RT info:eu-repo/semantics/article
T1 Mutual information and meta-heuristic classifiers applied to bearing fault diagnosis in three-phase induction motors
A1 Bazán, Gustavo Henrique
A1 Goedtel, Alessandro
A1 Castoldi, Marcelo Favoretto
A1 Godoy, Wagner Fontes
A1 Duque Pérez, Óscar
A1 Moríñigo Sotelo, Daniel
K1 Electric motors
K1 Pattern recognition
K1 Bearing failure diagnosis
K1 Artificial bee colony
K1 3306 Ingeniería y Tecnología Eléctricas
K1 3306.03 Motores Eléctricos
AB Three-phase induction motors are extensively used in industrial processes due to their robustness, adaptability to different operating conditions, and low operation and maintenance costs. Induction motor fault diagnosis has received special attention from industry since it can reduce process losses and ensure the reliable operation of industrial systems. Therefore, this paper presents a study on the use of meta-heuristic tools in the diagnosis of bearing failures in induction motors. The extraction of the fault characteristics is performed based on mutual information measurements between the stator current signals in the time domain. Then, the Artificial Bee Colony algorithm is used to select the relevant mutual information values and optimize the pattern classifier input data. To evaluate the classification accuracy under various levels of failure severity, the performance of two different pattern classifiers was compared: The C4.5 decision tree and the multi-layer artificial perceptron neural networks. The experimental results confirm the effectiveness of the proposed approach.
PB MDPI
SN 2076-3417
YR 2020
FD 2020
LK https://uvadoc.uva.es/handle/10324/58908
UL https://uvadoc.uva.es/handle/10324/58908
LA eng
NO Applied Sciences, 2021, Vol. 11, Nº. 1, 314
NO Producción Científica
DS UVaDOC
RD 01-jun-2024