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The X̄ Chart

The X̄ Chart

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...

Interpreting X̄ Charts

Interpreting X̄ Charts

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...

Interpreting R Charts

Interpreting R Charts

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...

The R Chart

The R Chart

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...

Introduction to Statistical Process Control

Introduction to Statistical Process Control

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...

Interpreting Run Charts

Interpreting Run Charts

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...

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Related Experiment Video

Updated: May 7, 2025

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Published on: October 28, 2022

Machine learning based parameter-free adaptive EWMA control chart to monitor process dispersion.

Muhammad Noor-Ul-Amin¹, Muhammad Waqas Kazmi¹, Salem Alkhalaf²

¹Department of Statistics, COMSATS University Islamabad-Lahore Campus, Lahore, Pakistan.

Scientific Reports

|December 29, 2024

Summary

This study introduces an adaptive exponentially weighted moving average (AEWMA) control chart using support vector regression (SVR) for improved process dispersion monitoring. The novel approach enhances shift detection by adapting parameters, offering greater reliability in industrial applications.

Keywords:

Adaptive control charts Machine learning Support vector regression

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Published on: October 28, 2022

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Area of Science:

Industrial Engineering
Statistical Process Control
Machine Learning

Background:

Traditional control charts use fixed parameters, limiting adaptability during online monitoring.
Adaptive control charts dynamically adjust parameters for enhanced process control.
Effective monitoring of process dispersion is crucial across diverse operational environments.

Purpose of the Study:

To develop and evaluate an adaptive exponentially weighted moving average (AEWMA) control chart.
To integrate support vector regression (SVR) for adaptive parameter adjustment in control charting.
To improve the sensitivity and reliability of process dispersion monitoring.

Main Methods:

Implementation of an adaptive exponentially weighted moving average (AEWMA) control chart.
Utilizing support vector regression (SVR) with linear, polynomial, and radial basis function (RBF) kernels.
Adapting the smoothing constant based on detected shifts in process dispersion.

Main Results:

The proposed SVR-based AEWMA control chart demonstrates enhanced performance in detecting process dispersion shifts.
The RBF kernel within the SVR framework proved particularly effective for adaptive monitoring.
Validation using real-life data confirmed the method's adaptability and reliability.

Conclusions:

The SVR-based AEWMA control chart offers a robust and adaptive solution for process dispersion monitoring.
This approach improves upon conventional methods by dynamically adjusting control parameters.
The study highlights the potential of machine learning integration in statistical process control.