IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v13y2021i16p9197-d615512.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/13/16/9197/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/13/16/9197/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    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.
    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. Carlos Candelo-Zuluaga & Jordi-Roger Riba & Carlos López-Torres & Antoni Garcia, 2019. "Detection of Inter-Turn Faults in Multi-Phase Ferrite-PM Assisted Synchronous Reluctance Machines," Energies, MDPI, vol. 12(14), pages 1-15, July.
    2. Wojciech Pietrowski & Konrad Górny, 2020. "Analysis of Torque Ripples of an Induction Motor Taking into Account a Inter-Turn Short-Circuit in a Stator Winding," Energies, MDPI, vol. 13(14), pages 1-19, July.
    3. Carlos Candelo-Zuluaga & Jordi-Roger Riba & Dinesh V. Thangamuthu & Antoni Garcia, 2020. "Detection of Partial Demagnetization Faults in Five-Phase Permanent Magnet Assisted Synchronous Reluctance Machines," Energies, MDPI, vol. 13(13), pages 1-17, July.
    4. Lien-Kai Chang & Shun-Hong Wang & Mi-Ching Tsai, 2020. "Demagnetization Fault Diagnosis of a PMSM Using Auto-Encoder and K-Means Clustering," Energies, MDPI, vol. 13(17), pages 1-12, August.
    5. Mariusz Korkosz & Jan Prokop & Bartlomiej Pakla & Grzegorz Podskarbi & Piotr Bogusz, 2020. "Analysis of Open-Circuit Fault in Fault-Tolerant BLDC Motors with Different Winding Configurations," Energies, MDPI, vol. 13(20), pages 1-27, October.
    6. Zorig, Assam & Hedayati Kia, Shahin & Chouder, Aissa & Rabhi, Abdelhamid, 2022. "A comparative study for stator winding inter-turn short-circuit fault detection based on harmonic analysis of induction machine signatures," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 196(C), pages 273-288.
    7. Kang Wang & Ruituo Huai & Zhihao Yu & Xiaoyang Zhang & Fengjuan Li & Luwei Zhang, 2019. "Comparison Study of Induction Motor Models Considering Iron Loss for Electric Drives," Energies, MDPI, vol. 12(3), pages 1-13, February.
    8. 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.
    9. Luo Wang & Yonggang Li & Junqing Li, 2018. "Diagnosis of Inter-Turn Short Circuit of Synchronous Generator Rotor Winding Based on Volterra Kernel Identification," Energies, MDPI, vol. 11(10), pages 1-15, September.
    10. Syidy Ab Rasid & Konstantinos N. Gyftakis & Markus Mueller, 2023. "Comparative Investigation of Three Diagnostic Methods Applied to Direct-Drive Permanent Magnet Machines Suffering from Demagnetization," Energies, MDPI, vol. 16(6), pages 1-18, March.
    11. Wenqi Ge & Chenchen Zhang & Yi Xie & Ming Yu & Youhua Wang, 2021. "Analysis of the Electromechanical Characteristics of Power Transformer under Different Residual Fluxes," Energies, MDPI, vol. 14(24), pages 1-22, December.
    12. Piotr Mynarek & Janusz Kołodziej & Adrian Młot & Marcin Kowol & Marian Łukaniszyn, 2021. "Influence of a Winding Short-Circuit Fault on Demagnetization Risk and Local Magnetic Forces in V-Shaped Interior PMSM with Distributed and Concentrated Winding," Energies, MDPI, vol. 14(16), pages 1-16, August.
    13. Zia Ullah & Bilal Ahmad Lodhi & Jin Hur, 2020. "Detection and Identification of Demagnetization and Bearing Faults in PMSM Using Transfer Learning-Based VGG," Energies, MDPI, vol. 13(15), pages 1-17, July.
    14. Jordi Burriel-Valencia & Ruben Puche-Panadero & Javier Martinez-Roman & Angel Sapena-Baño & Martin Riera-Guasp & Manuel Pineda-Sánchez, 2019. "Multi-Band Frequency Window for Time-Frequency Fault Diagnosis of Induction Machines," Energies, MDPI, vol. 12(17), pages 1-18, August.
    15. Mateusz Dybkowski & Szymon Antoni Bednarz, 2019. "Modified Rotor Flux Estimators for Stator-Fault-Tolerant Vector Controlled Induction Motor Drives," Energies, MDPI, vol. 12(17), pages 1-21, August.
    16. Gopu Venugopal & Arun Kumar Udayakumar & Adhavan Balashanmugham & Mohamad Abou Houran & Faisal Alsaif & Rajvikram Madurai Elavarasan & Kannadasan Raju & Mohammed H. Alsharif, 2023. "Fault Identification and Classification of Asynchronous Motor Drive Using Optimization Approach with Improved Reliability," Energies, MDPI, vol. 16(6), pages 1-25, March.
    17. Maciej Skowron & Marcin Wolkiewicz & Teresa Orlowska-Kowalska & Czeslaw T. Kowalski, 2019. "Effectiveness of Selected Neural Network Structures Based on Axial Flux Analysis in Stator and Rotor Winding Incipient Fault Detection of Inverter-fed Induction Motors," Energies, MDPI, vol. 12(12), pages 1-20, June.
    18. Grzegorz Tarchała & Marcin Wolkiewicz, 2019. "Performance of the Stator Winding Fault Diagnosis in Sensorless Induction Motor Drive," Energies, MDPI, vol. 12(8), pages 1-20, April.
    19. Anastasios Dounis, 2019. "Special Issue “Intelligent Control in Energy Systems”," Energies, MDPI, vol. 12(15), pages 1-9, August.
    20. Jannis N. Kahlen & Michael Andres & Albert Moser, 2021. "Improving Machine-Learning Diagnostics with Model-Based Data Augmentation Showcased for a Transformer Fault," Energies, MDPI, vol. 14(20), pages 1-20, October.

    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:jsusta:v:13:y:2021:i:16:p:9197-:d:615512. 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.