Jove
Visualize
Contáctanos

Videos de Conceptos Relacionados

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

También podría leer

Artículos Relacionados

Artículos vinculados a este trabajo por autores compartidos, revista y gráfico de citas.

Ordenar por
Same author

Generally weighted moving average control chart in the presence of measurement error via auxiliary information utilization.

PloS one·2025
Same author

A new EWMA chart for simultaneously monitoring the parameters of a shifted exponential distribution.

Journal of applied statistics·2025
Same author

Distribution-free Phase II triple EWMA control chart for joint monitoring the process location and scale parameters.

Journal of applied statistics·2024
Same author

A double generally weighted moving average control chart for monitoring the process variability.

Journal of applied statistics·2023
Same author

Logarithmic confidence estimation of a ratio of binomial proportions for dependent populations.

Journal of applied statistics·2023
Same author

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

Journal of applied statistics·2022
JoVE
x logofacebook logolinkedin logoyoutube logo
ACERCA DE JoVE
Visión GeneralLiderazgoBlogCentro de Ayuda JoVE
AUTORES
Proceso de PublicaciónConsejo EditorialAlcance y PolíticasRevisión por ParesPreguntas FrecuentesEnviar
BIBLIOTECARIOS
TestimoniosSuscripcionesAccesoRecursosConsejo Asesor de BibliotecasPreguntas Frecuentes
INVESTIGACIÓN
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchivo
EDUCACIÓN
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualCentro de Recursos para ProfesoresSitio de Profesores
Términos y Condiciones de Uso
Política de Privacidad
Políticas

Video Experimental Relacionado

Updated: Jan 8, 2026

Visualization of Low-Level Gamma Radiation Sources Using a Low-Cost, High-Sensitivity, Omnidirectional Compton Camera
06:28

Visualization of Low-Level Gamma Radiation Sources Using a Low-Cost, High-Sensitivity, Omnidirectional Compton Camera

Published on: January 30, 2020

13.2K

Gráfico de control AEWMA aplicado a datos con distribución Gamma con intervalos de muestreo fijos y variables

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
Resumen
Este resumen es generado por máquina.

El gráfico de control EWMA adaptativo (AEWMA) mejora la monitorización de procesos con datos sesgados. Su esquema de intervalo de muestreo variable (VSI) muestra una sensibilidad y estabilidad superiores en la detección de pequeños cambios en el proceso en comparación con otros métodos.

Palabras clave:
gráfico de control EWMA adaptativotiempo medio hasta la señaldistribución Gammaintervalo de muestreo variable

Más Videos Relacionados

Simulation of a Scaled Assembly Process with Collaboration of a Robotic Arm and Monitoring through a Vision System for Quality Control
05:47

Simulation of a Scaled Assembly Process with Collaboration of a Robotic Arm and Monitoring through a Vision System for Quality Control

Published on: August 29, 2025

381
Expedited Radiation Biodosimetry by Automated Dicentric Chromosome Identification ADCI and Dose Estimation
10:33

Expedited Radiation Biodosimetry by Automated Dicentric Chromosome Identification ADCI and Dose Estimation

Published on: September 4, 2017

16.4K

Videos de Experimentos Relacionados

Last Updated: Jan 8, 2026

Visualization of Low-Level Gamma Radiation Sources Using a Low-Cost, High-Sensitivity, Omnidirectional Compton Camera
06:28

Visualization of Low-Level Gamma Radiation Sources Using a Low-Cost, High-Sensitivity, Omnidirectional Compton Camera

Published on: January 30, 2020

13.2K
Simulation of a Scaled Assembly Process with Collaboration of a Robotic Arm and Monitoring through a Vision System for Quality Control
05:47

Simulation of a Scaled Assembly Process with Collaboration of a Robotic Arm and Monitoring through a Vision System for Quality Control

Published on: August 29, 2025

381
Expedited Radiation Biodosimetry by Automated Dicentric Chromosome Identification ADCI and Dose Estimation
10:33

Expedited Radiation Biodosimetry by Automated Dicentric Chromosome Identification ADCI and Dose Estimation

Published on: September 4, 2017

16.4K

Área de la Ciencia:

  • Ingeniería Industrial
  • Control Estadístico de Procesos
  • Gestión de Calidad

Sus antecedentes:

  • Los gráficos de control de Shewhart son limitados en la detección de pequeños cambios en el proceso.
  • Los gráficos de media móvil con ponderación exponencial (EWMA) detectan cambios menores pero son lentos para reaccionar a cambios repentinos.
  • Los gráficos de control EWMA adaptativos (AEWMA) ofrecen ajustes dinámicos para una monitorización mejorada.

Objetivo del estudio:

  • Evaluar el rendimiento del gráfico de control EWMA adaptativo (AEWMA) para datos sesgados, específicamente distribuciones Gamma.
  • Comparar la efectividad de los esquemas de intervalo de muestreo fijo (FSI) y de muestreo variable (VSI) dentro del marco AEWMA.
  • Evaluar la sensibilidad y estabilidad de los gráficos AEWMA en la detección de pequeños cambios en el proceso.

Principales métodos:

  • Se aplicó la transformación de Wilson-Hilferty para la aproximación de normalidad de datos sesgados.
  • Se utilizaron simulaciones de Monte Carlo para diseñar y evaluar los esquemas AEWMA FSI y VSI.
  • Se empleó el Tiempo Medio Hasta la Señal (ATS) como métrica de rendimiento, junto con la Longitud Media de la Carrera (ARL).

Principales resultados:

  • El gráfico AEWMA VSI demostró una mayor sensibilidad y estabilidad en la detección de pequeños cambios en el proceso.
  • Los gráficos AEWMA mostraron un mejor rendimiento que los gráficos EWMA tradicionales para datos sesgados.
  • Los esquemas de intervalo de muestreo variable generalmente superaron a los esquemas de intervalo de muestreo fijo.

Conclusiones:

  • El gráfico de control AEWMA VSI es una herramienta robusta y sensible para monitorizar procesos con datos sesgados, particularmente en industrias como la fabricación de semiconductores.
  • El estudio destaca las ventajas de las estrategias de muestreo adaptativo y variable en el control estadístico de procesos.
  • Los gráficos AEWMA proporcionan una mejora valiosa para los sistemas de gestión de calidad que manejan datos de procesos no distribuidos normalmente.