适应性EWMA控制图用于监测排序集采样方案下的变化系数
Afshan Riaz1, Muhammad Noor-Ul-Amin1, Walid Emam2
1COMSATS University Islamabad-Lahore Campus, Lahore, Pakistan.
Scientific reports
|October 17, 2023
概括
一个新的自适应指数加权移动变量系数 (AEWMCV) 控制图增强了过程监控. 它有效地检测过程变异性的变化,优于传统方法,特别是排序集采样技术.
科学领域:
- 工业工程 工业工程 工业工程
- 统计质量控制 统计质量控制
背景情况:
- 传统的控制图表与波动的工艺平均值和线性变化的标准偏差作斗争.
- 精确监测工艺变化对于保持产品质量和运营效率至关重要.
研究的目的:
- 引入一个自适应指数加权移动变量系数 (AEWMCV) 控制图.
- 通过排序集采样 (RSS) 和其变体来提高AEWMCV图表的性能.
- 评估拟议图的有效性与现有的变化系数 (CV) 控制图相比.
主要方法:
- 开发AEWMCV控制图,包括排序集采样,简单随机采样,四分位数RSS,中位数RSS和极端RSS.
- 使用平均运行长度 (ARL) 和运行长度标准偏差 (SDRL) 的指标进行性能评估.
- 将拟议的控制图应用于现实数据集以进行实际演示.
主要成果:
- 拟议的AEWMCV控制图表在与现有的CV控制图表相比,显示出更高的性能.
- 该图表在检测过程CV中轻微到中度变化的过程中特别有效.
- 现实世界的数据分析证实了AEWMCV控制图的实际适用性和有效性.
结论:
- 创新的AEWMCV控制图,增强了RSS技术,为具有动态平均值和标准偏差的过程提供了更好的性能.
- 这种方法提供了一个更敏感的工具,用于检测过程CV中的微妙变化.
- 这项研究对控制图表方法进行了有价值的进步,特别是在复杂或数据密集型场景中.
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