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Related Experiment Video

Updated: May 8, 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

A novel EWMA-based adaptive control chart for industrial application by using hastings approximation.

Muhammad Atif Sarwar¹, Muhammad Hanif², Fahad R Albogamy³

¹COMSATS University Islamabad-Lahore Campus, Lahore, Pakistan.

Scientific Reports

|December 28, 2024

Summary

This study introduces an adaptive control chart for the Truncated Transmuted Burr-II (TTB-II) distribution, improving process monitoring when data isn't normally distributed. It offers better detection of irregular variations compared to existing methods.

Keywords:

Adaptive control chart Industrial process control Nonnormal process Production monitoring Simulation study

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Area of Science:

Statistical Process Control
Quality Management
Distribution Theory

Background:

Traditional control charts assume normal distribution, often unmet in practice.
Monitoring non-normal data requires robust statistical methods.
Existing methods may lack sensitivity to specific variations.

Purpose of the Study:

To develop an adaptive control chart for the Truncated Transmuted Burr-II (TTB-II) distribution.
To monitor irregular variations in non-normal process data.
To enhance process monitoring accuracy and efficiency.

Main Methods:

Utilized an exponentially weighted moving average (EWMA) statistic.
Employed Hastings approximation for normalization.
Proposed a continuous function for adaptive smoothing constant adjustment.
Evaluated performance using Average Run Length (ARL) and Standard Deviation of Run Length (SDRL).

Main Results:

The proposed TTB-II control chart demonstrated competitive advantages over existing distributions (TB-II, TB-III, TB-XII, B-II, B-III, B-XII).
Monte Carlo simulations confirmed the effectiveness of the TTB-II chart in terms of run-length profiles.
The adaptive nature of the chart improved detection capabilities for irregular variations.

Conclusions:

The developed adaptive TTB-II control chart is effective for monitoring non-normal processes.
The chart offers a superior alternative to existing methods for specific applications.
Practical implementation is feasible, as shown with a real dataset.