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相关概念视频

The X̄ Chart00:58

The X̄ Chart

103
The  x̄ chart is a statistical tool for monitoring the means in a process.
The x̄ chart, often known as the individual control chart, is a crucial tool in statistical process control. It is designed to monitor process behavior and performance over time and is widely used in various industries to ensure that processes are operating at their optimum capacity and within specified limits.
A x̄ chart is constructed by plotting individual measurements of a quality...
103
The R Chart01:02

The R Chart

57
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...
57
Introduction to Statistical Process Control01:15

Introduction to Statistical Process Control

77
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...
77
Interpreting R Charts01:22

Interpreting R Charts

52
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...
52
Interpreting X̄ Charts01:13

Interpreting X̄ Charts

56
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...
56
Sample Size Calculation01:19

Sample Size Calculation

3.2K
Knowledge of the sample size is the first requirement to conduct random sampling or an experiment. The sample size is the total number of units, observations, or groups (in some cases) used to get the data to estimate a population parameter. As the name suggests, the sample size is that of the sample drawn from the population and differs from the population size.
The sample size for the given experiment or sampling effort is fundamental to any study design. Sample size decides the number of...
3.2K

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相关实验视频

Updated: Jun 9, 2025

Design and Optimization Strategies of a High-Performance Vented Box
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使用可变样本大小和工程应用的贝叶斯控制图.

Imad Khan1, Atif M Alamri2, Abdullah M Almarashi3

  • 1Abdul Wali Khan University Mardan, Mardan, Pakistan.

Scientific reports
|October 21, 2024
PubMed
概括

本研究引入了使用贝叶斯方法的可变样本大小 (VSS) 的自适应指数加权移动平均 (AEWMA) 控制图. 新图表在动态制造环境中提供了更好的检测和更少的错误报警.

关键词:
这就是ARL.ARL.贝叶斯的方法是贝叶斯的方法.控制图表中的控制图表.记录 记录正常 记录正常马克斯-EWMAMA 的时间.SDRL SDRL SDRL SDRL SDRL SDRL SDRL SDRL SDRL SDRL SDRL SDRL SDRL SDRL SDRL SDRL SDRL

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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
  • 运营研究 运营研究

背景情况:

  • 传统的统计过程控制 (SPC) 方法经常与动态的制造环境作斗争.
  • 现有的贝叶斯式EWMA和AEWMA图表具有固定的样本大小,在响应和检测方面存在局限性.

研究的目的:

  • 提出一个创新的自适应指数加权移动平均线 (AEWMA) 控制图,该控制图在贝叶斯方法论下结合了可变样本大小 (VSS).
  • 在动态制造环境中提高统计过程控制的响应性和有效性.

主要方法:

  • 使用可变样本大小 (VSS) 开发一个自适应指数加权移动平均 (AEWMA) 控制图.
  • 整数线性函数的集成用于基于AEWMA统计数据的动态样本大小调整.
  • 从EWMA图表中纳入平滑常数,以提高监控响应能力.
  • 进行了广泛的模拟,将拟议图与现有的贝叶斯式EWMA和AEWMA图与固定的样本大小 (FSS) 进行比较.

主要成果:

  • 拟议的贝叶斯VAEWMA控制图表显示,与现有方法相比,其性能优越.
  • 新的图表显示了用于检测改进的增强灵敏度.
  • 随着拟议的图表,观察到虚假报警率的显著下降.
  • 贝叶斯VAEWMA图表在模拟中被证明是整体上更有效的.

结论:

  • 这些发现支持在动态制造过程中需要动态统计过程控制工具.
  • 适应性SPC方法对于优化现代制造环境中的控制至关重要.
  • 一个真实数据应用验证了拟议的贝叶斯VAEWMA控制图的有效性和最佳性能.