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Open AccessArticle

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

1
Department of Electrical Engineering, University of Valladolid, 47011 Valladolid, Spain
2
Department of Electrical Engineering, Universidade Tecnologica Federal do Parana, Cornelio Procopio 86300-000, Brazil
*
Author to whom correspondence should be addressed.
Energies 2019, 12(17), 3392; https://doi.org/10.3390/en12173392
Received: 25 July 2019 / Revised: 31 August 2019 / Accepted: 2 September 2019 / Published: 3 September 2019
(This article belongs to the Special Issue Fault Diagnosis in Electric Motors)
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. View Full-Text
Keywords: condition monitoring; bearings; machine learning; current spectra condition monitoring; bearings; machine learning; current spectra
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MDPI and ACS Style

Duque-Perez, O.; Del Pozo-Gallego, C.; Morinigo-Sotelo, D.; Fontes Godoy, W. Condition Monitoring of Bearing Faults Using the Stator Current and Shrinkage Methods. Energies 2019, 12, 3392.

AMA Style

Duque-Perez O, Del Pozo-Gallego C, Morinigo-Sotelo D, Fontes Godoy W. Condition Monitoring of Bearing Faults Using the Stator Current and Shrinkage Methods. Energies. 2019; 12(17):3392.

Chicago/Turabian Style

Duque-Perez, Oscar; Del Pozo-Gallego, Carlos; Morinigo-Sotelo, Daniel; Fontes Godoy, Wagner. 2019. "Condition Monitoring of Bearing Faults Using the Stator Current and Shrinkage Methods" Energies 12, no. 17: 3392.

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