Jove
Visualize
Contact Us

Related Concept Videos

Fault Types01:18

Fault Types

130
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...
130
Bus Impedance Matrix01:24

Bus Impedance Matrix

182
Calculating subtransient fault currents for three-phase faults in an N-bus power system involves using the positive-sequence network. When a three-phase short circuit occurs at a specific bus, the analysis uses the superposition method to evaluate two separate circuits.
In the first circuit, all machine voltage sources are short-circuited, leaving only the prefault voltage source at the fault location. The positive-sequence bus impedance matrix can be determined by solving the nodal equations,...
182
Multimachine Stability01:25

Multimachine Stability

234
Multimachine stability analysis is crucial for understanding the dynamics and stability of power systems with multiple synchronous machines. The objective is to solve the swing equations for a network of M machines connected to an N-bus power system.
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:
234
Types of Errors: Detection and Minimization01:12

Types of Errors: Detection and Minimization

2.6K
Error is the deviation of the obtained result from the true, expected value or the estimated central value. Errors are expressed in absolute or relative terms.
Absolute error in a measurement is the numerical difference from the true or central value. Relative error is the ratio between absolute error and the true or central value, expressed as a percentage.
Errors can be classified by source, magnitude, and sign. There are three types of errors: systematic, random, and gross.
Systematic or...
2.6K

You might also read

Related Articles

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

Sort by
Same author

Machine Learning-Driven Techno-Economic Uncertainty Analysis in Batch Pharmaceutical Manufacturing.

ACS omega·2026
Same author

Can Focusing on One Deep Learning Architecture Improve Fault Diagnosis Performance?

Journal of chemical information and modeling·2025
Same author

Comprehensive Characterization of Commercial Reverse Osmosis Membranes through High-Temperature Cross-Flow Filtration.

ACS omega·2024
Same author

Facile and Efficient Phase-Selective Powder Polymer Organogelator for Oil Spill Remediation.

Langmuir : the ACS journal of surfaces and colloids·2022
Same author

Unsupervised Machine Learning Applied to Seismic Interpretation: Towards an Unsupervised Automated Interpretation Tool.

Sensors (Basel, Switzerland)·2021
Same author

Flocculation Efficiency and Spatial Distribution of Water in Oil Sands Tailings Flocculated with a Partially Hydrophobic Graft Copolymer.

ACS applied materials & interfaces·2021
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 Experiment Video

Updated: Sep 17, 2025

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
04:23

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

2.0K

A Diagnosis-Based Siamese Network for Fault Detection Through Transfer Learning.

João G Neto1, Karla Figueiredo2, João B P Soares3

  • 1Department of Chemical and Materials Engineering, Pontifical Catholic University of Rio de Janeiro, 225, Marquês de São Vicente Street, Gávea, Rio de Janeiro, RJ 22451-900, Brazil.

Journal of Chemical Information and Modeling
|June 30, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a novel fault detection framework using Siamese neural networks and transfer learning. The approach effectively addresses data imbalance and improves differentiation between normal and faulty industrial operations.

More Related Videos

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
06:45

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

1.8K
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.9K

Related Experiment Videos

Last Updated: Sep 17, 2025

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
04:23

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

2.0K
Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
06:45

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

1.8K
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.9K

Area of Science:

  • Industrial Process Monitoring
  • Machine Learning
  • Deep Learning

Background:

  • Traditional deep learning methods struggle with imbalanced data and feature inconsistencies in industrial fault detection.
  • Combining diverse fault conditions into single categories limits data-driven algorithm performance.

Purpose of the Study:

  • To propose a fault detection framework that overcomes limitations of traditional methods, particularly data imbalance and variability.
  • To enhance the differentiation between normal and faulty industrial operations using advanced machine learning techniques.

Main Methods:

  • A framework combining Siamese neural networks with transfer learning, utilizing a pretrained fault diagnosis model.
  • Transforming fault detection from a classification problem to an embedding similarity task.
  • Leveraging knowledge from the attribute space of individual fault patterns.

Main Results:

  • Achieved an F1-score of 91.41% on the test set, demonstrating high detection accuracy.
  • t-distributed stochastic neighbor embedding confirmed effective discrimination between most faulty conditions.
  • Superior performance compared to recent literature in individual fault detection rates.

Conclusions:

  • The proposed framework offers a robust alternative for handling data imbalance and limited labeled anomaly data in industrial processes.
  • Transfer learning effectively enhances fault detection by enabling better discrimination of fault patterns.
  • The method shows significant potential for improving the reliability of industrial monitoring systems.