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相关概念视频

Introduction to Statistical Process Control01:15

Introduction to Statistical Process Control

143
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...
143
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

451
Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
451
The X̄ Chart00:58

The X̄ Chart

130
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...
130
Run Charts01:12

Run Charts

64
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...
64
Interpreting Run Charts01:25

Interpreting Run Charts

105
Run charts, essentially line graphs plotted over time, serve as fundamental yet effective tools for process analysis. They chronicle data sequentially, facilitating the identification of trends, shifts, or cyclical movements. This graphical representation is instrumental in determining whether a process is stable or exhibits signs of potential instability indicative of special cause variation. In the healthcare domain, run charts depict infection rates over time, enabling hospitals to monitor...
105
Interpreting R Charts01:22

Interpreting R Charts

68
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...
68

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相关实验视频

Updated: Jul 14, 2025

Operation of the Collaborative Composite Manufacturing CCM System
10:09

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强大的过程能力指数Cpm和Cpmk使用韦布尔过程.

Muhammad Kashif1, Sami Ullah2, Muhammad Aslam3

  • 1Department of Mathematics and Statistics, University of Agriculture, Faisalabad, Pakistan.

Scientific reports
|October 9, 2023
PubMed
概括

本研究评估了针对非正常制造数据的强大的工艺能力指数 (PCI). 吉尼平均差异 (GMD) 方法对不对称过程有希望,在某些条件下表现优于其他过程.

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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
  • 制造过程分析 制造过程分析

背景情况:

  • 工艺能力指数 (PCI) 评估制造质量,通常假设正常分布.
  • 非正常的过程需要强大的PCI,对第三代方法的研究有限.
  • 现有的强大的PCI往往侧重于第一代和第二代方法.

研究的目的:

  • 在第三代PCI中评估分散度 (MAD,IQR,GMD) 对非正常过程.
  • 为这些强大的PCI构建启动过程置信区间 (CI).
  • 根据韦布尔过程不对称性,比较这些方法与基于量子的PCI的有效性.

主要方法:

  • 模拟具有变异不对称性的韦布尔过程.
  • 评估中位数绝对偏差 (MAD),四分位数间距离 (IQR) 和吉尼平均差 (GMD) 作为分散度.
  • 构建和比较启动过程的置信区间 (BCPB,PB,PTB).

主要成果:

  • 基于量子的PCI对高过程不对称性敏感.
  • 在所有样本大小中,IQR表现不佳.
  • 在不对称性下,GMD表现良好,但需要小心处理;在低/中等不对称性中,MAD表现出色.
  • 推的CI:BCPB用于基于量子的PCI,PB/PTB用于基于MAD的PCI.

结论:

  • 在不对称的非正常过程中,GMD方法是第三代PCI的可行选择.
  • 基于MAD的PCI在低至中度不对称的情况下是有效的.
  • 适当的置信区间选择对于在非正常制造场景中进行可靠的PCI估计至关重要.