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Rotor Fault Detection in Induction Motors Based on Time-Frequency Analysis Using the Bispectrum and the Autocovariance of Stray Flux Signals

Author

Listed:
  • Miguel E. Iglesias-Martínez

    (Departamento de Telecomunicaciones, Universidad de Pinar del Río, Pinar del Río, Martí #270, CP 20100, Cuba
    Instituto Universitario de Matemática Pura y Aplicada, Universitat Politècnica de València (UPV), Camino de Vera s/n, 46022 Valencia, Spain)

  • Jose Alfonso Antonino-Daviu

    (Instituto Tecnológico de la Energía, Universitat Politècnica de València (UPV), Camino de Vera s/n, 46022 Valencia, Spain)

  • Pedro Fernández de Córdoba

    (Instituto Universitario de Matemática Pura y Aplicada, Universitat Politècnica de València (UPV), Camino de Vera s/n, 46022 Valencia, Spain)

  • J. Alberto Conejero

    (Instituto Universitario de Matemática Pura y Aplicada, Universitat Politècnica de València (UPV), Camino de Vera s/n, 46022 Valencia, Spain)

Abstract

The aim of this work is to find out, through the analysis of the time and frequency domains, significant differences that lead us to obtain one or several variables that may result in an indicator that allows diagnosing the condition of the rotor in an induction motor from the processing of the stray flux signals. For this, the calculation of two indicators is proposed: the first is based on the frequency domain and it relies on the calculation of the sum of the mean value of the bispectrum of the flux signal. The use of high order spectral analysis is justified in that with the one-dimensional analysis resulting from the Fourier Transform, there may not always be solid differences at the spectral level that enable us to distinguish between healthy and faulty conditions. Also, based on the high-order spectral analysis, differences may arise that, with the classical analysis with the Fourier Transform, are not evident, since the high order spectra from the Bispectrum are immune to Gaussian noise, but not the results that can be obtained using the one-dimensional Fourier transform. On the other hand, a second indicator based on the temporal domain that is based on the calculation of the square value of the median of the autocovariance function of the signal is evaluated. The obtained results are satisfactory and let us conclude the affirmative hypothesis of using flux signals for determining the condition of the rotor of an induction motor.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:4:p:597-:d:205737
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    Citations

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    Cited by:

    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. Arkadiusz Duda & Piotr Drozdowski, 2020. "Induction Motor Fault Diagnosis Based on Zero-Sequence Current Analysis," Energies, MDPI, vol. 13(24), pages 1-25, December.

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