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

Power System Three-Phase Short Circuits01:21

Power System Three-Phase Short Circuits

185
Determining the subtransient fault current in a power system involves representing transformers by their leakage reactances, transmission lines by their equivalent series reactances, and synchronous machines as constant voltage sources behind their subtransient reactances. In this analysis, certain elements are excluded, such as winding resistances, series resistances, shunt admittances, delta-Y phase shifts, armature resistance, saturation, saliency, non-rotating impedance loads, and small...
185
Three-Phase Short Circuit—Unloaded Synchronous Machine01:21

Three-Phase Short Circuit—Unloaded Synchronous Machine

290
Conducting a three-phase short circuit test on an unloaded synchronous machine helps understand its impact on the system. The AC fault current's oscillogram, with the DC offset removed, reveals that the waveform amplitude decreases from an initially high value to a steady-state level for one phase of the machine.
This behavior occurs due to the magnetic flux produced by the short-circuit armature currents. Initially, these currents follow high-reluctance paths but eventually shift to...
290
Distribution Reliability and Automation01:25

Distribution Reliability and Automation

191
Distribution reliability in electrical power systems is critical for ensuring an uninterrupted power supply to consumers at minimal cost. According to IEEE Standard Terms, reliability is the probability that a device will function without failure over a specified time period or amount of usage. For electric power distribution, this translates to maintaining continuous power supply and addressing customer concerns over power outages. Several indices, as defined by IEEE Standard 1366-2012, are...
191
Bus Impedance Matrix01:24

Bus Impedance Matrix

214
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,...
214
Fault Types01:18

Fault Types

160
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...
160
Electro-mechanical Systems01:19

Electro-mechanical Systems

1.3K
Electromechanical systems are intricate configurations that effectively combine electrical and mechanical elements to achieve a desired outcome. Central to many of these systems is the DC motor, a device that converts electrical energy into mechanical motion, enabling various applications ranging from simple fans to complex robotic mechanisms.
A key component of the DC motor is the armature, a rotating circuit positioned within a magnetic field. As an electric current passes through the...
1.3K

You might also read

Related Articles

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

Sort by
Same author

Data-Driven Low-Frequency Oscillation Event Detection Strategy for Railway Electrification Networks.

Sensors (Basel, Switzerland)·2023
Same author

Novel High Accuracy Resolver Topology for Space Applications.

Sensors (Basel, Switzerland)·2021
Same author

DC-Link Voltage and Catenary Current Sensors Fault Reconstruction for Railway Traction Drives.

Sensors (Basel, Switzerland)·2018
Same author

Integral Sensor Fault Detection and Isolation for Railway Traction Drive.

Sensors (Basel, Switzerland)·2018

Related Experiment Video

Updated: Oct 30, 2025

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

1.9K

Data-Driven Fault Diagnosis for Electric Drives: A Review.

David Gonzalez-Jimenez1, Jon Del-Olmo1, Javier Poza1

  • 1Faculty of Engineering, Mondragon Unibertsitatea, 20500 Arrasate-Mondragón, Gipuzkoa, Spain.

Sensors (Basel, Switzerland)
|July 2, 2021
PubMed
Summary

This review explores data-driven strategies for electric drive fault detection and diagnosis (FDD), highlighting Machine Learning techniques that are advancing Condition Monitoring beyond traditional methods.

Keywords:
condition monitoringdata-drivenelectric driveelectric tractionfault detectionfault diagnosismachine learning

More Related Videos

A Modeling and Simulation Method for Preliminary Design of an Electro-Variable Displacement Pump
09:04

A Modeling and Simulation Method for Preliminary Design of an Electro-Variable Displacement Pump

Published on: June 1, 2022

3.3K
Modeling and Experimental Analysis of the Single-Shaft Coaxial Motor-Pump Assembly in Electrohydrostatic Actuators
08:59

Modeling and Experimental Analysis of the Single-Shaft Coaxial Motor-Pump Assembly in Electrohydrostatic Actuators

Published on: June 13, 2022

2.8K

Related Experiment Videos

Last Updated: Oct 30, 2025

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

1.9K
A Modeling and Simulation Method for Preliminary Design of an Electro-Variable Displacement Pump
09:04

A Modeling and Simulation Method for Preliminary Design of an Electro-Variable Displacement Pump

Published on: June 1, 2022

3.3K
Modeling and Experimental Analysis of the Single-Shaft Coaxial Motor-Pump Assembly in Electrohydrostatic Actuators
08:59

Modeling and Experimental Analysis of the Single-Shaft Coaxial Motor-Pump Assembly in Electrohydrostatic Actuators

Published on: June 13, 2022

2.8K

Area of Science:

  • Engineering
  • Computer Science
  • Industrial Automation

Background:

  • Industry 4.0 digital technologies are transforming industrial sectors.
  • Massive operational data acquisition and processing are key trends.
  • Data-driven approaches are surpassing traditional Condition Monitoring methods.

Purpose of the Study:

  • To review data-driven active supervision strategies for electric drives.
  • To focus on fault detection and diagnosis (FDD) methods.
  • To identify research gaps and opportunities in this field.

Main Methods:

  • Overview of primary FDD methodologies.
  • Explanation of Machine Learning workflow implementation for data-driven strategies.
  • Comprehensive review of relevant scientific literature.

Main Results:

  • Data-driven strategies are increasingly prevalent in electric drive FDD.
  • Machine Learning and Deep Learning are central to modern Condition Monitoring.
  • Traditional model- and signal-based methods are being superseded.

Conclusions:

  • The review consolidates current data-driven FDD strategies for electric drives.
  • Guidelines for implementing Machine Learning workflows are provided.
  • Key research gaps and future opportunities in electric drive FDD are identified.