Reliable Detection of Induction Motor Rotor Faults Under the Rotor Axial Air Duct Influence

2014 ◽  
Vol 50 (4) ◽  
pp. 2493-2502 ◽  
Author(s):  
Chanseung Yang ◽  
Tae-June Kang ◽  
Doosoo Hyun ◽  
Sang Bin Lee ◽  
Jose A. Antonino-Daviu ◽  
...  
Author(s):  
Chanseung Yang ◽  
Tae-June Kang ◽  
Doosoo Hyun ◽  
Sang Bin Lee ◽  
Jose Antonino-Daviu ◽  
...  

Author(s):  
K. Vinoth Kumar ◽  
Prawin Angel Michael

This chapter deals with the implementation of a PC-based monitoring and fault identification scheme for a three-phase induction motor using artificial neural networks (ANNs). To accomplish the task, a hardware system is designed and built to acquire three phase voltages and currents from a 3.3KW squirrel-cage, three-phase induction motor. A software program is written to read the voltages and currents, which are first used to train a feed-forward neural network structure. The trained network is placed in a Lab VIEW-based program formula node that monitors the voltages and currents online and displays the fault conditions and turns the motor. The complete system is successfully tested in real time by creating different faults on the motor.


2020 ◽  
Vol 24 (22) ◽  
pp. 16935-16946
Author(s):  
Rodrigo H. C. Palácios ◽  
Ivan N. da Silva ◽  
Wagner F. Godoy ◽  
José A. Fabri ◽  
Lucas B. de Souza

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