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関連する概念動画

The X̄ Chart00:58

The X̄ Chart

433
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...
433
Interpreting X̄ Charts01:13

Interpreting X̄ Charts

276
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...
276
Interpreting R Charts01:22

Interpreting R Charts

311
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...
311
The R Chart01:02

The R Chart

357
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...
357
Sampling Distribution01:12

Sampling Distribution

16.5K
Given simple random samples of size n from a given population with a measured characteristic such as mean, proportion, or standard deviation for each sample, the probability distribution of all the measured characteristics is called a sampling distribution. How much the statistic varies from one sample to another is known as the sampling variability of a statistic. You typically measure the sampling variability of a statistic by its standard error. The standard error of the mean is an example...
16.5K
Interpreting Run Charts01:25

Interpreting Run Charts

3.0K
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...
3.0K

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Monitoring process mean and dispersion with one double generally weighted moving average control chart.

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関連する実験動画

Updated: Jan 8, 2026

Visualization of Low-Level Gamma Radiation Sources Using a Low-Cost, High-Sensitivity, Omnidirectional Compton Camera
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ガンマ分布データに固定および可変サンプリング間隔を適用したAEWMA管理図の検討

Shin-Li Lu1, Meng-Chiao Chen2, Jen-Hsiang Chen3

  • 1Department of Industrial and Systems Engineering, Chung Yuan Christian University, Taoyuan, Taiwan. shinlilu@cycu.edu.tw.

Scientific reports
|December 12, 2025
PubMed
まとめ
この要約は機械生成です。

適応型EWMA(AEWMA)管理図は、歪んだデータのプロセス監視を改善します。その可変サンプリング間隔(VSI)スキームは、他の方法と比較して小さなプロセスシフトを検出する上で優れた感度と安定性を示します。

キーワード:
適応型EWMA管理図平均信号到達時間ガンマ分布可変サンプリング間隔

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Simulation of a Scaled Assembly Process with Collaboration of a Robotic Arm and Monitoring through a Vision System for Quality Control
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Expedited Radiation Biodosimetry by Automated Dicentric Chromosome Identification ADCI and Dose Estimation
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Expedited Radiation Biodosimetry by Automated Dicentric Chromosome Identification ADCI and Dose Estimation

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関連する実験動画

Last Updated: Jan 8, 2026

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Expedited Radiation Biodosimetry by Automated Dicentric Chromosome Identification ADCI and Dose Estimation
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科学分野:

  • 産業工学
  • 統計的プロセス制御
  • 品質管理

背景:

  • シェバート管理図は、小さなプロセスシフトを検出する上で限界があります。
  • 指数移動平均(EWMA)管理図は、マイナーシフトを検出しますが、急激な変化への反応が遅いです。
  • 適応型EWMA(AEWMA)管理図は、監視を強化するための動的な調整を提供します。

研究 の 目的:

  • 歪んだデータ、特にガンマ分布のプロセス監視のための適応型EWMA(AEWMA)管理図のパフォーマンスを評価すること。
  • AEWMAフレームワーク内での固定サンプリング間隔(FSI)スキームと可変サンプリング間隔(VSI)スキームの効果を比較すること。
  • 小さなプロセスシフトを検出するAEWMA管理図の感度と安定性を評価すること。

主な方法:

  • 歪んだデータの正規性近似のためにウィルソン-ヒルファティ変換を適用しました。
  • AEWMA FSIおよびVSIスキームを設計および評価するためにモンテカルロシミュレーションを利用しました。
  • 平均実行長(ARL)に加えて、パフォーマンス指標として平均信号到達時間(ATS)を採用しました。

主要な成果:

  • AEWMA VSI管理図は、小さなプロセスシフトを検出する上で高い感度と安定性を示しました。
  • AEWMA管理図は、歪んだデータに対して従来のEWMA管理図よりも優れたパフォーマンスを示しました。
  • 可変サンプリング間隔スキームは、一般的に固定サンプリング間隔スキームよりも優れたパフォーマンスを示しました。

結論:

  • AEWMA VSI管理図は、半導体製造などの産業における歪んだデータのプロセス監視のための堅牢で感度の高いツールです。
  • この研究は、統計的プロセス管理における適応型および可変サンプリング戦略の利点を強調しています。
  • AEWMA管理図は、正規分布しないプロセスデータを扱う品質管理システムに貴重な強化を提供します。