RT info:eu-repo/semantics/article
T1 Estimation of bearing fault severity in line-connected and inverter-fed three-phase induction motors
A1 Fontes Godoy, Wagner
A1 Moríñigo Sotelo, Daniel
A1 Duque Pérez, Óscar
A1 Nunes da Silva, Ivan
A1 Goedtel, Alessandro
A1 Palácios, Rodrigo Henrique Cunha
K1 Ingeniería eléctrica
K1 Diagnosis
K1 Bearing faults
K1 Intelligent estimation
K1 Three-phase induction motor
K1 Diagnóstico
K1 Fallas en rodamientos
K1 Estimación inteligente
K1 3313 Tecnología E Ingeniería Mecánicas
K1 3306 Ingeniería y Tecnología Eléctricas
AB This paper addresses a comprehensive evaluation of a bearing fault evolution and its consequent prediction concerning the remaining useful life. The proper prediction of bearing faults in their early stage is a crucial factor for predictive maintenance and mainly for the production management schedule. The detection and estimation of the progressive evolution of a bearing fault are performed by monitoring the amplitude of the current signals at the time domain. Data gathered from line-fed and inverter-fed three-phase induction motors were used to validate the proposed approach. To assess classification accuracy and fault estimation, the models described in this paper are investigated by using Artificial Neural Networks models. The paper also provides process flowcharts and classification tables to present the prognostic models used to estimate the remaining useful life of a defective bearing. Experimental results confirmed the method robustness and provide an accurate diagnosis regardless of the bearing fault stage, motor speed, load level, and type of supply.
PB MDPI
YR 2020
FD 2020
LK https://uvadoc.uva.es/handle/10324/59046
UL https://uvadoc.uva.es/handle/10324/59046
LA eng
NO Energies 2020, vol. 13, n. 13, 3481
NO Producción Científica
DS UVaDOC
RD 12-sep-2024