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Condition Monitoring of Bearing Faults Using the Stator Current and Shrinkage Methods

Author

Listed:
  • Oscar Duque-Perez

    (Department of Electrical Engineering, University of Valladolid, 47011 Valladolid, Spain)

  • Carlos Del Pozo-Gallego

    (Department of Electrical Engineering, University of Valladolid, 47011 Valladolid, Spain)

  • Daniel Morinigo-Sotelo

    (Department of Electrical Engineering, University of Valladolid, 47011 Valladolid, Spain)

  • Wagner Fontes Godoy

    (Department of Electrical Engineering, Universidade Tecnologica Federal do Parana, Cornelio Procopio 86300-000, Brazil)

Abstract

Condition monitoring of bearings is an open issue. The use of the stator current to monitor induction motors has been validated as a very advantageous and practical way to detect several types of faults. Nevertheless, for bearing faults, the use of vibrations or sound generally offers better results in the accuracy of the detection, although with some disadvantages related to the sensors used for monitoring. To improve the performance of bearing monitoring, it is proposed to take advantage of more information available in the current spectra, beyond the usually employed, incorporating the amplitude of a significant number of sidebands around the first eleven harmonics, growing exponentially the number of fault signatures. This is especially interesting for inverter-fed motors. But, in turn, this leads to the problem of overfitting when applying a classifier to perform the fault diagnosis. To overcome this problem, and still exploit all the useful information available in the spectra, it is proposed to use shrinkage methods, which have been lately proposed in machine learning to solve the overfitting issue when the problem has many more variables than examples to classify. A case study with a motor is shown to prove the validity of the proposal.

Suggested Citation

  • Oscar Duque-Perez & Carlos Del Pozo-Gallego & Daniel Morinigo-Sotelo & Wagner Fontes Godoy, 2019. "Condition Monitoring of Bearing Faults Using the Stator Current and Shrinkage Methods," Energies, MDPI, vol. 12(17), pages 1-13, September.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:17:p:3392-:d:263579
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    References listed on IDEAS

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    1. Robert Tibshirani, 2011. "Regression shrinkage and selection via the lasso: a retrospective," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 73(3), pages 273-282, June.
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    Cited by:

    1. Tomas A. Garcia-Calva & Daniel Morinigo-Sotelo & Oscar Duque-Perez & Arturo Garcia-Perez & Rene de J. Romero-Troncoso, 2020. "Time-Frequency Analysis Based on Minimum-Norm Spectral Estimation to Detect Induction Motor Faults," Energies, MDPI, vol. 13(16), pages 1-12, August.
    2. Waseem El Sayed & Mostafa Abd El Geliel & Ahmed Lotfy, 2020. "Fault Diagnosis of PMSG Stator Inter-Turn Fault Using Extended Kalman Filter and Unscented Kalman Filter," Energies, MDPI, vol. 13(11), pages 1-24, June.
    3. Eoghan T. Chelmiah & Violeta I. McLoone & Darren F. Kavanagh, 2023. "Low Complexity Non-Linear Spectral Features and Wear State Models for Remaining Useful Life Estimation of Bearings," Energies, MDPI, vol. 16(14), pages 1-20, July.

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