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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
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    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)

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