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Fast Real-Time RDFT- and GDFT-Based Direct Fault Diagnosis of Induction Motor Drive

Author

Listed:
  • Piotr Kołodziejek

    (Department of Electrical and Control Engineering, Gdansk University of Technology, 80-398 Gdansk, Poland)

  • Daniel Wachowiak

    (Department of Electrical and Control Engineering, Gdansk University of Technology, 80-398 Gdansk, Poland)

Abstract

This paper presents the theoretical analysis and experimental verification of a direct fault harmonic identification approach in a converter-fed electric drive for automated diagnosis purposes. On the basis of the analytical model of the proposed real-time direct fault diagnosis, the fault-related harmonic component is calculated using recursive DFT (RDFT) and Goertzel DFT (GDFT), applied instead of the full spectrum calculations required in the most popular FFT algorithm. The simulation model of an inverter sensorlessly controlled induction motor drive is linked with the induction machine rotor fault model for testing the sensitivity of the GDFT- and RDFT-based fault diagnosis to state variable estimation errors. According to the presented simulation results, the accuracy of the direct identification of a fault-related harmonic is sensitive to the quality of fault harmonic frequency estimation. The sensitivity analysis with respect to RDFT and GDFT algorithms is included. Based on the experimental setup with a sensorlessly controlled induction motor drive with the investigated rotor fault, fault diagnosis algorithms were implemented in the microprocessor by integration with the control system in one microcontroller and experimentally verified. The RDFT and GDFT approach has shown accurate and fast direct automated fault identification at a significantly decreased number of arithmetical operations in the microcontroller, which is convenient for the frequency-domain fault diagnosis in electric drives and supports fault-tolerant control system implementation.

Suggested Citation

  • Piotr Kołodziejek & Daniel Wachowiak, 2022. "Fast Real-Time RDFT- and GDFT-Based Direct Fault Diagnosis of Induction Motor Drive," Energies, MDPI, vol. 15(3), pages 1-14, February.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:3:p:1244-:d:744645
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    References listed on IDEAS

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    1. Mitja Nemec & Vanja Ambrožič & Rastko Fišer & David Nedeljković & Klemen Drobnič, 2019. "Induction Motor Broken Rotor Bar Detection Based on Rotor Flux Angle Monitoring," Energies, MDPI, vol. 12(5), pages 1-17, February.
    2. Marek Adamowicz & Janusz Szewczyk, 2020. "SiC-Based Power Electronic Traction Transformer (PETT) for 3 kV DC Rail Traction," Energies, MDPI, vol. 13(21), pages 1-30, October.
    3. Daniel Wachowiak, 2021. "A Universal Gains Selection Method for Speed Observers of Induction Machine," Energies, MDPI, vol. 14(20), pages 1-19, October.
    4. Zuolu Wang & Jie Yang & Haiyang Li & Dong Zhen & Yuandong Xu & Fengshou Gu, 2019. "Fault Identification of Broken Rotor Bars in Induction Motors Using an Improved Cyclic Modulation Spectral Analysis," Energies, MDPI, vol. 12(17), pages 1-20, August.
    5. Miroslaw Wlas & Stanislaw Galla & Abdellah Kouzou & Piotr Kolodziejek, 2022. "Analysis of an Energy Management System of a Small Plant Connected to the Rural Power System," Energies, MDPI, vol. 15(3), pages 1-21, January.
    6. Bohdan Pakhaliuk & Viktor Shevchenko & Jan Mućko & Oleksandr Husev & Mykola Lukianov & Piotr Kołodziejek & Natalia Strzelecka & Ryszard Strzelecki, 2021. "Optimal Rotating Receiver Angles Estimation for Multicoil Dynamic Wireless Power Transfer," Energies, MDPI, vol. 14(19), pages 1-15, September.
    7. Daniel Wachowiak, 2020. "Genetic Algorithm Approach for Gains Selection of Induction Machine Extended Speed Observer," Energies, MDPI, vol. 13(18), pages 1-24, September.
    8. Yuri Merizalde & Luis Hernández-Callejo & Oscar Duque-Perez, 2017. "State of the Art and Trends in the Monitoring, Detection and Diagnosis of Failures in Electric Induction Motors," Energies, MDPI, vol. 10(7), pages 1-34, July.
    9. Maciej Skowron & Teresa Orlowska-Kowalska & Marcin Wolkiewicz & Czeslaw T. Kowalski, 2020. "Convolutional Neural Network-Based Stator Current Data-Driven Incipient Stator Fault Diagnosis of Inverter-Fed Induction Motor," Energies, MDPI, vol. 13(6), pages 1-21, March.
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    Cited by:

    1. Chenyun Wu & Rabia Sehab & Ahmad Akrad & Cristina Morel, 2022. "Fault Diagnosis Methods and Fault Tolerant Control Strategies for the Electric Vehicle Powertrains," Energies, MDPI, vol. 15(13), pages 1-7, July.

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