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Application of Advanced Vibration Monitoring Systems and Long Short-Term Memory Networks for Brushless DC Motor Stator Fault Monitoring and Classification

Author

Listed:
  • Tomas Zimnickas

    (Department of Power Systems, Kaunas University of Technology, LT-51367 Kaunas, Lithuania)

  • Jonas Vanagas

    (Department of Power Systems, Kaunas University of Technology, LT-51367 Kaunas, Lithuania)

  • Karolis Dambrauskas

    (Department of Power Systems, Kaunas University of Technology, LT-51367 Kaunas, Lithuania)

  • Artūras Kalvaitis

    (Department of Power Systems, Kaunas University of Technology, LT-51367 Kaunas, Lithuania)

  • Mindaugas Ažubalis

    (Department of Power Systems, Kaunas University of Technology, LT-51367 Kaunas, Lithuania)

Abstract

In this research, electric motors faults and their identification is reviewed. Brushless direct-current (BLDC) motors stator fault identification using long short-term memory neural networks were analyzed. A proposed method of vibration data acquisition using cloud technologies with high accuracy, feature extraction using spectral entropy, and instantaneous frequency and standardization using mean and standard deviation was reviewed. Additionally, model training with raw and standardized data was compared. A total model accuracy of 97.10 percent was achieved. The proposed methods could successfully identify the motor stator status from normal, to loss of stator winding imminent and arcing, and lastly to open circuit in stator winding—motor needing to stop immediately—by using gathered data from real experiments, training the model and testing it theoretically.

Suggested Citation

  • Tomas Zimnickas & Jonas Vanagas & Karolis Dambrauskas & Artūras Kalvaitis & Mindaugas Ažubalis, 2020. "Application of Advanced Vibration Monitoring Systems and Long Short-Term Memory Networks for Brushless DC Motor Stator Fault Monitoring and Classification," Energies, MDPI, vol. 13(4), pages 1-18, February.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:4:p:820-:d:320365
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    Citations

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    Cited by:

    1. Karolina Kudelina & Bilal Asad & Toomas Vaimann & Anton Rassõlkin & Ants Kallaste & Huynh Van Khang, 2021. "Methods of Condition Monitoring and Fault Detection for Electrical Machines," Energies, MDPI, vol. 14(22), pages 1-20, November.
    2. Dimitrios A. Papathanasopoulos & Konstantinos N. Giannousakis & Evangelos S. Dermatas & Epaminondas D. Mitronikas, 2021. "Vibration Monitoring for Position Sensor Fault Diagnosis in Brushless DC Motor Drives," Energies, MDPI, vol. 14(8), pages 1-24, April.
    3. Tomas Zimnickas & Jonas Vanagas & Karolis Dambrauskas & Artūras Kalvaitis, 2020. "A Technique for Frequency Converter-Fed Asynchronous Motor Vibration Monitoring and Fault Classification, Applying Continuous Wavelet Transform and Convolutional Neural Networks," Energies, MDPI, vol. 13(14), pages 1-21, July.

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