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 Experiment Videos

Artificial neural network based fault identification scheme implementation for a three-phase induction motor.

Sri R Kolla1, Shawn D Altman

  • 1Electronics and Computer Technology Program, Department of Technology Systems, Bowling Green State University, Bowling Green, OH 43403, USA. skolla@bgsu.edu

ISA Transactions
|March 6, 2007
PubMed
Summary

This study introduces a PC-based system using artificial neural networks (ANNs) for real-time monitoring and fault identification in three-phase induction motors, enhancing operational safety and reliability.

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same journal

Observer-based output tracking for asynchronous boolean control networks under noise.

ISA transactions·2026
Same journal

Lateral stability control of distributed drive electric vehicles via controllable-region analysis and adaptive torque distribution.

ISA transactions·2026
Same journal

Toward cross-domain zero-shot diagnosis: Integrating fault type and severity assessment in complex mechanical systems.

ISA transactions·2026
Same journal

Safety control for nonlinear systems with unknown relative degree.

ISA transactions·2026
Same journal

LLM-assisted adaptive dynamic programming and prescribed performance control for USV trajectory tracking under stochastic disturbances.

ISA transactions·2026
Same journal

Stability-constrained incremental learning for robust ROV control with feasibility-guided data selection.

ISA transactions·2026

Area of Science:

  • Electrical Engineering
  • Artificial Intelligence
  • Motor Control Systems

Background:

  • Three-phase induction motors are critical in industrial applications.
  • Effective fault detection is essential for preventing failures and ensuring safety.
  • Traditional monitoring methods can be limited in identifying complex faults.

Purpose of the Study:

  • To develop and test a PC-based system for monitoring and fault identification.
  • To utilize artificial neural networks (ANNs) for intelligent fault detection.
  • To implement a real-time system for immediate fault response.

Main Methods:

  • Designed and built a hardware system to acquire motor voltage and current data.
  • Developed a software program for data acquisition and processing.

Related Experiment Videos

  • Trained a feed-forward neural network using JavaNNS for fault classification.
  • Integrated the trained ANN into a LabVIEW environment for online monitoring and control.

    • Main Results:

    • Successfully implemented a PC-based monitoring and fault identification scheme.
    • The system accurately identified various motor faults in real-time.
    • The trained artificial neural network demonstrated effective fault detection capabilities.
    • The system successfully triggered motor shutdown upon fault detection.

    Conclusions:

    • The developed PC-based ANN system is effective for real-time fault monitoring and identification in three-phase induction motors.
    • This intelligent system enhances motor reliability and operational safety.
    • The approach offers a robust solution for industrial motor diagnostics.