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Related Experiment Video

Updated: Jul 7, 2026

Simulation of a Scaled Assembly Process with Collaboration of a Robotic Arm and Monitoring through a Vision System for Quality Control
05:47

Simulation of a Scaled Assembly Process with Collaboration of a Robotic Arm and Monitoring through a Vision System for Quality Control

Published on: August 29, 2025

Neural-network-based robust fault diagnosis in robotic systems.

A T Vemuri1, M M Polycarpou

  • 1Dept. of Engine and Vehicle Res., Southwest Res. Inst., San Antonio, TX.

IEEE Transactions on Neural Networks
|January 1, 1997
PubMed
Summary

This study introduces a robust fault diagnosis scheme for robotic manipulators, using neural networks to effectively detect and manage system faults despite modeling uncertainties. The approach ensures reliable operation of robotic systems.

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Area of Science:

  • Robotics
  • Control Systems
  • Artificial Intelligence

Background:

  • Fault diagnosis is critical for modern robotic systems.
  • Model-based analytical redundancy is a common approach for robotic manipulator fault diagnosis.
  • Modeling uncertainties pose a significant challenge to the performance of fault diagnosis schemes.

Purpose of the Study:

  • To investigate fault diagnosis in rigid-link robotic manipulators considering modeling uncertainties.
  • To develop a robust fault diagnosis scheme using a learning architecture.
  • To rigorously establish the robustness and stability of the proposed fault diagnosis scheme.

Main Methods:

  • Utilizing a learning architecture with sigmoidal neural networks.
  • Monitoring the robotic system for off-nominal behavior indicative of faults.

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Last Updated: Jul 7, 2026

Simulation of a Scaled Assembly Process with Collaboration of a Robotic Arm and Monitoring through a Vision System for Quality Control
05:47

Simulation of a Scaled Assembly Process with Collaboration of a Robotic Arm and Monitoring through a Vision System for Quality Control

Published on: August 29, 2025

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
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  • Rigorously establishing robustness and stability properties of the fault diagnosis scheme.

    • Main Results:

    • The proposed neural-network-based scheme effectively detects and accommodates faults.
    • The fault diagnosis scheme demonstrates robustness against modeling uncertainties.
    • Simulation examples confirm the scheme's ability to handle faults in a two-link robotic manipulator.

    Conclusions:

    • A robust fault diagnosis scheme using neural networks is effective for robotic manipulators.
    • The developed method addresses the challenge of modeling uncertainties in fault diagnosis.
    • The approach enhances the reliability and operational safety of robotic systems.