RT info:eu-repo/semantics/article
T1 Multi-fault diagnosis in three-phase induction motors using data optimization and machine learning techniques
A1 Bazán, Gustavo Henrique
A1 Goedtel, Alessandro
A1 Duque Pérez, Óscar
A1 Moríñigo Sotelo, Daniel
K1 Ingeniería eléctrica
K1 Motores de inducción
K1 Multi-fault diagnosis
K1 Principal component analysis
K1 Pattern recognition
K1 Diagnóstico multifallo
K1 Análisis de componentes principales
K1 Reconocimiento de patrones
K1 3306 Ingeniería y Tecnología Eléctricas
AB 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.
PB MDPI
YR 2021
FD 2021
LK https://uvadoc.uva.es/handle/10324/59546
UL https://uvadoc.uva.es/handle/10324/59546
LA eng
NO Electronics, 2021, vol. 10, n. 12, 1462
NO Producción Científica
DS UVaDOC
RD 14-oct-2024