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Current and Stray Flux Combined Analysis for Sparking Detection in DC Motors/Generators Using Shannon Entropy.

Jorge E Salas-Robles1, Vicente Biot-Monterde2, Jose A Antonino-Daviu2

  • 1Escuela Técnica Superior de Ingeniera Aeroespacial y Diseño Industrial, Universitat Politècnica de València (UPV), Camino de Vera s/n, 46022 Valencia, Spain.

Entropy (Basel, Switzerland)
|September 27, 2024
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Summary
This summary is machine-generated.

This study introduces a new method to monitor brushed DC motor health by analyzing Shannon entropy in electrical signals. This predictive maintenance tool detects sparking, preventing failures and improving electric vehicle reliability.

Keywords:
Shannon entropybrushed DC machinescurrent signalsstray flux signals

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Area of Science:

  • Electrical Engineering and Applied Physics
  • Mechanical Engineering and Tribology
  • Signal Processing and Machine Condition Monitoring

Background:

  • Brushed DC motors (DCMs) are crucial in automotive applications like electric vehicles (EVs) for their performance characteristics.
  • Premature failure in DCMs, often caused by brush-commutator sparking, leads to substantial economic losses and reduced operational reliability.
  • Effective condition monitoring is essential for predictive maintenance and mitigating failures in critical electromechanical systems.

Purpose of the Study:

  • To develop and validate novel methods for analyzing the temporal and frequency evolution of Shannon entropy in DCM signals.
  • To investigate the relationship between sparking phenomena, signal entropy, and fault severity in brushed DC motors.
  • To establish a predictive maintenance framework for early detection and assessment of commutation quality in DCMs.

Main Methods:

  • Two signal processing approaches were employed: Short-Time Fourier Transform (STFT) for indirect entropy analysis and the S-Transform for direct analysis.
  • Shannon entropy was calculated for armature current and stray flux signals to quantify signal complexity and information content.
  • Experimental data was used to correlate sparking activity with changes in entropy and frequency spectrum characteristics.

Main Results:

  • Increased sparking activity significantly elevates system entropy, particularly due to pronounced low-frequency harmonics in the analyzed signals.
  • Both STFT and S-Transform methods effectively captured the temporal and spectral evolution of entropy related to sparking.
  • A clear correlation was observed between sparking severity, harmonic content, and the rise in signal entropy.

Conclusions:

  • The proposed entropy-based analysis provides a robust method for detecting and quantifying sparking in brushed DC motors.
  • This technique enables the development of fault-severity indicators (Key Performance Indicators - KPIs) for real-time condition monitoring.
  • The findings support the implementation of this method as a predictive maintenance tool to enhance DCM reliability and prevent costly failures.