INVESTIGADORES
BOSSIO Jose Maria
artículos
Título:
Self-organizing map approach for classification of mechanical and rotor faults on induction motors
Autor/es:
BOSSIO, JOSÉ M.; DE ANGELO CRISTIAN H.; BOSSIO GUILLERMO R.
Revista:
NEURAL COMPUTING AND APPLICATIONS
Editorial:
SPRINGER
Referencias:
Lugar: Berlin; Año: 2012 vol. 21 p. 1 - 11
ISSN:
0941-0643
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.