IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v12y2019i21p4212-d283768.html
   My bibliography  Save this article

Selected Rolling Bearing Fault Diagnostic Methods in Wheel Embedded Permanent Magnet Brushless Direct Current Motors

Author

Listed:
  • Marcin Skora

    (Department of Electrical Machines, Drives and Measurements, Wrocław University of Science and Technology, 50-370 Wroclaw, Poland)

  • Pawel Ewert

    (Department of Electrical Machines, Drives and Measurements, Wrocław University of Science and Technology, 50-370 Wroclaw, Poland)

  • Czeslaw T. Kowalski

    (Department of Electrical Machines, Drives and Measurements, Wrocław University of Science and Technology, 50-370 Wroclaw, Poland)

Abstract

In recent years, the number of outer rotor permanent magnet brushless direct current (PM BLDC) motor drives has been intensively growing. Due to the specifics of drive operation, bearing faults are especially common, which results in motor stoppage. In a number of these types of motor applications, the monitoring and diagnostics of bearing conditions is relatively rare. This article presents the results of research aimed at searching for simple and simultaneously effective methods for assessing the condition of bearings that can be built into the drive control system. In the experimental research, four vibration signal processing methods were analysed with regards to the identification accuracy of fault symptoms in the geometric elements of bearings (characteristic frequencies). The results are presented for three cases of bearing faults and compared with a new bearing, they were obtained based on a vibration signal analysis using the classical fast Fourier transform (FFT), Fourier transform of signal absolute values, Fourier transform of an envelope signal obtained using the Hilbert transform, and the Fourier transform of a signal filtered with the Teager–Kaiser energy operator (TKEO).

Suggested Citation

  • Marcin Skora & Pawel Ewert & Czeslaw T. Kowalski, 2019. "Selected Rolling Bearing Fault Diagnostic Methods in Wheel Embedded Permanent Magnet Brushless Direct Current Motors," Energies, MDPI, vol. 12(21), pages 1-19, November.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:21:p:4212-:d:283768
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/12/21/4212/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/12/21/4212/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Vito Mario Fico & Antonio Leopoldo Rodríguez Vázquez & María Ángeles Martín Prats & Franco Bernelli-Zazzera, 2019. "Failure Detection by Signal Similarity Measurement of Brushless DC Motors," Energies, MDPI, vol. 12(7), pages 1-23, April.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Hisahide Nakamura & Yukio Mizuno, 2022. "Diagnosis for Slight Bearing Fault in Induction Motor Based on Combination of Selective Features and Machine Learning," Energies, MDPI, vol. 15(2), pages 1-12, January.
    2. Wagner Fontes Godoy & Daniel Morinigo-Sotelo & Oscar Duque-Perez & Ivan Nunes da Silva & Alessandro Goedtel & Rodrigo Henrique Cunha Palácios, 2020. "Estimation of Bearing Fault Severity in Line-Connected and Inverter-Fed Three-Phase Induction Motors," Energies, MDPI, vol. 13(13), pages 1-17, July.
    3. Pawel Ewert & Teresa Orlowska-Kowalska & Kamila Jankowska, 2021. "Effectiveness Analysis of PMSM Motor Rolling Bearing Fault Detectors Based on Vibration Analysis and Shallow Neural Networks," Energies, MDPI, vol. 14(3), pages 1-24, January.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Vito Mario Fico & María Ángeles Martín Prats & Carmelina Ierardi, 2020. "High Technology Readiness Level Techniques for Brushless Direct Current Motors Failures Detection: A Systematic Review," Energies, MDPI, vol. 13(7), pages 1-24, April.
    2. Krzysztof Tomczyk & Marek Sieja & Grzegorz Nowakowski, 2021. "Application of Identification Reference Nets for the Preliminary Modeling on the Example of Electrical Machines," Energies, MDPI, vol. 14(11), pages 1-15, May.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:12:y:2019:i:21:p:4212-:d:283768. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.