Search research articles

Related Concept Videos

Fault Types

Fault Types

When analyzing a single line-to-ground fault from phase A to ground at a three-phase bus, it is important to consider the fault impedance. This impedance is zero for a bolted fault, equal to the arc impedance for an arcing fault, and represents the total fault impedance for a transmission-line insulator flashover. To derive sequence and phase currents, fault conditions are translated from the phase domain to the sequence domain.
For line-to-line faults occurring between phases B and C, the...

You might also read

Related Articles

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

Sort by

Same author

Design of an optimal fractional fuzzy gain-scheduled Smith Predictor for a time-delay process with experimental application.

ISA transactions·2019

Same author

Second-order integral sliding-mode control with experimental application.

ISA transactions·2014

Same author

Author's reply:Comments on "Second-order sliding mode control with experimental application".

ISA transactions·2013

Same author

Second-order sliding mode control with experimental application.

ISA transactions·2010

Same author

Sliding mode control with PID sliding surface and experimental application to an electromechanical plant.

ISA transactions·2006

Same author

Experimental on-line identification of an electromechanical system.

ISA transactions·2004

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Search research articles

Related Experiment Video

Updated: Jun 5, 2026

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

Variance sensitive adaptive threshold-based PCA method for fault detection with experimental application.

Alkan Alkaya¹, Ilyas Eker

¹Department of Electrical and Electronic Engineering, Mersin University, 33343 Çiftlikköy, Mersin, Turkey. alkanalkaya@mersin.edu.tr

ISA Transactions

|January 22, 2011

Summary

This study introduces a novel Principal Component Analysis (PCA) method with a variance sensitive adaptive threshold for improved fault detection. The new technique effectively reduces false alarms in transient states, enhancing industrial process monitoring reliability.

More Related Videos

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine

Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine

Published on: January 5, 2024

Related Experiment Videos

Last Updated: Jun 5, 2026

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

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine

Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine

Published on: January 5, 2024

Area of Science:

Statistical Process Monitoring
Industrial Fault Detection

Background:

Principal Component Analysis (PCA) is a common statistical process monitoring technique.
Conventional PCA for fault detection uses fixed thresholds, leading to false alarms and data issues during transient process states.

Purpose of the Study:

To propose a new PCA method addressing false alarms and data loss in transient operations.
To enhance the reliability of industrial process monitoring systems.

Main Methods:

Development of a novel PCA method utilizing a variance sensitive adaptive threshold (T(vsa)).
Experimental implementation and validation on an electromechanical system.
Comparison with conventional PCA monitoring methods.

Main Results:

The proposed T(vsa) PCA method effectively overcomes false alarms in transient states.
The method demonstrates applicability and effectiveness in both steady-state and transient operations.
Early warning capabilities for operators were confirmed through experimental tests.

Conclusions:

The variance sensitive adaptive threshold PCA method is a reliable and effective solution for industrial process monitoring.
This advanced PCA technique improves fault detection accuracy during dynamic process conditions.