Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

The X̄ Chart00:58

The X̄ Chart

178
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...
178
Interpreting X̄ Charts01:13

Interpreting X̄ Charts

104
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...
104
The R Chart01:02

The R Chart

125
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...
125
Weighted Mean00:57

Weighted Mean

5.3K
While taking the arithmetic, geometric, or harmonic mean of a sample data set, equal importance is assigned to all the data points. However, all the values may not always be equally important in some data sets. An intrinsic bias might make it more important to give more weightage to specific values over others.
For example, consider the number of goals scored in the matches of a tournament. While computing the average number of goals scored in the tournament, it may be more important to...
5.3K
Run Charts01:12

Run Charts

98
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...
98
Interpreting R Charts01:22

Interpreting R Charts

99
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...
99

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Effect of rolling surface texture on bearing friction pairs lubrication.

iScience·2026
Same author

Supramolecular Polymer-Based Delayed Crosslinking Weighted Fracturing Fluid with a Double Network for Ultra-Deep Reservoirs.

Gels (Basel, Switzerland)·2026
Same author

Study on a Thermally Crosslinking Clay-Free Weak Gel Water-Based Drilling Fluid.

Gels (Basel, Switzerland)·2026
Same author

Characterization of ecto-5'-nucleotidase (CD73) involved in inflammatory regulation in Japanese flounder (Paralichthys olivaceus).

Fish & shellfish immunology·2026
Same author

Mathematical and Algorithmic Advances in Machine Learning for Statistical Process Control: A Systematic Review.

Entropy (Basel, Switzerland)·2026
Same author

Integrated Analysis of ATAC-Seq and RNA-Seq Reveals the Signal Transduction Regulation of the Molting Cycle in the Muscle of Chinese Mitten Crab (<i>Eriocheir sinensis</i>).

Biomolecules·2026

Related Experiment Video

Updated: Aug 23, 2025

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

14.6K

A one-sided exponentially weighted moving average control chart for time between events.

FuPeng Xie1, Philippe Castagliola2, YuLong Qiao1

  • 1School of Automation, Nanjing University of Science and Technology, Nanjing, People's Republic of China.

Journal of Applied Statistics
|November 3, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces an enhanced EWMA control chart for time-between-events to quickly detect process mean changes. The new chart proves more sensitive and robust to parameter estimation than existing methods.

Keywords:
62P30Control chartEWMAMarkov chain modelparameter estimationtime-between-events

More Related Videos

A Method for Tracking the Time Evolution of Steady-State Evoked Potentials
12:03

A Method for Tracking the Time Evolution of Steady-State Evoked Potentials

Published on: May 25, 2019

8.5K
Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons
07:59

Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons

Published on: June 9, 2023

1.4K

Related Experiment Videos

Last Updated: Aug 23, 2025

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

14.6K
A Method for Tracking the Time Evolution of Steady-State Evoked Potentials
12:03

A Method for Tracking the Time Evolution of Steady-State Evoked Potentials

Published on: May 25, 2019

8.5K
Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons
07:59

Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons

Published on: June 9, 2023

1.4K

Area of Science:

  • Industrial Engineering
  • Statistical Process Control

Background:

  • Exponentially weighted moving average (EWMA) control charts for time-between-events (TBE) are crucial for monitoring high-quality processes.
  • Early detection of process mean deteriorations is essential for maintaining product quality and efficiency.

Purpose of the Study:

  • To develop an enhanced one-sided EWMA TBE scheme for rapid detection of increases or decreases in the process mean.
  • To improve the sensitivity of EWMA TBE schemes using a truncation method.
  • To investigate the performance of the proposed scheme with estimated parameters.

Main Methods:

  • Development of an enhanced one-sided EWMA TBE scheme incorporating a truncation method.
  • Performance evaluation using Average Run Length (ARL) and Standard Deviation of Run Length (SDRL) via the Markov chain method.
  • Optimal design procedure based on ARL for the proposed EWMA TBE chart.

Main Results:

  • The proposed optimal one-sided EWMA TBE chart demonstrates superior sensitivity to both upward and downward mean shifts compared to existing schemes.
  • The enhanced scheme exhibits better performance in resisting the effects of parameter estimation.
  • Numerical results validate the effectiveness of the proposed chart.

Conclusions:

  • The enhanced one-sided EWMA TBE chart offers improved sensitivity and robustness for process monitoring.
  • The developed optimal design procedure facilitates practical implementation.
  • The proposed scheme is effective for both simulated and real-world datasets.