2026-06-10T06:21:35Zhttps://uvadoc.uva.es/oai/request
oai:uvadoc.uva.es:10324/595462023-05-09T19:00:45Zcom_10324_966com_10324_952com_10324_894col_10324_967
UVaDOC author Bazán, Gustavo Henrique author Goedtel, Alessandro author Duque Pérez, Óscar author Moríñigo Sotelo, Daniel 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 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. eng Atribución 4.0 Internacional Ingeniería eléctrica Motores de inducción Multi-fault diagnosis in three-phase induction motors using data optimization and machine learning techniques info:eu-repo/semantics/article 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URL https://uvadoc.uva.es/bitstream/10324/59546/1/Multi-Fault-Diagnosis-in-Three-Phase.pdf File MD5 3d8bae1329d3255201a78985db903be2 1254645 application/pdf Multi-Fault-Diagnosis-in-Three-Phase.pdf