IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2024i5p1234-d1351294.html
   My bibliography  Save this article

A Diagnostic Curve for Online Fault Detection in AC Drives

Author

Listed:
  • Natalia Koteleva

    (Department of Automation of Technological Processes and Production, Empress Catherine II Saint Petersburg Mining University, 2, 21 Line of Vasilyevsky Island, 199106 St. Petersburg, Russia)

  • Nikolai Korolev

    (Educational Research Center for Digital Technologies, Empress Catherine II Saint Petersburg Mining University, 2, 21 Line of Vasilyevsky Island, 199106 St. Petersburg, Russia)

Abstract

The AC drive is an important component and the most common element of any manufacturing process. A particularly serious task is the proper assessment of the AC drive’s technical condition, as its failure can cause problems for entire units and complexes of industrial enterprises. At present, there are several approaches either to determine electric drives’ condition or to find certain defects. Frequently, these methods require the installation of additional equipment that exceeds the price of the electric drive by several times. In this work, a simple approach is proposed. It includes the use of a diagnostic curve to assess the condition. This diagnostic curve is produced from the measurement results of the current sensors on the drive. Based on the Park vector modification, this is a simple and affordable way to obtain real-time information. The obtained curve can be used for the following purposes: directly for condition assessment by visual monitoring, as a sign for diagnostic systems built on artificial intelligence methods, for dynamic tuning of the drive control system. The article gives the algorithm for obtaining the diagnostic curve, showing its efficiency for model and field experiments. In model experiments, the faults in the rotor and stator of the drive were simulated; in field experiments, the state was analyzed by changing the load on the motor.

Suggested Citation

  • Natalia Koteleva & Nikolai Korolev, 2024. "A Diagnostic Curve for Online Fault Detection in AC Drives," Energies, MDPI, vol. 17(5), pages 1-14, March.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:5:p:1234-:d:1351294
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/5/1234/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/17/5/1234/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Chen, Yu & Wang, Yuandi & Zhao, Changyi, 2024. "From riches to digitalization: The role of AMC in overcoming challenges of digital transformation in resource-rich regions," Technological Forecasting and Social Change, Elsevier, vol. 200(C).
    2. Ruslan Gizatullin & Mikhail Dvoynikov & Natalya Romanova & Victor Nikitin, 2023. "Drilling in Gas Hydrates: Managing Gas Appearance Risks," Energies, MDPI, vol. 16(5), pages 1-13, March.
    3. Yuriy Zhukovskiy & Aleksandra Buldysko & Ilia Revin, 2023. "Induction Motor Bearing Fault Diagnosis Based on Singular Value Decomposition of the Stator Current," Energies, MDPI, vol. 16(8), pages 1-23, April.
    4. Muhammed Ali Gultekin & Ali Bazzi, 2023. "Review of Fault Detection and Diagnosis Techniques for AC Motor Drives," Energies, MDPI, vol. 16(15), pages 1-22, July.
    Full references (including those not matched with items on IDEAS)

    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. Reza Bazghandi & Mohammad Hoseintabar Marzebali & Vahid Abolghasemi & Shahin Hedayati Kia, 2023. "A Novel Mode Un-Mixing Approach in Variational Mode Decomposition for Fault Detection in Wound Rotor Induction Machines," Energies, MDPI, vol. 16(14), pages 1-17, July.
    2. Pavel Tsiglianu & Natalia Romasheva & Artem Nenko, 2023. "Conceptual Management Framework for Oil and Gas Engineering Project Implementation," Resources, MDPI, vol. 12(6), pages 1-27, May.
    3. Przemyslaw Pietrzak & Piotr Pietrzak & Marcin Wolkiewicz, 2024. "Microcontroller-Based Embedded System for the Diagnosis of Stator Winding Faults and Unbalanced Supply Voltage of the Induction Motors," Energies, MDPI, vol. 17(2), pages 1-22, January.

    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:17:y:2024:i:5:p:1234-:d:1351294. 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.