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Time-Domain Interpretation of PD Control01:07

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Proportional-Derivative (PD) control is a widely used control method in various engineering systems to enhance stability and performance. In a system with only proportional control, common issues include high maximum overshoot and oscillation, observed in both the error signal and its rate of change. This behavior can be divided into three distinct phases: initial overshoot, subsequent undershoot, and gradual stabilization.
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Prediction of Motor Failure Time Using An Artificial Neural Network.

Gustavo Scalabrini Sampaio1, Arnaldo Rabello de Aguiar Vallim Filho2, Leilton Santos da Silva3

  • 1Postgraduate Program in Electrical Engineering and Computing, Mackenzie Presbyterian University, Rua da Consolação, 896, Prédio 30-Consolação, São Paulo 01302-907, Brazil. gustavo.sampaio@mackenzista.com.br.

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|October 11, 2019
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Summary
This summary is machine-generated.

This study introduces a method using vibration data and Artificial Neural Networks (ANNs) for predictive maintenance. ANNs effectively predict equipment failure, supporting smart industry growth.

Keywords:
artificial neural networkcondition-based maintenanceindustry maintenancepredictive maintenancesmart industryvibratory analysis

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Area of Science:

  • Mechanical Engineering
  • Data Science
  • Artificial Intelligence

Background:

  • Industry seeks to minimize costs by avoiding corrective maintenance.
  • Scheduled maintenance is not always the most cost-effective solution.
  • Condition-based maintenance offers a more efficient approach.

Purpose of the Study:

  • To develop a methodology for processing vibration data from a simulated motor.
  • To construct a dataset for training and testing Artificial Neural Networks (ANNs).
  • To enable ANNs to predict future equipment conditions and potential failures.

Main Methods:

  • A device model simulating motor vibrations was created using a fan and magnets.
  • Vibration data were collected using an accelerometer and processed into a structured dataset.
  • An Artificial Neural Network was trained and tested using the prepared dataset.

Main Results:

  • ANN training demonstrated rapid and stable convergence.
  • Cross-validation and generalization tests showed excellent performance.
  • ANNs outperformed other machine learning techniques in generalizability.

Conclusions:

  • Artificial Neural Networks can effectively perform predictive maintenance tasks on industrial equipment.
  • This predictive capability supports the advancement of smart industries.
  • The proposed methodology provides a viable approach for condition-based maintenance.