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A Technique for Frequency Converter-Fed Asynchronous Motor Vibration Monitoring and Fault Classification, Applying Continuous Wavelet Transform and Convolutional Neural Networks

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)

Abstract

In this article, a type of diagnostic tool for an asynchronous motor powered from a frequency converter is proposed. An all-purpose, effective, and simple method for asynchronous motor monitoring is used. This method includes a single vibration measuring device fixed on the motor’s housing to detect faults such as worn-out or broken bearings, shaft misalignment, defective motor support, lost phase to the stator, and short circuit in one of the phase windings in the stator. The gathered vibration data are then standardized and continuous wavelet transform (CWT) is applied for feature extraction. Using morl wavelets, the algorithm is applied to all the datasets in the research and resulting scalograms are then fed to a complex deep convolutional neural network (CNN). Training and testing are done using separate datasets. The resulting model could successfully classify all the defects at an excellent rate and even separate mechanical faults from electrical ones. The best performing model achieved 97.53% accuracy.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:14:p:3690-:d:386138
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    References listed on IDEAS

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    1. 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.
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    Cited by:

    1. Abderrahman El Idrissi & Aziz Derouich & Said Mahfoud & Najib El Ouanjli & Ahmed Chantoufi & Ameena Saad Al-Sumaiti & Mahmoud A. Mossa, 2022. "Bearing Fault Diagnosis for an Induction Motor Controlled by an Artificial Neural Network—Direct Torque Control Using the Hilbert Transform," Mathematics, MDPI, vol. 10(22), pages 1-32, 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.

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    1. 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.
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