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Conducting a three-phase short circuit test on an unloaded synchronous machine helps understand its impact on the system. The AC fault current's oscillogram, with the DC offset removed, reveals that the waveform amplitude decreases from an initially high value to a steady-state level for one phase of the machine.
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A Visual Fault Detection Method for Induction Motors Based on a Zero-Sequence Current and an Improved Symmetrized Dot

Liangyuan Huang1,2, Jihong Wen1,2, Yi Yang1,2

  • 1College of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, China.

Entropy (Basel, Switzerland)
|May 28, 2022
PubMed
Summary

This study introduces a visual fault detection method for induction motors using zero-sequence current and a novel local symmetrized dot pattern (LSDP). The LSDP method achieves 96.85% accuracy in detecting motor faults from current signals without extra sensors.

Keywords:
fault detectioninduction motorskernel density estimationlocal symmetrized dot patternzero-sequence current

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Area of Science:

  • Electrical Engineering
  • Mechanical Engineering
  • Signal Processing

Background:

  • Motor faults, particularly mechanical ones, exhibit subtle amplitudes in stator current.
  • Effective signal representation for motor current faults remains a challenge.

Purpose of the Study:

  • To develop a visual fault detection method for induction motors using zero-sequence current.
  • To address limitations in current signal representation for fault detection.

Main Methods:

  • Empirical Mode Decomposition (EMD) to filter power frequency from zero-sequence current.
  • A novel Local Symmetrized Dot Pattern (LSDP) method for robust image mapping.
  • Kernel Density Estimation (KDE) to enhance image contrast for normal vs. fault samples.

Main Results:

  • The LSDP method generates intuitive 2D image representations from current signals.
  • Visual differences between normal and faulty motor states are enhanced.
  • Fault detection accuracy reached 96.85% using the proposed method.

Conclusions:

  • The proposed visual fault detection method effectively identifies motor faults using current signals.
  • This approach eliminates the need for additional sensors like vibration detectors.
  • 2D image representation is a viable technique for current-based motor fault diagnosis.