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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
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    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.
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    Citations

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    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. 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.
    4. 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.
    5. 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.
    6. 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.

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