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相关概念视频

Interpreting X̄ Charts01:13

Interpreting X̄ Charts

64
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...
64
The X̄ Chart00:58

The X̄ Chart

116
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...
116
The R Chart01:02

The R Chart

78
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...
78
Introduction to Statistical Process Control01:15

Introduction to Statistical Process Control

114
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...
114
Interpreting R Charts01:22

Interpreting R Charts

63
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...
63
Design Example: Aggregate Gradation01:24

Design Example: Aggregate Gradation

93
The right type and quality of aggregates are crucial for concrete as they significantly influence its properties, mix proportions, and cost-effectiveness. If different sources are available for sand, the commonly used fine aggregate in concrete, the selection of sand is primarily based on its gradation.
The grading, or particle-size distribution, of sand is determined using sieve analysis, with standard sizes ranging from 150 μm to 10 mm (ASTM No. 100 sieve to 3⁄8 in. sieve). Sand is...
93

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相关实验视频

Updated: Jun 25, 2025

Surrogate Model Development for Digital Experiments in Welding
09:17

Surrogate Model Development for Digital Experiments in Welding

Published on: March 28, 2025

789

新的适应性EWMA CV控制图表适用于烧结过程.

Sadaf Ayesha1, Asma Arshad1, Olayan Albalawi2

  • 1Department of Statistics, National College of Business Administration and Economics, Lahore, Pakistan.

Scientific reports
|May 21, 2024
PubMed
概括

本研究引入了适应指数加权移动平均线 (EWMA) 控制图,用于监测变化系数 (CV). 新的AAEWMA CV图有效地检测不稳定的生产过程中不经常发生的CV变化.

关键词:
适应性控制图表 适应性控制图表运行时间的平均长度.变化系数 的变化系数.埃沃玛 (EWMA) 是一家在欧洲的公司.标准偏差的运行长度是标准偏差.统计过程控制统计过程控制

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Experimental Methods for Investigation of Shape Memory Based Elastocaloric Cooling Processes and Model Validation
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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
  • 过程监控 过程监控

背景情况:

  • 使用变化系数 (CV) 监测相对过程变化对于长期生产观测至关重要,特别是使用不稳定的介质.
  • 现有的指数加权移动平均线 (EWMA) 图表在检测过程CV中不经常发生的变化时可能缺乏灵敏度.
  • 适应性控制图表在检测过程转移方面提供了潜在的改进.

研究的目的:

  • 为变化系数 (CV) 监测开发和评估一种新的修改自适应指数加权移动平均线 (AAEWMA) 控制图.
  • 提高工业环境中罕见的过程CV变化的检测.
  • 提高现有的适应性EWMA CV图表的有效性.

主要方法:

  • 开发一种新的自适应功能,以根据估计的CV转移大小调整平滑常数.
  • 使用蒙特卡洛模拟方法计算效率分析的运行长度值.
  • 应用一个工业数据示例来证明图表的实际实施和有效性.

主要成果:

  • 拟议的AAEWMA CV控制图表表现出与现有的AEWMA CV图表相比更高的效率.
  • 适应功能有效地调整平滑常数,改善检测不频繁的CV转移.
  • 模拟结果证实了AAEWMA CV图表在识别过程可变性变化方面的增强性能.

结论:

  • 新型的AAEWMA心血管控制图在监测相对过程变异性和检测罕见的心血管变化方面非常有效.
  • 适应机制显著提高了EWMA心血管监测图表的灵敏度和性能.
  • 强烈建议在工业质量控制中实施AAEWMA CV图.