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Supervised Machine-Learning Methodology for Industrial Robot Positional Health Using Artificial Neural Networks,

Ervin Galan-Uribe1, Juan P Amezquita-Sanchez1, Luis Morales-Velazquez1

  • 1Facultad de Ingeniería, Universidad Autónoma de Querétaro, Campus San Juan del Río, Río Moctezuma 249, Col. San Cayetano, San Juan del Río 76807, QRO, Mexico.

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Summary
This summary is machine-generated.

This study introduces a new method to detect robotic positional degradation using actuator current signals. The approach achieves 100% accuracy, enabling timely predictive maintenance and preventing manufacturing losses.

Keywords:
Katz fractal dimensionaccuracy degradationdiscrete wavelet transformfault prognosisprincipal component analysisrobot health

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Area of Science:

  • Robotics and Industrial Automation
  • Machine Learning for Predictive Maintenance

Background:

  • Robotic systems are crucial for repetitive industrial tasks requiring high positional accuracy.
  • Degradation in robot positional accuracy leads to significant resource loss.
  • Current predictive maintenance methods using external sensors are complex for industrial settings.

Purpose of the Study:

  • To propose an effective and simpler method for detecting positional degradation in robot joints.
  • To analyze actuator current signals for early fault diagnosis.

Main Methods:

  • Utilized discrete wavelet transform for signal processing.
  • Applied nonlinear indices and principal component analysis for feature extraction.
  • Employed artificial neural networks for classification of positional deviations.

Main Results:

  • The proposed methodology accurately detects robot positional degradation using actuator current signals.
  • Achieved 100% classification accuracy for robot positional degradation.
  • Demonstrated the effectiveness of analyzing current signals for health monitoring.

Conclusions:

  • Early detection of positional degradation is feasible by analyzing actuator currents.
  • The developed method facilitates timely implementation of prognosis and health management (PHM) strategies.
  • This approach helps prevent losses in manufacturing processes by ensuring robot accuracy.