Rotor Fault Detection in Induction Motors Based on Time-Frequency Analysis Using the Bispectrum and the Autocovariance of Stray Flux Signals
by
Miguel E. Iglesias-Martínez 1,2
,
Jose Alfonso Antonino-Daviu 3,*,
Pedro Fernández de Córdoba 2 and
J. Alberto Conejero 2
Miguel E. Iglesias-Martínez 1,2,
Jose Alfonso Antonino-Daviu 3,*,
Pedro Fernández de Córdoba 2 and
J. Alberto Conejero 2
1
Departamento de Telecomunicaciones, Universidad de Pinar del Río, Pinar del Río, Martí #270, CP 20100, Cuba
2
Instituto Universitario de Matemática Pura y Aplicada, Universitat Politècnica de València (UPV), Camino de Vera s/n, 46022 Valencia, Spain
3
Instituto Tecnológico de la Energía, Universitat Politècnica de València (UPV), Camino de Vera s/n, 46022 Valencia, Spain
*
Author to whom correspondence should be addressed.
Energies 2019, 12(4), 597; https://doi.org/10.3390/en12040597
Received: 20 January 2019 / Revised: 10 February 2019 / Accepted: 11 February 2019 / Published: 14 February 2019
(This article belongs to the Special Issue Fault Diagnosis in Electric Motors)
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.
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Keywords:
indicator; fault diagnosis; induction motors; bispectrum; autocovariance
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MDPI and ACS Style
Iglesias-Martínez, M.E.; Antonino-Daviu, J.A.; Fernández de Córdoba, P.; Conejero, J.A. Rotor Fault Detection in Induction Motors Based on Time-Frequency Analysis Using the Bispectrum and the Autocovariance of Stray Flux Signals. Energies 2019, 12, 597.
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