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oai:uvadoc.uva.es:10324/595462023-05-09T19:00:45Zcom_10324_966com_10324_952com_10324_894col_10324_967

Bazán, Gustavo Henrique ce451d40-9fd5-4823-b6a8-daf09cf527e3 600 Goedtel, Alessandro 96dc1bb0-67af-4c77-b0f7-2f527d99b775 600 Duque Pérez, Óscar c0b3733d20d8dc17 600 0000-0003-2994-2520 Moríñigo Sotelo, Daniel a63d3901c1ac65e4 600 0000-0001-6153-9438 2023-05-09T10:46:03Z 2023-05-09T10:46:03Z 2021 Electronics, 2021, vol. 10, n. 12, 1462 https://uvadoc.uva.es/handle/10324/59546 10.3390/electronics10121462 1462 12 Electronics 10 2079-9292 Producción Científica Induction motors are very robust, with low operating and maintenance costs, and are therefore widely used in industry. They are, however, not fault-free, with bearings and rotor bars accounting for about 50% of the total failures. This work presents a two-stage approach for three-phase induction motors diagnosis based on mutual information measures of the current signals, principal component analysis, and intelligent systems. In a first stage, the fault is identified, and, in a second stage, the severity of the defect is diagnosed. A case study is presented where different severities of bearing wear and bar breakage are analyzed. To test the robustness of the proposed method, voltage imbalances and load torque variations are considered. The results reveal the promising performance of the proposal with overall accuracies above 90% in all cases, and in many scenarios 100% of the cases are correctly classified. This work also evaluates different strategies for extracting the signals, showing the possibility of reducing the amount of information needed. Results show a satisfactory relation between efficiency and computational cost, with decreases in accuracy of less than 4% but reducing the amount of data by more than 90%, facilitating the efficient use of this method in embedded systems. Consejo Nacional de Desarrollo Científico y Tecnológico (processes No. 474290/2008-5, 473576/2011-2, 552269/2011-5, 201902/2015-0 and 405228/2016-3) application/pdf eng MDPI info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/4.0/ © 2021 The Authors Atribución 4.0 Internacional Ingeniería eléctrica Motores de inducción Multi-fault diagnosis Principal component analysis Pattern recognition Diagnóstico multifallo Análisis de componentes principales Reconocimiento de patrones 3306 Ingeniería y Tecnología Eléctricas Multi-fault diagnosis in three-phase induction motors using data optimization and machine learning techniques info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion https://www.mdpi.com/2079-9292/10/12/1462 SI THUMBNAIL Multi-Fault-Diagnosis-in-Three-Phase.pdf.jpg Multi-Fault-Diagnosis-in-Three-Phase.pdf.jpg IM Thumbnail image/jpeg 3554 https://uvadoc.uva.es/bitstream/10324/59546/4/Multi-Fault-Diagnosis-in-Three-Phase.pdf.jpg 833b039289ba9326037071edccec450c MD5 4 LICENSE license.txt license.txt text/plain 3929 https://uvadoc.uva.es/bitstream/10324/59546/3/license.txt 289d98c1665ee0d20312360f0e8643c8 MD5 3 CC-LICENSE license_rdf license_rdf application/rdf+xml; charset=utf-8 921 https://uvadoc.uva.es/bitstream/10324/59546/2/license_rdf 9eecc1514e7247cad41e4db756248760 MD5 2 ORIGINAL Multi-Fault-Diagnosis-in-Three-Phase.pdf Multi-Fault-Diagnosis-in-Three-Phase.pdf application/pdf 1254645 https://uvadoc.uva.es/bitstream/10324/59546/1/Multi-Fault-Diagnosis-in-Three-Phase.pdf 3d8bae1329d3255201a78985db903be2 MD5 1 10324/59546 oai:uvadoc.uva.es:10324/59546 2023-05-09 21:00:45.786 UVaDOC repositorio@uva.es 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