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Open AccessArticle
Detection of Contamination and Failure in the Outer Race on Ceramic, Metallic, and Hybrid Bearings through AI Using Magnetic Flux and Current
by
Jonathan Cureño-Osornio
Jonathan Cureño-Osornio
Geovanni Díaz-Saldaña
Geovanni Díaz-Saldaña 
,
Roque A. Osornio-Rios
Roque A. Osornio-Rios
Larisa Dunai
Larisa Dunai
Lilia Sava
Lilia Sava
Jose A. Antonino-Daviu
Jose A. Antonino-Daviu
Israel Zamudio-Ramírez
Israel Zamudio-Ramírez
1
Engineering Faculty, San Juan del Río Campus, Universidad Autónoma de Querétaro (UAQ), Av. Río Moctezuma 249, San Juan del Río 76807, Mexico
2
Department Graphic Engineering, Universitat Politècnica de València (UPV), Camino de Vera s/n, 46022 Valencia, Spain
3
Faculty of Electronics and Telecommunications, Technical University of Moldova (UTM), MD-2004 Chisinau, Moldova
4
Instituto Tecnológico de la Energía, Universitat Politècnica de València (UPV), Camino de Vera s/n, 46022 Valencia, Spain
*
Author to whom correspondence should be addressed.
Machines 2024, 12(8), 505; https://doi.org/10.3390/machines12080505 (registering DOI)
Submission received: 24 June 2024
/
Revised: 23 July 2024
/
Accepted: 25 July 2024
/
Published: 27 July 2024
Abstract
Bearings are one of the most essential elements in an induction motor, and they are built with different materials and constructions according to their application. These components are usually one of the most failure-prone parts of an electric motor, so correct and accurate measurements, instrumentation, and processing methods are required to prevent and detect the presence of different failures. This work develops a methodology based on the fusion of current and magnetic stray flux signals, calculation of statistical and non-statistical indicators, genetic algorithms (GAs), linear discriminant analysis (LDA), and neural networks. The proposed approach achieves a diagnostic effectiveness of 99.8% for detecting various damages in the outer race at 50 Hz frequency and 96.6% at 60 Hz. It also demonstrates 99.8% effectiveness for detecting damages in the presence of contaminants in lubrication at 50 Hz and 97% at 60 Hz. These results apply across metallic, ceramic, and hybrid bearings.
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MDPI and ACS Style
Cureño-Osornio, J.; Díaz-Saldaña, G.; Osornio-Rios, R.A.; Dunai, L.; Sava, L.; Antonino-Daviu, J.A.; Zamudio-Ramírez, I.
Detection of Contamination and Failure in the Outer Race on Ceramic, Metallic, and Hybrid Bearings through AI Using Magnetic Flux and Current. Machines 2024, 12, 505.
https://doi.org/10.3390/machines12080505
AMA Style
Cureño-Osornio J, Díaz-Saldaña G, Osornio-Rios RA, Dunai L, Sava L, Antonino-Daviu JA, Zamudio-Ramírez I.
Detection of Contamination and Failure in the Outer Race on Ceramic, Metallic, and Hybrid Bearings through AI Using Magnetic Flux and Current. Machines. 2024; 12(8):505.
https://doi.org/10.3390/machines12080505
Chicago/Turabian Style
Cureño-Osornio, Jonathan, Geovanni Díaz-Saldaña, Roque A. Osornio-Rios, Larisa Dunai, Lilia Sava, Jose A. Antonino-Daviu, and Israel Zamudio-Ramírez.
2024. "Detection of Contamination and Failure in the Outer Race on Ceramic, Metallic, and Hybrid Bearings through AI Using Magnetic Flux and Current" Machines 12, no. 8: 505.
https://doi.org/10.3390/machines12080505
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