RT info:eu-repo/semantics/article T1 Condition monitoring of bearing faults using the stator current and shrinkage methods A1 Duque Pérez, Óscar A1 Pozo Gallego, Carlos del A1 Moríñigo Sotelo, Daniel A1 Fontes Godoy, Wagner K1 Condition monitoring K1 Monitoreo de condición K1 Machine learning K1 Aprendizaje automático AB 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. PB MDPI SN 1996-1073 YR 2019 FD 2019 LK https://uvadoc.uva.es/handle/10324/55762 UL https://uvadoc.uva.es/handle/10324/55762 LA eng NO Energies, 2019, vol. 12, n. 17, 3392 NO Producción Científica DS UVaDOC RD 24-nov-2024