Preprint Article Version 1 This version is not peer-reviewed

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

Version 1 : Received: 26 July 2019 / Approved: 28 July 2019 / Online: 28 July 2019 (11:52:17 CEST)

How to cite: 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. Preprints 2019, 2019070309 (doi: 10.20944/preprints201907.0309.v1). 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. Preprints 2019, 2019070309 (doi: 10.20944/preprints201907.0309.v1).

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 much more variables than examples to classify. A case study with a motor is shown to prove the validity of the proposal

Subject Areas

condition monitoring; bearings; machine learning; current spectra

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