IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v12y2019i8p1438-d222756.html
   My bibliography  Save this article

A Performance Evaluation of Two Bispectrum Analysis Methods Applied to Electrical Current Signals for Monitoring Induction Motor-Driven Systems

Author

Listed:
  • Baoshan Huang

    (School of Industrial Automation, Beijing Institute of Technology, Zhuhai 519088, China)

  • Guojin Feng

    (Department of Mechanical, Aerospace and Civil Engineering, Brunel University London, Uxbridge, Middlesex UB8 3PH, UK)

  • Xiaoli Tang

    (Centre for Efficiency and Performance Engineering, University of Huddersfield, Huddersfield HD1 3DH, UK)

  • James Xi Gu

    (School of Industrial Automation, Beijing Institute of Technology, Zhuhai 519088, China
    School of Engineering, University of Bolton, Bolton BL3 5AB, UK)

  • Guanghua Xu

    (School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an 710049, China)

  • Robert Cattley

    (Centre for Efficiency and Performance Engineering, University of Huddersfield, Huddersfield HD1 3DH, UK)

  • Fengshou Gu

    (Centre for Efficiency and Performance Engineering, University of Huddersfield, Huddersfield HD1 3DH, UK)

  • Andrew D. Ball

    (Centre for Efficiency and Performance Engineering, University of Huddersfield, Huddersfield HD1 3DH, UK)

Abstract

This paper investigates the performance of the conventional bispectrum (CB) method and its new variant, the modulation signal bispectrum (MSB) method, in analysing the electrical current signals of induction machines for the condition monitoring of rotor systems driven by electrical motors. Current signal models which include the phases of the various electrical and magnetic quantities are explained first to show the theoretical relationships of spectral sidebands and their associated phases due to rotor faults. It then discusses the inefficiency of CB and the proficiency of MSB in characterising the sidebands based on simulated signals. Finally, these two methods are applied to analyse current signals measured from different rotor faults, including broken rotor bar (BRB), downstream gearbox wear progressions and various compressor faults, and the diagnostic results show that the MSB outperforms the CB method significantly in that it provides more accurate and sparse diagnostics, thanks to its unique capability of nonlinear modulation detection and random noise suppression.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:8:p:1438-:d:222756
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/12/8/1438/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/12/8/1438/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Miguel E. Iglesias-Martínez & Jose Alfonso Antonino-Daviu & Pedro Fernández de Córdoba & J. Alberto Conejero, 2019. "Rotor Fault Detection in Induction Motors Based on Time-Frequency Analysis Using the Bispectrum and the Autocovariance of Stray Flux Signals," Energies, MDPI, vol. 12(4), pages 1-16, February.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. 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.
    3. Jianguo Wang & Minmin Xu & Chao Zhang & Baoshan Huang & Fengshou Gu, 2020. "Online Bearing Clearance Monitoring Based on an Accurate Vibration Analysis," Energies, MDPI, vol. 13(2), pages 1-17, January.
    4. 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.
    5. 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.
    6. 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.

    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. Lotfi Saidi & Mohamed Benbouzid & Demba Diallo & Yassine Amirat & Elhoussin Elbouchikhi & Tianzhen Wang, 2020. "Higher-Order Spectra Analysis-Based Diagnosis Method of Blades Biofouling in a PMSG Driven Tidal Stream Turbine," Energies, MDPI, vol. 13(11), pages 1-18, June.
    2. Arkadiusz Duda & Maciej Sułowicz, 2020. "A New Effective Method of Induction Machine Condition Assessment Based on Zero-Sequence Voltage (ZSV) Symptoms," Energies, MDPI, vol. 13(14), pages 1-26, July.
    3. Israel Zamudio-Ramirez & Roque Alfredo Osornio-Rios & Miguel Trejo-Hernandez & Rene de Jesus Romero-Troncoso & Jose Alfonso Antonino-Daviu, 2019. "Smart-Sensors to Estimate Insulation Health in Induction Motors via Analysis of Stray Flux," Energies, MDPI, vol. 12(9), pages 1-16, May.
    4. Miguel E. Iglesias-Martínez & Juan Carlos Castro-Palacio & Felix Scholkmann & Victor Milián-Sánchez & Pedro Fernández de Córdoba & Antonio Mocholí-Salcedo & Ferrán Mocholí Belenguer & Valeriy A. Kolom, 2020. "Correlations between Background Radiation Inside a Multilayer Interleaving Structure, Geomagnetic Activity, and Cosmic Radiation: A Fourth-Order Cumulant-Based Correlation Analysis," Mathematics, MDPI, vol. 8(3), pages 1-8, March.
    5. Arkadiusz Duda & Piotr Drozdowski, 2020. "Induction Motor Fault Diagnosis Based on Zero-Sequence Current Analysis," Energies, MDPI, vol. 13(24), pages 1-25, December.

    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:12:y:2019:i:8:p:1438-:d:222756. 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.