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Adaptive Scheme for Detecting Induction Motor Incipient Broken Bar Faults at Various Load and Inertia Conditions.

Mohamed Esam El-Dine Atta1, Doaa Khalil Ibrahim1, Mahmoud Gilany1

  • 1Department of Electrical Power Engineering, Faculty of Engineering, Cairo University, Giza 12613, Egypt.

Sensors (Basel, Switzerland)
|January 11, 2022
PubMed
Summary

This study presents a new online adaptive protection scheme for detecting and diagnosing broken bar faults (BBFs) in induction motors. The method accurately identifies BBFs in early stages under various conditions without needing prior settings.

Keywords:
Fast Fourier Transform (FFT)broken bar faults (BBFs)incipient BBFsnon-adjacent BBFsvariable inertiavariable load

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Area of Science:

  • Electrical Engineering
  • Mechanical Engineering
  • Condition Monitoring

Background:

  • Induction motors are critical in industrial applications.
  • Broken bar faults (BBFs) are common and can lead to significant downtime.
  • Early and accurate detection of BBFs is essential for reliable operation.

Purpose of the Study:

  • To introduce a novel online adaptive protection scheme for detecting and diagnosing broken bar faults (BBFs) in induction motors.
  • To enable precise detection of incipient adjacent and non-adjacent BBFs under diverse operating conditions and noisy environments.
  • To develop a scheme that does not require predetermined settings and can adaptively determine thresholds.

Main Methods:

  • Utilizes an analytical approach to monitor variations in the phase angle of main sideband frequency components.
  • Applies Fast Fourier Transform (FFT) to a single phase of the stator current.
  • Employs an adaptive threshold calculation for fault discrimination and proposes a fault severity index.

Main Results:

  • The scheme accurately detects half, one, two, or three broken bars (adjacent/non-adjacent) in their incipient phases.
  • Achieves up to 100% accuracy in detection and severity estimation, independent of motor parameters.
  • Demonstrates effectiveness under variable inertia, loading conditions, and in noisy environments, verified by simulations and experimental data.

Conclusions:

  • The proposed online adaptive protection scheme offers a robust and accurate solution for BBF detection and diagnosis in induction motors.
  • The method's adaptability and independence from pre-set parameters make it highly practical for real-world applications.
  • This approach significantly enhances the reliability and predictive maintenance capabilities for induction motor systems.