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Low-Frequency Magnetic Fields in Diagnostics of Low-Speed Electrical and Mechanical Systems

Author

Listed:
  • Milan Oravec

    (Faculty of Mechanical Engineering, Technical University of Košice, Letná 9, 042 00 Košice, Slovakia)

  • Pavol Lipovský

    (Faculty of Aeronautics, Technical University of Košice, Rampová 7, 041 21 Košice, Slovakia
    EDIS vvd, Electronic Digital Systems Research and Development Cooperative, Rampová 7, 041 21 Košice, Slovakia)

  • Miroslav Šmelko

    (Faculty of Aeronautics, Technical University of Košice, Rampová 7, 041 21 Košice, Slovakia
    EDIS vvd, Electronic Digital Systems Research and Development Cooperative, Rampová 7, 041 21 Košice, Slovakia)

  • Pavel Adamčík

    (Technical Diagnostics, Ltd., Jilemnického 3, 080 01 Prešov, Slovakia)

  • Mirosław Witoś

    (Air Force Institute of Technology, Ul. Księcia Bolesława 6, 01-494 Warsaw, Poland)

  • Jerzy Kwaśniewski

    (Faculty of Mechanical Engineering and Robotics, The AGH University of Science and Technology, Al. Mickiewicza 30, 30-059 Krakow, Poland)

Abstract

The magnetic field created by technical devices is a source of information. This information could be used in contactless diagnostics and predictive maintenance or for resolving problems along with standard NDT (nondestructive testing) methods, especially if we consider large, slow-speed devices, such as electromotors, transmissions, or generators. Identification of causalities of device failure processes with near magnetic field is one of the suitable NDT methods improving sustainability of systems. The measurements presented in the article were performed with the VEMA 04 fluxgate vector magnetometer with the DC-250 Hz bandwidth and 2 nT sensitivity. Postprocessing of the results was performed in the means of standard methods of discrete Fourier Transform, spectrogram creation and Wavelet Transform. The article presents data gathered during the measurement of a pair of extraction fans with power of 140 kW each and maximum revolutions up to 740 rev/min controlled by frequency converters and a single semi-Kaplan water power plant with 400 kW peak power at 1005 rev/min maximum generator speed. The measurements were performed before and after repairs of one of the ventilators in the ventilation system at 60% and 100% of maximal output power. The rotating magnetic fields of the fan electromotor stator, fan rotor revolutions, rotor slip frequency and ball-bearing frequencies were identified in frequency spectrums in the distance of 700 mm from fan electromotor axis in both cases. During the measurements on the semi-Kaplan turbine, the changes in states of mechanical and electrical components of the machine were monitored in the magnetic fields with increase of the power in the range of 0–95%, before and after phasing to the electrical grid. Standard processing methods, Discrete Fourier Transform, spectrograms and Discrete Wavelet Transform were used. In the spectrograms of the measured magnetic fields, the 1st–4th harmonics of the turbine shaft, generator shaft and also their side frequencies were identified. Significant changes of magnetic fields in time were identified in the area of 60–95% power. With the help of the Wavelet, transform intervals were identified where it is desirable to operate the turbine. The analyses of magnetic fields measurements performed on the power plant were compared with vibro-diagnostic principles.

Suggested Citation

  • Milan Oravec & Pavol Lipovský & Miroslav Šmelko & Pavel Adamčík & Mirosław Witoś & Jerzy Kwaśniewski, 2021. "Low-Frequency Magnetic Fields in Diagnostics of Low-Speed Electrical and Mechanical Systems," Sustainability, MDPI, vol. 13(16), pages 1-23, August.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:16:p:9197-:d:615512
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    References listed on IDEAS

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    1. Luqman Maraaba & Zakariya Al-Hamouz & Mohammad Abido, 2018. "An Efficient Stator Inter-Turn Fault Diagnosis Tool for Induction Motors," Energies, MDPI, vol. 11(3), pages 1-18, March.
    2. Zia Ullah & Jin Hur, 2018. "A Comprehensive Review of Winding Short Circuit Fault and Irreversible Demagnetization Fault Detection in PM Type Machines," Energies, MDPI, vol. 11(12), pages 1-27, November.
    3. Xiaomu Duan & Tong Zhao & Jinxin Liu & Li Zhang & Liang Zou, 2018. "Analysis of Winding Vibration Characteristics of Power Transformers Based on the Finite-Element Method," Energies, MDPI, vol. 11(9), pages 1-19, September.
    4. Baoshan Huang & Guojin Feng & Xiaoli Tang & James Xi Gu & Guanghua Xu & Robert Cattley & Fengshou Gu & Andrew D. Ball, 2019. "A Performance Evaluation of Two Bispectrum Analysis Methods Applied to Electrical Current Signals for Monitoring Induction Motor-Driven Systems," Energies, MDPI, vol. 12(8), pages 1-23, April.
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