可变性控制图中的自然变化。然后描述了这些图表的重要模式的生成函数及其相应的数值参数。此外，将组织的模拟器功能与之前论文中应用的模拟器进行了比较。