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Automatic Classification of Rotor Faults in Soft-Started Induction Motors, Based on Persistence Spectrum and

Vicente Biot-Monterde1, Angela Navarro-Navarro1, Israel Zamudio-Ramirez1,2

  • 1Instituto Tecnológico de la Energía (ITE), Universitat Politècnica de València (UPV), Camino de Vera S/N, 46022 Valencia, Spain.

Sensors (Basel, Switzerland)
|January 8, 2023
PubMed
Summary

This study introduces a new method using Persistence Spectrum and Convolutional Neural Networks to accurately detect broken rotor bars in induction motors (IMs) even with soft starters. The technique achieves near-perfect classification of motor health states.

Keywords:
CNNautomatic fault diagnosisbroken rotor barsinduction motorsoft startersstray-flux

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Area of Science:

  • Electrical Engineering
  • Machine Condition Monitoring
  • Artificial Intelligence in Industrial Applications

Background:

  • Induction motors (IMs) are crucial in industry but susceptible to rotor bar breakages (BRB), often exacerbated by soft starter use.
  • Soft starters complicate fault diagnosis by altering signal patterns, necessitating advanced detection methods.
  • Accurate rotor health monitoring is vital for preventing costly industrial downtime.

Purpose of the Study:

  • To develop an automated method for classifying rotor health states in induction motors driven by soft starters.
  • To address the diagnostic challenges posed by soft starter components in fault signal analysis.
  • To accurately detect and classify the severity of rotor bar breakages.

Main Methods:

  • Utilizing the Persistence Spectrum (PS) of start-up stray-flux signals for fault signature extraction.
  • Applying Data Augmentation Techniques (DAT), including Gaussian noise, to enhance the training dataset.
  • Training a Convolutional Neural Network (CNN) on PS images for automated rotor health classification (healthy, one broken bar, two broken bars).

Main Results:

  • The proposed method achieved a 100.00% classification rate for individual soft starter models.
  • A combined analysis across all tested models yielded a high accuracy of 99.89%.
  • Validation on a test bench confirmed the method's reliability in detecting rotor bar breakages.

Conclusions:

  • The Persistence Spectrum combined with CNN offers a robust and highly accurate solution for diagnosing rotor bar breakages in induction motors with soft starters.
  • This automated approach effectively overcomes the diagnostic interference introduced by soft starter devices.
  • The validated method provides a reliable tool for ensuring the operational integrity of induction motors in industrial settings.