Artículo
Self-organizing map approach for classification of mechanical and rotor faults on induction motors
Fecha de publicación:
07/2013
Editorial:
Springer
Revista:
Neural Computing And Applications
ISSN:
0941-0643
Idioma:
Inglés
Tipo de recurso:
Artículo publicado
Clasificación temática:
Resumen
Two neural network-based schemes for fault diagnosis and identification on induction motors are presented in this paper. Fault identification is performed using self-organizing maps neural networks. The first scheme uses the information of the motor phase current for feeding the network, in order to perform the diagnosis of load unbalance and shaft misalignment faults. The network is trained using data generated through the simulation of a motor-load system model, which allows including the effects of load unbalance and shaft misalignment. The second scheme is based on the motor's active and reactive instantaneous powers, in order to detect and diagnose faults whose characteristic frequencies are very close each other, such as broken rotor bars and oscillating loads. This network is trained using data obtained through the experimental measurements. Additional experimental data are later applied to both networks in order to validate the proposal. It is demonstrated that the proposed strategies are able to correctly identify, both unbalanced and misaligned load, as well as broken bars and low-frequency oscillating loads, thus avoiding the need for an expert to perform the task.
Palabras clave:
FAULT DIAGNOSIS
,
INDUCTION MOTORS
,
NEURAL NETWORKS
,
SELF-ORGANIZING MAPS
Archivos asociados
Licencia
Identificadores
Colecciones
Articulos(CCT - CORDOBA)
Articulos de CTRO.CIENTIFICO TECNOL.CONICET - CORDOBA
Articulos de CTRO.CIENTIFICO TECNOL.CONICET - CORDOBA
Citación
Bossio, Guillermo Rubén; de Angelo, Cristian Hernan; Bossio, Guillermo Rubén; Self-organizing map approach for classification of mechanical and rotor faults on induction motors; Springer; Neural Computing And Applications; 23; 1; 7-2013; 41-51
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