研究MEWMA-CoDa控制图的零状态和稳定状态性能,使用可变采样间隔
Muhammad Imran1, Jinsheng Sun1, Xuelong Hu2
1Nanjing University of Science and Technology, Nanjing, People's Republic of China.
Journal of applied statistics
|March 25, 2024
概括
本研究引入了一个新的多变量指数移动平均控制图组合数据,显著减少检测过程偏差的时间. 拟议的图表比现有方法提供了更好的性能,特别是在稳定状态条件下.
科学领域:
- 统计过程控制 统计过程控制
- 多变量质量控制多变量质量控制
- 组合式数据分析数据分析
背景情况:
- 传统的控制图使用固定的采样间隔,这对于检测过程转移可能是低效的.
- 可变采样间隔 (VSI) 方案根据过程监测统计数据调整采样频率,提供潜在的改进.
- 在工业过程中常见的组成数据,需要专门的方法来进行准确的分析.
研究的目的:
- 为组合数据 (MEWMA-C) 提出一个新的多变量指数移动平均值控制图.
- 为了优化图表参数,使用零状态和稳定状态的信号平均时间 (ATS).
- 为了评估拟议的MEWMA-C图表的统计性能.
主要方法:
- 采用同位数逻辑比 (ILR) 转换来处理组合数据.
- 开发了一个参数优化方法,考虑零状态 (Z) 和稳定状态 (SS) ATS.
- 采用连续时间马尔科夫链 (CTMC) 模型进行绩效评估.
主要成果:
- 拟议的MEWMA-C图表显著减少了与传统图表相比,失控 (OOC) 的平均时间到信号 (ATS).
- 变量数 (d) 对MEWMA-C图表的表现产生负面影响,而子组大小 (n) 则具有轻微的积极影响.
- 与EWMA-C和MEWMA等竞争图表相比,MEWMA-C图表表现出优异的表现,特别是在稳定状态条件下.
结论:
- MEWMA-C图表是监测工业过程中的组成数据的有效工具.
- 可变采样间隔策略提高了控制图的灵敏度.
- 拟议的方法在统计学上显著地提高了工艺监测的准确性.
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