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相关概念视频

The R Chart01:02

The R Chart

109
In statistical process control, control charts, particularly R charts, are instrumental in monitoring process variations and identifying non-random patterns that run charts might miss. R charts track the variability within process subgroups, which is crucial when standard deviation use is impractical or unknown process variations exist.
R charts are pivotal for pinpointing shifts in process variability. Stability is indicated when all data points remain within the defined upper and lower...
109
Interpreting R Charts01:22

Interpreting R Charts

87
R chart, or range chart, is a fundamental tool in statistical process control used to monitor the variability within a process. It complements the X-bar (x̄) chart by focusing on the range of the data, rather than individual values, providing a clear picture of the process dispersion over time.
An R chart plots the range of subsets of measurements collected from a process. Each point on the chart represents the range—defined as the difference between the maximum and minimum...
87
Interpreting X̄ Charts01:13

Interpreting X̄ Charts

88
Interpreting x̄ charts, a type of control chart used in statistical process control helps monitor the variation in processes over time. The x̄ chart is based on the sample mean and allows for monitoring variations in the process mean over time. These charts are pivotal for quality assurance in manufacturing and other sectors.
An x̄ chart plots the values of individual measurements over time against control limits calculated from historical data. The central line...
88
Introduction to Statistical Process Control01:15

Introduction to Statistical Process Control

176
Statistical Process Control (SPC) is a method used to monitor and control quality within processes, particularly in manufacturing and service delivery, by employing statistical methods. SPC aims to distinguish between natural (common cause) variation and variation due to specific changes or events (special cause), allowing for timely improvements and sustained quality. The control chart, a pivotal tool in SPC, visually displays data over time alongside a central line of upper and lower control...
176
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

587
This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
587
Run Charts01:12

Run Charts

86
Run charts serve as an essential instrument for visualizing the performance of various processes over time, enabling the identification of trends and patterns crucial for quality improvement. These charts map out a series of data points chronologically, offering insights into the stability and efficiency of a process. A run chart's creation involves plotting data points on a graph, with the time intervals on the horizontal axis and the specific measurements on the vertical axis. For...
86

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相关实验视频

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A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
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适应性EWMA控制图使用贝叶斯方法在排序集采样方案下应用到硬烤过程.

Imad Khan1, Muhammad Noor-Ul-Amin2, Dost Muhammad Khan1

  • 1Department of Statistics, Abdul Wali Khan University Mardan, Khyber Pakhtunkhwa, Pakistan.

Scientific reports
|June 10, 2023
PubMed
概括

本研究引入了一种新的贝叶斯适应性EWMA (AEWMA) 控制图,使用排序集采样 (RSS) 来改进过程监控. 与传统的简单随机抽样技术相比,提出的方法提高了平均值转移的检测.

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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
  • 贝叶斯统计学贝叶斯统计学

背景情况:

  • 像CUSUM和EWMA这样的内存类型控制图表对于检测小到中等的过程转移是有效的.
  • 现有的方法通常依赖于简单的随机抽样 (SRS),这可能会限制灵敏度.

研究的目的:

  • 提出一个新的贝叶斯适应性EWMA (AEWMA) 控制图,使用排序集采样 (RSS).
  • 在平方误差损失函数 (SELF) 和linex损失函数 (LLF) 下监测正常分布过程的平均移位.

主要方法:

  • 开发一个包含RSS设计的贝叶斯AEWMA控制图.
  • 使用蒙特卡洛模拟的性能评估.
  • 基于SRS的比较与现有的贝叶斯AEWMA图表.

主要成果:

  • 拟议的贝叶斯AEWMA控制图与RSS方案显示在检测平均值转移方面具有更高的灵敏度.
  • 平均运行长度 (ARL) 和运行长度标准偏差 (SDRL) 的指标证实了性能的提高.
  • 半导体制造中的一个数值示例验证了拟议方法的优越性.

结论:

  • 使用RSS设计的贝叶斯AEWMA控制图比基于SRS的方法更有效地检测过程平均值的变化.
  • 拟议的图表提供了增强的灵敏度和失控信号检测能力.