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

Oscar Duque-Perez, Carlos Del Pozo-Gallego, Daniel Morinigo-Sotelo and Wagner Fontes Godoy
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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

Energies, 2019, vol. 12, issue 17, 1-13

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.

Keywords: condition monitoring; bearings; machine learning; current spectra (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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